From 4d587de866404eb9c4e634f2fade94d5e7537f08 Mon Sep 17 00:00:00 2001 From: DeveshParagiri Date: Fri, 20 Feb 2026 00:59:28 -0500 Subject: [PATCH 01/20] remove name_config and correlation_rate from spec/build contracts --- extropy/cli/commands/scenario.py | 81 +++++-- extropy/cli/commands/spec.py | 34 +-- extropy/core/models/__init__.py | 20 +- extropy/core/models/population.py | 47 ---- extropy/population/__init__.py | 2 - extropy/population/spec_builder/__init__.py | 2 - extropy/population/spec_builder/binder.py | 8 - extropy/population/spec_builder/hydrator.py | 36 +-- .../spec_builder/hydrators/__init__.py | 3 - .../population/spec_builder/hydrators/name.py | 215 ------------------ extropy/population/spec_builder/schemas.py | 29 --- extropy/population/spec_builder/selector.py | 52 +++-- 12 files changed, 131 insertions(+), 398 deletions(-) delete mode 100644 extropy/population/spec_builder/hydrators/name.py diff --git a/extropy/cli/commands/scenario.py b/extropy/cli/commands/scenario.py index 0ae9d70..3d58513 100644 --- a/extropy/cli/commands/scenario.py +++ b/extropy/cli/commands/scenario.py @@ -22,7 +22,13 @@ parse_population_ref, ) from ..display import display_extend_attributes -from ..utils import format_elapsed, Output, ExitCode +from ..utils import ( + format_elapsed, + Output, + ExitCode, + get_next_invalid_artifact_path, + save_invalid_json_artifact, +) @app.command("scenario") @@ -299,6 +305,23 @@ def do_selection(): out.error(f"Attribute selection failed: {selection_error}") raise typer.Exit(1) + if not new_attributes: + invalid_path = save_invalid_json_artifact( + { + "stage": "scenario", + "error": "No scenario-specific extended attributes discovered", + "description": description, + "base_population": f"population.v{pop_version}", + }, + scenario_path, + extension=".json", + ) + out.error( + f"Scenario requires non-empty extended_attributes. Saved: {invalid_path}", + exit_code=ExitCode.SCENARIO_ERROR, + ) + raise typer.Exit(out.finish()) + if not agent_mode: console.print( f"[green]✓[/green] Found {len(new_attributes)} NEW attributes ({format_elapsed(selection_elapsed)})" @@ -349,25 +372,17 @@ def on_progress(step: str, status: str, count: int | None): current_step[1] = status household_config = None - name_config = None def do_hydration(): - nonlocal \ - hydrated, \ - sources, \ - warnings, \ - hydration_error, \ - household_config, \ - name_config + nonlocal hydrated, sources, warnings, hydration_error, household_config try: - hydrated, sources, warnings, household_config, name_config = ( - hydrate_attributes( - attributes=new_attributes, - description=f"{pop_spec.meta.description} + {description}", - geography=pop_spec.meta.geography, - context=pop_spec.attributes, - on_progress=on_progress, - ) + hydrated, sources, warnings, household_config = hydrate_attributes( + attributes=new_attributes, + description=f"{pop_spec.meta.description} + {description}", + geography=pop_spec.meta.geography, + context=pop_spec.attributes, + include_household=True, + on_progress=on_progress, ) except Exception as e: hydration_error = e @@ -454,15 +469,20 @@ def run_pipeline(): nonlocal result_spec, validation_result, pipeline_error try: timeline_mode = None if timeline == "auto" else timeline + resolved_timestep_unit = ( + timestep_unit or sufficiency_result.inferred_timestep_unit + ) # We need to create a merged spec first to pass to scenario creation # For now, create scenario without agents (they'll be sampled later) result_spec, validation_result = create_scenario_spec( description=description, population_spec=pop_spec, extended_attributes=bound_attrs, + household_config=household_config, + agent_focus_mode=sufficiency_result.inferred_agent_focus_mode, on_progress=on_scenario_progress, timeline_mode=timeline_mode, - timestep_unit_override=timestep_unit, + timestep_unit_override=resolved_timestep_unit, max_timesteps_override=max_timesteps, ) except Exception as e: @@ -482,6 +502,20 @@ def run_pipeline(): time.sleep(0.1) if pipeline_error: + invalid_path = save_invalid_json_artifact( + { + "stage": "scenario", + "error": str(pipeline_error), + "description": description, + "base_population": f"population.v{pop_version}", + }, + scenario_path, + extension=".json", + ) + if not agent_mode: + console.print( + f"[yellow]⚠[/yellow] Scenario invalid artifact saved: [bold]{invalid_path}[/bold]" + ) out.error(f"Scenario creation failed: {pipeline_error}") raise typer.Exit(1) @@ -491,9 +525,6 @@ def run_pipeline(): # Set metadata result_spec.meta.name = scenario_name result_spec.meta.base_population = f"population.v{pop_version}" - result_spec.extended_attributes = bound_attrs - result_spec.household_config = household_config - result_spec.agent_focus_mode = sufficiency_result.inferred_agent_focus_mode # Display Summary if not agent_mode: @@ -501,14 +532,14 @@ def run_pipeline(): # Validation if validation_result and validation_result.errors: - invalid_path = scenario_path.with_suffix(".invalid.yaml") + invalid_path = get_next_invalid_artifact_path(scenario_path) result_spec.to_yaml(invalid_path) if not agent_mode: console.print( f"[yellow]⚠[/yellow] Scenario saved to [bold]{invalid_path}[/bold] for review" ) - out.error("Scenario validation failed") - raise typer.Exit(1) + out.error("Scenario validation failed", exit_code=ExitCode.SCENARIO_ERROR) + raise typer.Exit(out.finish()) # Human Checkpoint #2 if not agent_mode and not yes: @@ -686,7 +717,7 @@ def _display_scenario_summary(spec) -> None: if spec.seed_exposure: console.print("[bold]Exposure Channels:[/bold]") for ch in spec.seed_exposure.channels: - console.print(f" - {ch.name} ({ch.reach})") + console.print(f" - {ch.name}") console.print() # Extended attributes diff --git a/extropy/cli/commands/spec.py b/extropy/cli/commands/spec.py index fc0903f..616cec6 100644 --- a/extropy/cli/commands/spec.py +++ b/extropy/cli/commands/spec.py @@ -30,7 +30,12 @@ display_spec_summary, display_validation_result, ) -from ..utils import format_elapsed, Output, ExitCode +from ..utils import ( + format_elapsed, + Output, + ExitCode, + get_next_invalid_artifact_path, +) @app.command("spec") @@ -272,12 +277,10 @@ def spec_command( raise typer.Exit(1) geography = sufficiency_result.geography - agent_focus = sufficiency_result.agent_focus geo_str = f", {geography}" if geography else "" - focus_str = f", focus: {agent_focus}" if agent_focus else "" if not agent_mode: - console.print(f"[green]✓[/green] Context sufficient{geo_str}{focus_str}") + console.print(f"[green]✓[/green] Context sufficient{geo_str}") # Step 1: Attribute Selection console.print() @@ -352,17 +355,18 @@ def on_progress(step: str, status: str, count: int | None): current_step[0] = step current_step[1] = status - name_config = None - def do_hydration(): - nonlocal hydrated, sources, warnings, hydration_error, name_config + nonlocal hydrated, sources, warnings, hydration_error try: - hydrated, sources, warnings, _household_config, name_config = ( - hydrate_attributes( - attributes, description, geography, on_progress=on_progress - ) + hydrated, sources, warnings, _household_config = hydrate_attributes( + attributes, + description, + geography, + include_household=False, + on_progress=on_progress, ) - # Note: household_config is ignored at spec level - it's set per-scenario + # Spec stage intentionally does not hydrate household config. + # Household policy is scenario-owned; names are Faker-first at sampler runtime. except Exception as e: hydration_error = e finally: @@ -424,8 +428,6 @@ def do_hydration(): attributes=bound_attrs, sampling_order=sampling_order, sources=sources, - agent_focus=agent_focus, - name_config=name_config, ) console.print("[green]✓[/green] Spec assembled") @@ -435,8 +437,7 @@ def do_hydration(): validation_result = validate_spec(population_spec) if not display_validation_result(validation_result): - # Save with .invalid.yaml suffix so work isn't lost - invalid_path = resolved_output.with_suffix(".invalid.yaml") + invalid_path = get_next_invalid_artifact_path(resolved_output) population_spec.to_yaml(invalid_path) console.print() console.print( @@ -473,7 +474,6 @@ def do_hydration(): "Spec saved", output=str(resolved_output), geography=geography, - agent_focus=agent_focus, attribute_count=len(population_spec.attributes), elapsed_seconds=elapsed, study_folder=str(study_ctx.root) if study_ctx else None, diff --git a/extropy/core/models/__init__.py b/extropy/core/models/__init__.py index 6e11603..18cb21f 100644 --- a/extropy/core/models/__init__.py +++ b/extropy/core/models/__init__.py @@ -40,9 +40,6 @@ Dependent, LifeStageThreshold, HouseholdConfig, - # Name models - NameEntry, - NameConfig, STANDARD_PERSONALITY_ATTRIBUTES, ) @@ -65,7 +62,6 @@ ExposureRule, SeedExposure, # Interaction - InteractionType, InteractionConfig, SpreadModifier, SpreadConfig, @@ -78,6 +74,12 @@ ScenarioSimConfig, # Identity IdentityDimension, + PartnerCorrelationPolicy, + MaritalRoles, + GeoRoles, + SchoolParentRole, + ReligionRoles, + SamplingSemanticRoles, # Scenario ScenarioMeta, ScenarioSpec, @@ -159,9 +161,6 @@ "Dependent", "LifeStageThreshold", "HouseholdConfig", - # Population - Name models - "NameEntry", - "NameConfig", "STANDARD_PERSONALITY_ATTRIBUTES", # Scenario - Event "EventType", @@ -173,7 +172,6 @@ "ExposureRule", "SeedExposure", # Scenario - Interaction - "InteractionType", "InteractionConfig", "SpreadModifier", "SpreadConfig", @@ -189,6 +187,12 @@ "ScenarioSpec", # Scenario - Identity "IdentityDimension", + "PartnerCorrelationPolicy", + "MaritalRoles", + "GeoRoles", + "SchoolParentRole", + "ReligionRoles", + "SamplingSemanticRoles", # Validation (shared) "Severity", "ValidationIssue", diff --git a/extropy/core/models/population.py b/extropy/core/models/population.py index 0483cdb..24db747 100644 --- a/extropy/core/models/population.py +++ b/extropy/core/models/population.py @@ -140,27 +140,6 @@ class HouseholdConfig(BaseModel): avg_household_size: float = 2.5 -# ============================================================================= -# Name Generation Models -# ============================================================================= - - -class NameEntry(BaseModel): - name: str - weight: float = 1.0 - - -class NameConfig(BaseModel): - """Name frequency tables for culturally-appropriate name generation. - - None on SpecMeta = use bundled US CSV data. - """ - - male_first_names: list[NameEntry] = Field(default_factory=list) - female_first_names: list[NameEntry] = Field(default_factory=list) - last_names: list[NameEntry] = Field(default_factory=list) - - # Standard personality attributes that spec builders should include. # `conformity` (float, 0-1, correlated with agreeableness) is consumed by # Phase C for threshold behavior in simulation. @@ -405,10 +384,6 @@ class AttributeSpec(BaseModel): default="individual", description="individual: varies per person; household: shared across household members; partner_correlated: correlated between partners using assortative mating", ) - correlation_rate: float | None = Field( - default=None, - description="For partner_correlated scope: probability (0-1) that partner has same value. None uses type-specific defaults (age uses gaussian, race uses per-group rates).", - ) semantic_type: Literal[ "age", "income", "education", "employment", "occupation", None ] = Field( @@ -457,10 +432,6 @@ class SpecMeta(BaseModel): description: str = Field(description="Original population description") geography: str | None = Field(default=None, description="Geographic scope") - agent_focus: str | None = Field( - default=None, - description="Who the study agents represent (natural language). Used for display/documentation.", - ) created_at: datetime = Field(default_factory=datetime.now) version: str = Field(default="1.0", description="Spec format version") persona_template: str | None = Field( @@ -471,7 +442,6 @@ class SpecMeta(BaseModel): default=None, description="Scenario description from extend command, used by scenario command", ) - name_config: NameConfig | None = Field(default=None) # ============================================================================= @@ -614,17 +584,12 @@ def merge(self, extension: "PopulationSpec") -> "PopulationSpec": # It will be regenerated by the CLI after merge to include all # attributes (base + extension). - # Prefer extension's name_config if present, else keep base's - merged_nc = extension.meta.name_config or self.meta.name_config - merged_meta = SpecMeta( description=f"{self.meta.description} + {extension.meta.description}", geography=self.meta.geography, - agent_focus=self.meta.agent_focus, created_at=datetime.now(), version=self.meta.version, persona_template=None, - name_config=merged_nc, ) return PopulationSpec( @@ -702,10 +667,6 @@ class DiscoveredAttribute(BaseModel): default="individual", description="individual: varies per person; household: shared across household members; partner_correlated: correlated between partners", ) - correlation_rate: float | None = Field( - default=None, - description="For partner_correlated scope: probability (0-1) that partner has same value", - ) semantic_type: Literal[ "age", "income", "education", "employment", "occupation", None ] = Field( @@ -762,10 +723,6 @@ class HydratedAttribute(BaseModel): default="individual", description="individual: varies per person; household: shared across household members; partner_correlated: correlated between partners", ) - correlation_rate: float | None = Field( - default=None, - description="For partner_correlated scope: probability (0-1) that partner has same value", - ) semantic_type: Literal[ "age", "income", "education", "employment", "occupation", None ] = Field( @@ -820,10 +777,6 @@ class SufficiencyResult(BaseModel): sufficient: bool geography: str | None = None - agent_focus: str | None = Field( - default=None, - description="Who this study is about, e.g. 'surgeons', 'high school students', 'retired couples', 'families'", - ) clarifications_needed: list[str] = Field(default_factory=list) questions: list[ClarificationQuestion] = Field( default_factory=list, diff --git a/extropy/population/__init__.py b/extropy/population/__init__.py index e6f2d42..7db4c3c 100644 --- a/extropy/population/__init__.py +++ b/extropy/population/__init__.py @@ -16,7 +16,6 @@ hydrate_conditional_base, hydrate_conditional_modifiers, hydrate_household_config, - hydrate_name_config, bind_constraints, build_spec, ) @@ -57,7 +56,6 @@ "hydrate_conditional_base", "hydrate_conditional_modifiers", "hydrate_household_config", - "hydrate_name_config", "bind_constraints", "build_spec", # Sampler diff --git a/extropy/population/spec_builder/__init__.py b/extropy/population/spec_builder/__init__.py index e0f7e69..d2eec30 100644 --- a/extropy/population/spec_builder/__init__.py +++ b/extropy/population/spec_builder/__init__.py @@ -33,7 +33,6 @@ hydrate_conditional_base, hydrate_conditional_modifiers, hydrate_household_config, - hydrate_name_config, ) from .binder import bind_constraints, build_spec from ..validator import ( @@ -56,7 +55,6 @@ "hydrate_conditional_base", "hydrate_conditional_modifiers", "hydrate_household_config", - "hydrate_name_config", "bind_constraints", "build_spec", # Validation types diff --git a/extropy/population/spec_builder/binder.py b/extropy/population/spec_builder/binder.py index 534092c..dcb0828 100644 --- a/extropy/population/spec_builder/binder.py +++ b/extropy/population/spec_builder/binder.py @@ -13,7 +13,6 @@ SpecMeta, GroundingSummary, SamplingConfig, - NameConfig, ) from ...utils import topological_sort, extract_names_from_expression @@ -139,7 +138,6 @@ def bind_constraints( category=attr.category, description=attr.description, scope=attr.scope, - correlation_rate=attr.correlation_rate, semantic_type=attr.semantic_type, identity_type=attr.identity_type, display_format=attr.display_format, @@ -194,8 +192,6 @@ def build_spec( attributes: list[AttributeSpec], sampling_order: list[str], sources: list[str], - agent_focus: str | None = None, - name_config: NameConfig | None = None, ) -> PopulationSpec: """ Assemble the final PopulationSpec from all components. @@ -206,8 +202,6 @@ def build_spec( attributes: List of AttributeSpec sampling_order: Order for sampling sources: List of source URLs from research - agent_focus: Who the study agents represent (natural language) - name_config: LLM-researched name frequency tables (None = use bundled CSVs) Returns: Complete PopulationSpec ready for YAML export @@ -215,9 +209,7 @@ def build_spec( meta = SpecMeta( description=description, geography=geography, - agent_focus=agent_focus, created_at=datetime.now(), - name_config=name_config, ) grounding = _compute_grounding_summary(attributes, sources) diff --git a/extropy/population/spec_builder/hydrator.py b/extropy/population/spec_builder/hydrator.py index 226207a..6898f71 100644 --- a/extropy/population/spec_builder/hydrator.py +++ b/extropy/population/spec_builder/hydrator.py @@ -22,7 +22,6 @@ DiscoveredAttribute, HydratedAttribute, HouseholdConfig, - NameConfig, ) from ...utils.callbacks import HydrationProgressCallback from .hydrators import ( @@ -31,7 +30,6 @@ hydrate_conditional_base, hydrate_conditional_modifiers, hydrate_household_config, - hydrate_name_config, ) logger = logging.getLogger(__name__) @@ -124,12 +122,11 @@ def hydrate_attributes( description: str, geography: str | None = None, context: list[AttributeSpec] | None = None, + include_household: bool = True, model: str | None = None, reasoning_effort: str = "low", on_progress: ProgressCallback | None = None, -) -> tuple[ - list[HydratedAttribute], list[str], list[str], HouseholdConfig, NameConfig | None -]: +) -> tuple[list[HydratedAttribute], list[str], list[str], HouseholdConfig]: """ Research distributions for discovered attributes using split hydration. @@ -148,15 +145,16 @@ def hydrate_attributes( description: Original population description geography: Geographic scope for research context: Existing attributes from base population (for extend mode) + include_household: Whether to hydrate household config (Step 2e) model: Model to use reasoning_effort: "low", "medium", or "high" on_progress: Optional callback for progress updates (step, status, count) Returns: - Tuple of (list of HydratedAttribute, list of source URLs, list of validation warnings, HouseholdConfig, NameConfig | None) + Tuple of (list of HydratedAttribute, list of source URLs, list of validation warnings, HouseholdConfig) """ if not attributes: - return [], [], [], HouseholdConfig(), None + return [], [], [], HouseholdConfig() all_sources = [] all_warnings = [] @@ -302,8 +300,8 @@ def on_retry(attempt: int, max_retries: int, error_summary: str): len(modifier_sources), ) - # Step 2e: Household config (conditional - skip for purely professional populations) - if _should_hydrate_household_config(population, attributes): + # Step 2e: Household config (optional; enabled by scenario stage) + if include_household and _should_hydrate_household_config(population, attributes): report("2e", "Researching household composition...") household_config, hh_sources = hydrate_household_config( population=population, @@ -314,28 +312,16 @@ def on_retry(attempt: int, max_retries: int, error_summary: str): ) all_sources.extend(hh_sources) report("2e", "Household config researched", len(hh_sources)) - else: + elif include_household: logger.info( "Skipping household config hydration - no household-scoped attributes " "or household keywords in population description" ) household_config = HouseholdConfig() report("2e", "Skipped (no household context)", 0) - - # Step 2f: Name config - report("2f", "Researching population-appropriate names...") - name_config, name_sources = hydrate_name_config( - population=population, - geography=geography, - model=model, - reasoning_effort=reasoning_effort, - on_retry=make_retry_callback("2f"), - ) - all_sources.extend(name_sources) - if name_config is not None: - report("2f", "Name config researched", len(name_sources)) else: - report("2f", "Using bundled CSV names", 0) + household_config = HouseholdConfig() + report("2e", "Skipped (disabled for this stage)", 0) # Combine all hydrated attributes all_hydrated = independent_attrs + derived_attrs + conditional_attrs @@ -347,4 +333,4 @@ def on_retry(attempt: int, max_retries: int, error_summary: str): # when validate_spec() is called on the final PopulationSpec report("strategy", "Strategy consistency check passed", None) - return all_hydrated, unique_sources, all_warnings, household_config, name_config + return all_hydrated, unique_sources, all_warnings, household_config diff --git a/extropy/population/spec_builder/hydrators/__init__.py b/extropy/population/spec_builder/hydrators/__init__.py index af41059..39dfd8d 100644 --- a/extropy/population/spec_builder/hydrators/__init__.py +++ b/extropy/population/spec_builder/hydrators/__init__.py @@ -5,14 +5,12 @@ - derived.py: Specify formulas for derived attributes (Step 2b) - conditional.py: Research base distributions and modifiers (Steps 2c + 2d) - household.py: Research household composition parameters (Step 2e) -- name.py: Research culturally-appropriate names (Step 2f) """ from .independent import hydrate_independent from .derived import hydrate_derived from .conditional import hydrate_conditional_base, hydrate_conditional_modifiers from .household import hydrate_household_config -from .name import hydrate_name_config __all__ = [ "hydrate_independent", @@ -20,5 +18,4 @@ "hydrate_conditional_base", "hydrate_conditional_modifiers", "hydrate_household_config", - "hydrate_name_config", ] diff --git a/extropy/population/spec_builder/hydrators/name.py b/extropy/population/spec_builder/hydrators/name.py deleted file mode 100644 index de5217b..0000000 --- a/extropy/population/spec_builder/hydrators/name.py +++ /dev/null @@ -1,215 +0,0 @@ -"""Step 2f: Name Config Hydration. - -Research culturally-appropriate name frequency tables for the target population. -US populations skip this step (bundled SSA/Census CSVs are higher quality). -""" - -import logging -import re - -from ....core.llm import agentic_research, RetryCallback -from ....core.models.population import NameConfig, NameEntry -from ..schemas import build_name_config_schema - -logger = logging.getLogger(__name__) - -# Heuristic patterns for detecting US populations -_US_PATTERNS = re.compile( - r"\b(" - r"united\s+states|(? bool: - """Heuristic: does this description clearly indicate a US population?""" - text = f"{population} {geography or ''}".lower() - if _US_PATTERNS.search(text): - return True - # Check for US state names - for state in _US_STATES: - if state in text: - return True - return False - - -def hydrate_name_config( - population: str, - geography: str | None = None, - model: str | None = None, - reasoning_effort: str = "low", - on_retry: RetryCallback | None = None, -) -> tuple[NameConfig | None, list[str]]: - """ - Research culturally-appropriate names for the target population (Step 2f). - - For US populations, returns (None, []) — bundled CSVs are higher quality. - - Args: - population: Population description - geography: Geographic scope - model: Model to use - reasoning_effort: "low", "medium", or "high" - on_retry: Callback for retry notifications - - Returns: - Tuple of (NameConfig or None, list of source URLs) - """ - if _is_us_population(population, geography): - logger.debug("US population detected — using bundled CSV names") - return None, [] - - geo_context = f" in {geography}" if geography else "" - - prompt = f"""Research culturally-appropriate personal names for {population}{geo_context}. - -Provide name frequency tables that reflect the real naming patterns of this population: - -1. **male_first_names**: 150+ male first names with frequency weights. - - Span generations (older traditional names AND modern/younger names). - - Include regional variation where relevant. - - Weights should reflect actual popularity (higher = more common). - -2. **female_first_names**: 150+ female first names with frequency weights. - - Same guidelines as male names. - -3. **last_names**: 150+ surnames/family names with frequency weights. - - Reflect the actual surname distribution of this population. - - Include common AND less-common names for long-tail diversity. - -4. **sources**: List of URLs or citations used. - -## Important Guidelines - -- Do NOT just list the top-10 stereotypical names. Include diverse, realistic names. -- Weights should create a realistic long-tail distribution (a few common names, many less-common ones). -- Include generational variation: names popular with older people vs younger people. -- For populations with multiple ethnic/linguistic groups, include names from all major groups proportionally. -- The total number of names in each list should be at least 150 for realistic diversity. - -Return a single JSON object with all fields.""" - - try: - data, sources = agentic_research( - prompt=prompt, - response_schema=build_name_config_schema(), - schema_name="name_config", - model=model, - reasoning_effort=reasoning_effort, - on_retry=on_retry, - ) - except Exception: - logger.warning( - "Name config hydration failed, falling back to CSV names", exc_info=True - ) - return None, [] - - try: - config = _parse_name_config(data) - response_sources = data.get("sources", []) - all_sources = list(set(sources + [s for s in response_sources if s])) - return config, all_sources - except Exception: - logger.warning( - "Failed to parse name config response, falling back to CSV names", - exc_info=True, - ) - return None, sources - - -def _parse_name_config(data: dict) -> NameConfig: - """Parse LLM response into a NameConfig with diversity guardrails.""" - male = _parse_name_entries(data.get("male_first_names", [])) - female = _parse_name_entries(data.get("female_first_names", [])) - last = _parse_name_entries(data.get("last_names", [])) - - # Diversity guardrail: warn if top-5 names exceed 40% of total weight - for label, entries in [ - ("male_first_names", male), - ("female_first_names", female), - ("last_names", last), - ]: - if len(entries) >= 5: - total_weight = sum(e.weight for e in entries) - if total_weight > 0: - top5_weight = sum( - e.weight - for e in sorted(entries, key=lambda x: x.weight, reverse=True)[:5] - ) - if top5_weight / total_weight > 0.40: - logger.warning( - f"Name config: top-5 {label} account for " - f"{top5_weight / total_weight:.0%} of total weight " - f"(>40%% threshold)" - ) - - return NameConfig( - male_first_names=male, - female_first_names=female, - last_names=last, - ) - - -def _parse_name_entries(raw: list) -> list[NameEntry]: - """Parse a list of {name, weight} dicts into NameEntry objects.""" - entries = [] - for item in raw: - if isinstance(item, dict) and "name" in item: - weight = float(item.get("weight", 1.0)) - if weight > 0: - entries.append(NameEntry(name=str(item["name"]), weight=weight)) - return entries diff --git a/extropy/population/spec_builder/schemas.py b/extropy/population/spec_builder/schemas.py index 37e8c84..6fc89a0 100644 --- a/extropy/population/spec_builder/schemas.py +++ b/extropy/population/spec_builder/schemas.py @@ -370,35 +370,6 @@ def build_household_config_schema() -> dict: } -def build_name_config_schema() -> dict: - """Build JSON schema for name config hydration.""" - name_entry = { - "type": "object", - "properties": { - "name": {"type": "string"}, - "weight": {"type": "number"}, - }, - "required": ["name", "weight"], - "additionalProperties": False, - } - return { - "type": "object", - "properties": { - "male_first_names": {"type": "array", "items": name_entry}, - "female_first_names": {"type": "array", "items": name_entry}, - "last_names": {"type": "array", "items": name_entry}, - "sources": {"type": "array", "items": {"type": "string"}}, - }, - "required": [ - "male_first_names", - "female_first_names", - "last_names", - "sources", - ], - "additionalProperties": False, - } - - def build_modifiers_schema() -> dict: """Build JSON schema for conditional modifiers hydration.""" return { diff --git a/extropy/population/spec_builder/selector.py b/extropy/population/spec_builder/selector.py index f4543cd..e52146c 100644 --- a/extropy/population/spec_builder/selector.py +++ b/extropy/population/spec_builder/selector.py @@ -7,6 +7,8 @@ - Personality: Behavioral/psychological traits when relevant """ +import re + from ...core.llm import reasoning_call from ...core.models import AttributeSpec, DiscoveredAttribute @@ -70,6 +72,15 @@ def _is_multi_country_geography(geography: str | None, description: str) -> bool return False +def _canonicalize_attribute_name(name: str) -> str: + """Normalize model-produced attribute names into stable snake_case.""" + normalized = name.strip().lower() + normalized = re.sub(r"[\s\-\/]+", "_", normalized) + normalized = re.sub(r"[^a-z0-9_]", "", normalized) + normalized = re.sub(r"_+", "_", normalized).strip("_") + return normalized + + # JSON schema for attribute selection response ATTRIBUTE_SELECTION_SCHEMA = { "type": "object", @@ -112,12 +123,6 @@ def _is_multi_country_geography(geography: str | None, description: str) -> bool "enum": ["individual", "household", "partner_correlated"], "description": "individual: varies per person; household: shared across household members; partner_correlated: correlated between partners (e.g., age, education, religion, politics)", }, - "correlation_rate": { - "type": "number", - "minimum": 0, - "maximum": 1, - "description": "For partner_correlated scope only: probability (0-1) that partner has same value. Use ~0.6-0.7 for education, ~0.7-0.8 for religion, ~0.5-0.6 for politics. Omit for age (uses gaussian offset) and race/ethnicity (uses per-group rates).", - }, "depends_on": { "type": "array", "items": {"type": "string"}, @@ -353,13 +358,6 @@ def select_attributes( - `partner_correlated`: Correlated between partners (assortative mating) - Examples: age, education_level, religious_affiliation, political views, race/ethnicity, country - Partners tend to have similar values but NOT identical - - Provide `correlation_rate` (0-1) for probability of same value: - - age: OMIT (uses gaussian offset automatically) - - race/ethnicity: OMIT (uses per-group rates automatically) - - education: ~0.6-0.7 - - religion: ~0.7-0.8 - - politics: ~0.5-0.6 - - country: ~0.95 (most people marry within their country) - `individual`: Varies per person, sampled independently - Examples: gender, occupation, personality traits, personal attitudes @@ -383,7 +381,6 @@ def select_attributes( - description: One clear sentence - strategy: independent, derived, or conditional - scope: individual, household, or partner_correlated - - correlation_rate: (only for partner_correlated scope, omit for age/race) - depends_on: List of attribute names (max 3, empty if independent)""" data = reasoning_call( @@ -395,9 +392,30 @@ def select_attributes( ) attributes = [] + seen_raw_by_canonical: dict[str, str] = {} for attr_data in data.get("attributes", []): + raw_name = str(attr_data.get("name", "")).strip() + canonical_name = _canonicalize_attribute_name(attr_data.get("name", "")) + if not canonical_name: + continue + if canonical_name in seen_raw_by_canonical: + prev_raw = seen_raw_by_canonical[canonical_name] + if prev_raw != raw_name: + raise ValueError( + "Ambiguous attribute-name normalization: " + f"'{prev_raw}' and '{raw_name}' both normalize to '{canonical_name}'" + ) + continue + strategy = attr_data.get("strategy", "independent") - depends_on = attr_data.get("depends_on", []) + depends_on = [ + dep + for dep in ( + _canonicalize_attribute_name(str(dep)) + for dep in attr_data.get("depends_on", []) + ) + if dep and dep != canonical_name + ] if strategy == "independent" and depends_on: strategy = "conditional" @@ -405,19 +423,19 @@ def select_attributes( strategy = "independent" attr = DiscoveredAttribute( - name=attr_data["name"], + name=canonical_name, type=attr_data["type"], category=attr_data["category"], description=attr_data["description"], strategy=strategy, scope=attr_data.get("scope", "individual"), - correlation_rate=attr_data.get("correlation_rate"), semantic_type=attr_data.get("semantic_type"), identity_type=attr_data.get("identity_type"), display_format=attr_data.get("display_format"), depends_on=depends_on, ) attributes.append(attr) + seen_raw_by_canonical[canonical_name] = raw_name # Add Big Five if recommended and not already present if data.get("include_big_five", False): From 41f9c5580652e3922f81f1ecbcb66d2e99602022 Mon Sep 17 00:00:00 2001 From: DeveshParagiri Date: Fri, 20 Feb 2026 00:59:45 -0500 Subject: [PATCH 02/20] switch runtime naming to faker-only path and remove legacy hooks --- extropy/population/names/generator.py | 495 ++++++++++++++++++++--- extropy/population/sampler/core.py | 495 ++++++++++++++++++++++- extropy/population/sampler/households.py | 144 +++++-- 3 files changed, 1034 insertions(+), 100 deletions(-) diff --git a/extropy/population/names/generator.py b/extropy/population/names/generator.py index b0833d2..743e5ac 100644 --- a/extropy/population/names/generator.py +++ b/extropy/population/names/generator.py @@ -1,28 +1,24 @@ -"""Demographically-plausible name generation for synthetic agents. +"""Deterministic Faker-first name generation for synthetic agents. -Uses bundled SSA first-name frequencies (by decade + gender) and Census -surname frequencies (by ethnicity) to produce names that are statistically -representative of US demographics. Seeded RNG ensures reproducibility. - -When a NameConfig is provided (from LLM research), culturally-appropriate -names are drawn from those tables instead. +Primary path uses Faker with locale routing from country/region and identity hints. +Bundled CSV registries remain as a safety fallback when Faker locale generation fails. """ from __future__ import annotations import csv import random +import re +from functools import lru_cache from pathlib import Path -from typing import TYPE_CHECKING - -if TYPE_CHECKING: - from ...core.models.population import NameConfig +from typing import Any _DATA_DIR = Path(__file__).parent / "data" # Lazy-loaded lookup tables _first_names: dict[tuple[str, str], list[tuple[str, float]]] | None = None _last_names: dict[str, list[tuple[str, float]]] | None = None +_faker_cache: dict[str, Any] = {} # Ethnicity aliases — maps common attribute values to CSV ethnicity keys _ETHNICITY_MAP: dict[str, str] = { @@ -47,6 +43,188 @@ "aapi": "asian", } +_ETHNICITY_LOCALE_MAP: dict[str, list[str]] = { + "hispanic": ["es_MX", "es_ES", "pt_BR"], + "asian": ["en_IN", "zh_CN", "ja_JP", "ko_KR", "vi_VN"], + "black": ["en_US", "en_GB", "fr_FR"], + "white": ["en_US", "en_GB", "de_DE", "fr_FR", "it_IT"], +} + +_US_STATE_ABBREVIATIONS = { + "AL", + "AK", + "AZ", + "AR", + "CA", + "CO", + "CT", + "DE", + "FL", + "GA", + "HI", + "ID", + "IL", + "IN", + "IA", + "KS", + "KY", + "LA", + "ME", + "MD", + "MA", + "MI", + "MN", + "MS", + "MO", + "MT", + "NE", + "NV", + "NH", + "NJ", + "NM", + "NY", + "NC", + "ND", + "OH", + "OK", + "OR", + "PA", + "RI", + "SC", + "SD", + "TN", + "TX", + "UT", + "VT", + "VA", + "WA", + "WV", + "WI", + "WY", + "DC", +} + +# Region aliases are kept data-driven here so free-text variants +# ("SE Asia", "South East Asia", etc.) resolve consistently. +_REGION_CODE_ALIASES: dict[str, tuple[list[str], list[str]]] = { + "north_america": ( + ["north america", "n america", "na", "amer"], + ["US", "CA", "MX"], + ), + "latin_america": ( + ["latin america", "latam", "south america", "central america", "caribbean"], + [ + "MX", + "BR", + "AR", + "CO", + "CL", + "PE", + "VE", + "UY", + "PY", + "BO", + "EC", + "GT", + "HN", + "SV", + "NI", + "CR", + "PA", + "DO", + "CU", + "PR", + ], + ), + "europe": ( + [ + "europe", + "eu", + "emea", + "western europe", + "eastern europe", + "nordic", + "scandinavian", + "balkan", + "mediterranean", + ], + [ + "GB", + "IE", + "FR", + "DE", + "IT", + "ES", + "PT", + "NL", + "BE", + "CH", + "AT", + "DK", + "SE", + "NO", + "FI", + "PL", + "CZ", + "HU", + "RO", + "GR", + "RU", + "UA", + "TR", + ], + ), + "east_asia": ( + ["east asia"], + ["CN", "JP", "KR", "TW", "HK", "MO"], + ), + "south_asia": ( + ["south asia"], + ["IN", "PK", "BD", "LK", "NP", "BT", "MV"], + ), + "southeast_asia": ( + ["southeast asia", "south east asia", "se asia", "sea"], + ["ID", "MY", "SG", "TH", "VN", "PH", "MM", "KH", "LA", "BN", "TL"], + ), + "central_asia": ( + ["central asia"], + ["KZ", "UZ", "KG", "TJ", "TM"], + ), + "middle_east": ( + ["middle east", "mena", "gulf"], + [ + "AE", + "SA", + "QA", + "KW", + "BH", + "OM", + "JO", + "LB", + "IL", + "PS", + "IQ", + "IR", + "TR", + "SY", + "YE", + "EG", + ], + ), + "africa": ( + ["africa", "sub saharan africa", "sub-saharan africa", "north africa"], + ["NG", "ZA", "KE", "GH", "ET", "TZ", "UG", "SN", "CM", "DZ", "MA", "TN", "EG"], + ), + "oceania": ( + ["oceania", "pacific"], + ["AU", "NZ", "PG", "FJ"], + ), + "asia": ( + ["asia", "apac"], + ["IN", "CN", "JP", "KR", "ID", "TH", "VN", "MY", "PH", "SG", "PK", "BD"], + ), +} + # Decade snapping thresholds _VALID_DECADES = [1940, 1950, 1960, 1970, 1980, 1990, 2000, 2010] @@ -124,6 +302,244 @@ def age_to_birth_decade(age: int | float, reference_year: int = 2025) -> int: return (birth_year // 10) * 10 +def _normalize_geo_text(text: str | None) -> str: + if not text: + return "" + token = text.strip().lower() + token = token.replace("&", " and ") + token = re.sub(r"[\s\-_\/\.]+", " ", token) + token = re.sub(r"[^a-z0-9 ]", "", token) + token = re.sub(r"\s+", " ", token).strip() + return token + + +@lru_cache(maxsize=1) +def _country_lookup() -> dict[str, str]: + """Build normalized country token -> ISO alpha2 map.""" + lookup: dict[str, str] = {} + try: + from iso3166 import countries # Lightweight ISO registry. + except Exception: + return lookup + + for country in countries: + alpha2 = country.alpha2.upper() + candidates = { + country.alpha2, + country.alpha3, + country.name, + getattr(country, "apolitical_name", None), + getattr(country, "official_name", None), + getattr(country, "common_name", None), + } + for candidate in candidates: + if not candidate: + continue + norm = _normalize_geo_text(str(candidate)) + if norm: + lookup[norm] = alpha2 + + # Common shorthands that are noisy in free-text geography. + lookup.update( + { + "usa": "US", + "u s": "US", + "u s a": "US", + "united states": "US", + "uk": "GB", + "u k": "GB", + "uae": "AE", + "south korea": "KR", + "north korea": "KP", + "russia": "RU", + "ivory coast": "CI", + } + ) + return lookup + + +@lru_cache(maxsize=1) +def _available_locales() -> tuple[set[str], dict[str, list[str]], list[str]]: + """Return supported Faker locales and reverse country->locale index.""" + try: + from faker.config import AVAILABLE_LOCALES + except Exception: + return set(), {}, [] + + available = set(AVAILABLE_LOCALES) + by_country: dict[str, list[str]] = {} + language_only: list[str] = [] + for locale in AVAILABLE_LOCALES: + if "_" in locale: + suffix = locale.rsplit("_", 1)[1].upper() + if len(suffix) == 2 and suffix.isalpha(): + by_country.setdefault(suffix, []).append(locale) + continue + language_only.append(locale) + return available, by_country, language_only + + +def _try_country_code_from_hint(geo_hint: str | None) -> str | None: + if not geo_hint: + return None + lookup = _country_lookup() + normalized = _normalize_geo_text(geo_hint) + if not normalized: + return None + + # Exact country token/code match (handles alpha2/alpha3 like IN/IND). + if normalized in lookup: + return lookup[normalized] + + parts = [p.strip() for p in re.split(r"[,;|]", geo_hint) if p.strip()] + for part in reversed(parts): + norm = _normalize_geo_text(part) + if norm in lookup: + return lookup[norm] + + tokens = normalized.split() + for span in range(min(4, len(tokens)), 0, -1): + for i in range(len(tokens) - span + 1): + phrase = " ".join(tokens[i : i + span]) + # Avoid ambiguous short tokens inside region strings + # (e.g. "SE Asia" should not resolve to country code SE). + if len(phrase) <= 2: + continue + if phrase in lookup: + return lookup[phrase] + + # US subnational fallback (e.g., "Austin, TX") + if re.search(r",\s*([A-Za-z]{2})\b", geo_hint): + abbr = re.search(r",\s*([A-Za-z]{2})\b", geo_hint) + if abbr and abbr.group(1).upper() in _US_STATE_ABBREVIATIONS: + return "US" + + return None + + +def _region_country_codes(geo_hint: str | None) -> list[str]: + normalized = _normalize_geo_text(geo_hint) + if not normalized: + return [] + best_codes: list[str] = [] + best_len = -1 + for aliases, codes in _REGION_CODE_ALIASES.values(): + for alias in aliases: + alias_norm = _normalize_geo_text(alias) + if not alias_norm: + continue + if re.search( + rf"(? best_len: + best_len = len(alias_norm) + best_codes = codes + if best_codes: + return best_codes + return [] + + +def _build_locale_candidates(country: str | None, ethnicity_key: str) -> list[str]: + available, by_country, _language_only = _available_locales() + locales: list[str] = [] + + raw_hint = (country or "").strip() + norm_hint = _normalize_geo_text(raw_hint) + + # If caller already passes a locale token, honor directly. + if raw_hint and re.fullmatch(r"[a-z]{2}[_-][A-Z]{2}", raw_hint): + direct = raw_hint.replace("-", "_") + if direct in available: + locales.append(direct) + + # Country-first resolution via ISO lookup. + country_code = _try_country_code_from_hint(raw_hint) + if country_code: + locales.extend(by_country.get(country_code, [])) + else: + # Region-scope fallback (SE Asia, LATAM, EMEA, etc.) + for code in _region_country_codes(norm_hint): + locales.extend(by_country.get(code, [])) + + # Ethnicity hint next. + for locale in _ETHNICITY_LOCALE_MAP.get(ethnicity_key, []): + if locale in available: + locales.append(locale) + + # Global fallback locale always last. + locales.append("en_US") + + seen: set[str] = set() + deduped: list[str] = [] + for locale in locales: + if locale not in seen: + seen.add(locale) + deduped.append(locale) + return deduped + + +def _get_faker(locale: str) -> Any | None: + """Get or create a Faker instance for locale; return None if unavailable.""" + if locale in _faker_cache: + return _faker_cache[locale] + + try: + from faker import Faker # Imported lazily to keep module import cheap. + except Exception: + return None + + try: + fake = Faker(locale) + except Exception: + return None + + _faker_cache[locale] = fake + return fake + + +def _faker_generate_name( + norm_gender: str, + ethnicity_key: str, + country: str | None, + birth_decade: int, + seed: int, +) -> tuple[str, str] | None: + """Generate name via Faker locale routing. Returns None if all locales fail.""" + mixed_seed = (seed * 1315423911 + birth_decade * 2654435761) & 0x7FFFFFFF + rng = random.Random(mixed_seed) + locales = _build_locale_candidates(country, ethnicity_key) + rng.shuffle(locales) + + for locale in locales: + fake = _get_faker(locale) + if fake is None: + continue + + # Ensure deterministic draws per call. + fake.seed_instance(mixed_seed) + + try: + if norm_gender == "male": + first_name = fake.first_name_male() + else: + first_name = fake.first_name_female() + except Exception: + try: + first_name = fake.first_name() + except Exception: + continue + + try: + last_name = fake.last_name() + except Exception: + continue + + if first_name and last_name: + return str(first_name), str(last_name) + + return None + + def generate_name( gender: str | None = None, ethnicity: str | None = None, @@ -132,7 +548,6 @@ def generate_name( age: int | float | None = None, country: str = "US", seed: int | None = None, - name_config: NameConfig | None = None, ) -> tuple[str, str]: """Generate a demographically-plausible (first_name, last_name) pair. @@ -141,24 +556,14 @@ def generate_name( ethnicity: Agent's ethnicity/race attribute birth_decade: Decade of birth (e.g. 1980). Derived from age if not given. age: Agent's age (used to derive birth_decade if not provided) - country: Country code — only "US" supported for now + country: Country/region hint used for Faker locale routing seed: RNG seed for reproducibility - name_config: LLM-researched name tables (None = use bundled CSVs) Returns: Tuple of (first_name, last_name) """ rng = random.Random(seed) - # Use NameConfig if available and populated - if name_config is not None: - result = _generate_from_config(name_config, gender, rng) - if result is not None: - return result - - # Fallback: bundled CSV path - first_names, last_names = _ensure_loaded() - # Resolve birth decade if birth_decade is None and age is not None: birth_decade = age_to_birth_decade(age) @@ -167,20 +572,34 @@ def generate_name( norm_gender = _normalize_gender(gender) norm_ethnicity = _normalize_ethnicity(ethnicity) + + # Primary path: Faker (locale-routed by country/region + ethnicity hints) + faker_result = _faker_generate_name( + norm_gender=norm_gender, + ethnicity_key=norm_ethnicity, + country=country, + birth_decade=birth_decade, + seed=rng.randint(0, 2**31 - 1), + ) + if faker_result is not None: + return faker_result + + # Safety fallback: bundled CSVs + first_names, last_names = _ensure_loaded() decade_str = _snap_decade(birth_decade) # Pick first name first_key = (norm_gender, decade_str) first_options = first_names.get(first_key) if not first_options: - # Fallback: try any decade for this gender + # Fallback tier 2: any decade for this demographic bucket for d in reversed(_VALID_DECADES): fallback_key = (norm_gender, str(d)) if fallback_key in first_names: first_options = first_names[fallback_key] break if not first_options: - # Ultimate fallback + # Fallback tier 3: global default first_options = [("Alex", 1.0)] first_name = _weighted_choice(first_options, rng) @@ -188,31 +607,9 @@ def generate_name( # Pick last name last_options = last_names.get(norm_ethnicity) if not last_options: - # Fallback to white surnames - last_options = last_names.get("white", [("Smith", 1.0)]) + pooled = [item for rows in last_names.values() for item in rows] + last_options = pooled or [("Smith", 1.0)] last_name = _weighted_choice(last_options, rng) return first_name, last_name - - -def _generate_from_config( - config: NameConfig, gender: str | None, rng: random.Random -) -> tuple[str, str] | None: - """Try generating a name from NameConfig tables. Returns None if empty.""" - norm_gender = _normalize_gender(gender) - - if norm_gender == "male": - first_entries = config.male_first_names - else: - first_entries = config.female_first_names - - if not first_entries or not config.last_names: - return None - - first_options = [(e.name, e.weight) for e in first_entries] - last_options = [(e.name, e.weight) for e in config.last_names] - - first_name = _weighted_choice(first_options, rng) - last_name = _weighted_choice(last_options, rng) - return first_name, last_name diff --git a/extropy/population/sampler/core.py b/extropy/population/sampler/core.py index 4e76d69..f16573d 100644 --- a/extropy/population/sampler/core.py +++ b/extropy/population/sampler/core.py @@ -14,6 +14,7 @@ import sqlite3 from datetime import datetime from pathlib import Path +import re from typing import Any, Literal from ...core.models import ( @@ -22,7 +23,8 @@ SamplingStats, SamplingResult, HouseholdConfig, - NameConfig, + SamplingSemanticRoles, + MaritalRoles, ) from ...utils.callbacks import ItemProgressCallback from .distributions import sample_distribution, coerce_to_type @@ -176,6 +178,120 @@ def _has_household_attributes(spec: PopulationSpec) -> bool: return any(attr.scope == "household" for attr in spec.attributes) +_GENERIC_CITIZENSHIP_VALUES = { + "citizen", + "citizenship", + "noncitizen", + "non_citizen", + "non citizen", + "resident", + "permanent_resident", + "permanent resident", + "immigrant", + "non_immigrant", + "non immigrant", + "unknown", + "na", + "n_a", + "n/a", +} + + +def _extract_country_from_citizenship(value: str) -> str | None: + """Extract country-like token from citizenship-style values. + + Examples: + - "US citizen" -> "US" + - "citizen of India" -> "India" + - "non_citizen" -> None + """ + text = value.strip() + if not text: + return None + + normalized = _normalize_attr_token(text) + if normalized in _GENERIC_CITIZENSHIP_VALUES: + return None + + cleaned = text + for pattern in ( + r"\bcitizenship\b", + r"\bcitizen\b", + r"\bnationality\b", + r"\bnational\b", + r"\bpermanent resident\b", + r"\bresident\b", + r"\bpassport\b", + r"\bholder\b", + r"\bof\b", + ): + cleaned = re.sub(pattern, " ", cleaned, flags=re.IGNORECASE) + cleaned = re.sub(r"\s+", " ", cleaned).strip(" ,;/") + if not cleaned: + return None + + cleaned_norm = _normalize_attr_token(cleaned) + if cleaned_norm in _GENERIC_CITIZENSHIP_VALUES: + return None + + return cleaned + + +def _resolve_country_hint( + agent: dict[str, Any], + default_geography: str | None = None, + semantic_roles: SamplingSemanticRoles | None = None, +) -> str: + """Resolve geography hint for name generation. + + Precedence: + 1. Explicit country-like agent fields + 2. Spec-level geography (pipeline scope anchor) + 3. Broader region/location fields + 4. US fallback + """ + geo_roles = semantic_roles.geo_roles if semantic_roles else None + + prioritized_keys: list[str] = [] + if geo_roles and geo_roles.country_attr: + prioritized_keys.append(geo_roles.country_attr) + prioritized_keys.extend(("country", "country_code", "nationality")) + + for key in prioritized_keys: + value = agent.get(key) + if value is None: + continue + text = str(value).strip() + if text: + return text + + citizenship_value = agent.get("citizenship") + if citizenship_value is not None: + parsed_country = _extract_country_from_citizenship(str(citizenship_value)) + if parsed_country: + return parsed_country + + if default_geography: + text = str(default_geography).strip() + if text: + return text + + region_keys: list[str] = [] + if geo_roles and geo_roles.region_attr: + region_keys.append(geo_roles.region_attr) + region_keys.extend(("region", "geography", "location")) + + for key in region_keys: + value = agent.get(key) + if value is None: + continue + text = str(value).strip() + if text: + return text + + return "US" + + def sample_population( spec: PopulationSpec, count: int, @@ -183,6 +299,7 @@ def sample_population( on_progress: ItemProgressCallback | None = None, household_config: HouseholdConfig | None = None, agent_focus_mode: Literal["primary_only", "couples", "all"] | None = None, + semantic_roles: SamplingSemanticRoles | None = None, ) -> SamplingResult: """ Generate agents from a PopulationSpec. @@ -257,15 +374,27 @@ def sample_population( on_progress, hh_config, agent_focus_mode=agent_focus_mode, + semantic_roles=semantic_roles, + ) + _reconcile_household_coherence( + agents, attr_map, stats, semantic_roles=semantic_roles ) else: agents = _sample_population_independent( - spec, attr_map, rng, n, id_width, stats, numeric_values, on_progress + spec, + attr_map, + rng, + n, + id_width, + stats, + numeric_values, + on_progress, + semantic_roles=semantic_roles, ) households = [] - # Compute final statistics - _finalize_stats(stats, numeric_values, len(agents)) + # Recompute descriptive stats from finalized agent payloads + _recompute_descriptive_stats(spec, agents, stats) # Check expression constraints _check_expression_constraints(spec, agents, stats) @@ -303,12 +432,20 @@ def _sample_population_independent( stats: SamplingStats, numeric_values: dict[str, list[float]], on_progress: ItemProgressCallback | None = None, + semantic_roles: SamplingSemanticRoles | None = None, ) -> list[dict[str, Any]]: """Sample N agents independently (legacy path).""" agents: list[dict[str, Any]] = [] for i in range(n): agent = _sample_single_agent( - spec, attr_map, rng, i, id_width, stats, numeric_values + spec, + attr_map, + rng, + i, + id_width, + stats, + numeric_values, + semantic_roles=semantic_roles, ) agents.append(agent) if on_progress: @@ -322,7 +459,8 @@ def _generate_npc_partner( categorical_options: dict[str, list[str]], rng: random.Random, config: HouseholdConfig, - name_config: NameConfig | None = None, + geography_hint: str | None = None, + semantic_roles: SamplingSemanticRoles | None = None, ) -> dict[str, Any]: """Generate a lightweight NPC partner profile for context. @@ -340,9 +478,11 @@ def _generate_npc_partner( "age", "int", primary["age"], - None, # Uses gaussian offset rng, config, + semantic_type=getattr(attr_map.get("age"), "semantic_type", "age"), + identity_type=getattr(attr_map.get("age"), "identity_type", None), + policy_override=_resolve_partner_policy_override("age", semantic_roles), ) # Process attributes based on their scope @@ -359,10 +499,14 @@ def _generate_npc_partner( attr_name, attr.type, primary[attr_name], - attr.correlation_rate, rng, config, available_options=categorical_options.get(attr_name), + semantic_type=attr.semantic_type, + identity_type=attr.identity_type, + policy_override=_resolve_partner_policy_override( + attr_name, semantic_roles + ), ) # Individual scope: skip for NPC (not enough data to sample fully) @@ -376,8 +520,12 @@ def _generate_npc_partner( gender=partner["gender"], ethnicity=str(ethnicity) if ethnicity else None, birth_decade=birth_decade, + country=_resolve_country_hint( + primary, + geography_hint, + semantic_roles=semantic_roles, + ), seed=rng.randint(0, 2**31), - name_config=name_config, ) partner["first_name"] = first_name @@ -399,6 +547,7 @@ def _sample_dependent_as_agent( dependent: Any, parent: dict[str, Any], household_id: str, + semantic_roles: SamplingSemanticRoles | None = None, ) -> dict[str, Any]: """Promote a dependent to a full agent with all attributes sampled. @@ -406,7 +555,14 @@ def _sample_dependent_as_agent( then samples remaining attributes normally. """ agent = _sample_single_agent( - spec, attr_map, rng, index, id_width, stats, numeric_values + spec, + attr_map, + rng, + index, + id_width, + stats, + numeric_values, + semantic_roles=semantic_roles, ) # Override with dependent's known attributes @@ -417,7 +573,7 @@ def _sample_dependent_as_agent( agent["relationship_to_primary"] = dependent.relationship dep_name = str(getattr(dependent, "name", "")).strip() if dep_name: - agent["first_name"] = dep_name + agent["first_name"] = dep_name.split()[0] if parent.get("last_name"): agent["last_name"] = parent["last_name"] @@ -443,6 +599,7 @@ def _sample_population_households( on_progress: ItemProgressCallback | None = None, config: HouseholdConfig | None = None, agent_focus_mode: Literal["primary_only", "couples", "all"] | None = None, + semantic_roles: SamplingSemanticRoles | None = None, ) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]: """Sample agents in household units with correlated demographics. @@ -480,7 +637,14 @@ def _sample_population_households( # Sample Adult 1 (primary) — always an agent adult1 = _sample_single_agent( - spec, attr_map, rng, agent_index, id_width, stats, numeric_values + spec, + attr_map, + rng, + agent_index, + id_width, + stats, + numeric_values, + semantic_roles=semantic_roles, ) adult1_age = adult1.get("age", 35) agent_index += 1 @@ -523,6 +687,7 @@ def _sample_population_households( household_attrs, categorical_options, config, + semantic_roles=semantic_roles, ) adult2["household_id"] = household_id adult2["household_role"] = "adult_secondary" @@ -541,7 +706,8 @@ def _sample_population_households( categorical_options, rng, config, - name_config=spec.meta.name_config, + geography_hint=spec.meta.geography, + semantic_roles=semantic_roles, ) adult1["partner_npc"] = npc_partner adult1["partner_id"] = None @@ -557,7 +723,12 @@ def _sample_population_households( rng, config, ethnicity=adult1.get("race_ethnicity"), - name_config=spec.meta.name_config, + country=_resolve_country_hint( + adult1, + spec.meta.geography, + semantic_roles=semantic_roles, + ), + household_last_name=adult1.get("last_name"), ) if has_kids and focus_mode == "all": @@ -579,6 +750,7 @@ def _sample_population_households( dep, adult1, household_id, + semantic_roles=semantic_roles, ) agents.append(kid_agent) adult_ids.append(kid_agent["_id"]) @@ -640,6 +812,7 @@ def _sample_partner_agent( household_attrs: set[str], categorical_options: dict[str, list[str]], config: HouseholdConfig | None = None, + semantic_roles: SamplingSemanticRoles | None = None, ) -> dict[str, Any]: """Sample a partner agent with correlated demographics. @@ -666,10 +839,14 @@ def _sample_partner_agent( attr_name, attr.type, primary[attr_name], - attr.correlation_rate, rng, config, available_options=categorical_options.get(attr_name), + semantic_type=attr.semantic_type, + identity_type=attr.identity_type, + policy_override=_resolve_partner_policy_override( + attr_name, semantic_roles + ), ) else: # Individual scope: sample independently @@ -696,8 +873,12 @@ def _sample_partner_agent( gender=str(gender) if gender is not None else None, ethnicity=str(ethnicity) if ethnicity is not None else None, birth_decade=birth_decade, + country=_resolve_country_hint( + agent, + spec.meta.geography, + semantic_roles=semantic_roles, + ), seed=index, - name_config=spec.meta.name_config, ) agent["first_name"] = first_name @@ -712,6 +893,7 @@ def _sample_single_agent( id_width: int, stats: SamplingStats, numeric_values: dict[str, list[float]], + semantic_roles: SamplingSemanticRoles | None = None, ) -> dict[str, Any]: """Sample a single agent following the sampling order.""" agent: dict[str, Any] = {"_id": f"agent_{index:0{id_width}d}"} @@ -741,7 +923,6 @@ def _sample_single_agent( _update_stats(attr, value, stats, numeric_values) # Generate demographically-plausible name - name_config = spec.meta.name_config gender = agent.get("gender") or agent.get("sex") ethnicity = ( agent.get("race_ethnicity") or agent.get("ethnicity") or agent.get("race") @@ -752,8 +933,12 @@ def _sample_single_agent( gender=str(gender) if gender is not None else None, ethnicity=str(ethnicity) if ethnicity is not None else None, birth_decade=birth_decade, + country=_resolve_country_hint( + agent, + spec.meta.geography, + semantic_roles=semantic_roles, + ), seed=index, - name_config=name_config, ) agent["first_name"] = first_name agent["last_name"] = last_name @@ -795,7 +980,7 @@ def _sample_attribute( # No modifiers, sample directly return sample_distribution(attr.sampling.distribution, rng, agent) - value, triggered = apply_modifiers_and_sample( + value, triggered, condition_warnings = apply_modifiers_and_sample( attr.sampling.distribution, attr.sampling.modifiers, rng, @@ -806,6 +991,13 @@ def _sample_attribute( if attr.name in stats.modifier_triggers: for idx in triggered: stats.modifier_triggers[attr.name][idx] += 1 + for warning in condition_warnings: + if len(stats.condition_warnings) < 100: + stats.condition_warnings.append(f"{attr.name}: {warning}") + elif len(stats.condition_warnings) == 100: + stats.condition_warnings.append( + "... additional condition warnings truncated" + ) return value @@ -867,6 +1059,271 @@ def _finalize_stats( stats.attribute_stds[name] = 0.0 +def _recompute_descriptive_stats( + spec: PopulationSpec, + agents: list[dict[str, Any]], + stats: SamplingStats, +) -> None: + """Recompute descriptive stats from finalized agents.""" + stats.attribute_means = {} + stats.attribute_stds = {} + stats.categorical_counts = {} + stats.boolean_counts = {} + + numeric_values: dict[str, list[float]] = {} + for attr in spec.attributes: + if attr.type in ("int", "float"): + numeric_values[attr.name] = [] + elif attr.type == "categorical": + stats.categorical_counts[attr.name] = {} + elif attr.type == "boolean": + stats.boolean_counts[attr.name] = {True: 0, False: 0} + + for agent in agents: + for attr in spec.attributes: + if attr.name not in agent: + continue + value = agent[attr.name] + if attr.type in ("int", "float") and isinstance(value, (int, float)): + numeric_values[attr.name].append(float(value)) + elif attr.type == "categorical": + key = str(value) + stats.categorical_counts[attr.name][key] = ( + stats.categorical_counts[attr.name].get(key, 0) + 1 + ) + elif attr.type == "boolean": + key = bool(value) + stats.boolean_counts[attr.name][key] = ( + stats.boolean_counts[attr.name].get(key, 0) + 1 + ) + + _finalize_stats(stats, numeric_values, len(agents)) + + +def _normalize_attr_token(name: str) -> str: + """Normalize attribute tokens for tolerant matching.""" + token = name.strip().lower() + token = re.sub(r"[\s\-]+", "_", token) + token = re.sub(r"[^a-z0-9_]", "", token) + token = re.sub(r"_+", "_", token).strip("_") + return token + + +def _find_attr_name( + attr_map: dict[str, AttributeSpec], + aliases: set[str], +) -> str | None: + """Find an attribute by normalized alias set.""" + normalized_aliases = {_normalize_attr_token(a) for a in aliases} + for name in attr_map: + if _normalize_attr_token(name) in normalized_aliases: + return name + return None + + +def _resolve_partner_policy_override( + attr_name: str, + semantic_roles: SamplingSemanticRoles | None, +) -> str | None: + """Resolve scenario-owned partner-correlation policy override.""" + if semantic_roles is None: + return None + override = semantic_roles.partner_correlation_roles.get(attr_name) + return str(override) if override else None + + +def _pick_marital_value( + attr: AttributeSpec, + has_partner: bool, + marital_roles: MaritalRoles | None = None, +) -> Any | None: + """Pick a coherent marital value based on realized partner status.""" + if attr.type == "boolean": + return has_partner + + if ( + attr.type != "categorical" + or attr.sampling.distribution is None + or not hasattr(attr.sampling.distribution, "options") + ): + return None + + options = list(getattr(attr.sampling.distribution, "options", []) or []) + if not options: + return None + + if marital_roles and marital_roles.attr: + if _normalize_attr_token(attr.name) == _normalize_attr_token( + marital_roles.attr + ): + preferred = ( + marital_roles.partnered_values + if has_partner + else marital_roles.single_values + ) + for value in preferred: + if value in options: + return value + + normalized = {opt: _normalize_attr_token(opt) for opt in options} + partnered_tokens = ( + "married", + "partner", + "cohabit", + "relationship", + "civil_union", + ) + single_tokens = ("single", "unmarried", "not_married", "divorced", "widowed") + + token_set = partnered_tokens if has_partner else single_tokens + for opt, norm in normalized.items(): + if any(token in norm for token in token_set): + return opt + return options[0] + + +def _extract_surname_from_name(name: Any) -> str | None: + """Extract surname token from a full-name-like value.""" + if not isinstance(name, str): + return None + parts = [p for p in name.strip().split() if p] + if len(parts) < 2: + return None + return parts[-1] + + +def _household_surname_seed( + primary: dict[str, Any], members: list[dict[str, Any]] +) -> str | None: + """Choose canonical household surname from available household context.""" + for candidate in (primary.get("last_name"),): + if isinstance(candidate, str) and candidate.strip(): + return candidate.strip() + + for member in members: + candidate = member.get("last_name") + if isinstance(candidate, str) and candidate.strip(): + return candidate.strip() + + partner_npc = primary.get("partner_npc") + if isinstance(partner_npc, dict): + candidate = partner_npc.get("last_name") + if isinstance(candidate, str) and candidate.strip(): + return candidate.strip() + + dependents = primary.get("dependents") + if isinstance(dependents, list): + for dep in dependents: + if not isinstance(dep, dict): + continue + candidate = _extract_surname_from_name(dep.get("name")) + if candidate: + return candidate + + return None + + +def _reconcile_household_coherence( + agents: list[dict[str, Any]], + attr_map: dict[str, AttributeSpec], + stats: SamplingStats, + semantic_roles: SamplingSemanticRoles | None = None, +) -> None: + """Deterministically reconcile household coherence after realization.""" + by_household: dict[str, list[dict[str, Any]]] = {} + for agent in agents: + hid = agent.get("household_id") + if not isinstance(hid, str) or not hid: + continue + by_household.setdefault(hid, []).append(agent) + + if not by_household: + return + + marital_attr = _find_attr_name( + attr_map, + {"marital_status", "marital", "relationship_status"}, + ) + household_size_attr = _find_attr_name(attr_map, {"household_size"}) + + marital_updates = 0 + household_size_updates = 0 + surname_updates = 0 + + for members in by_household.values(): + primary = next( + (m for m in members if m.get("household_role") == "adult_primary"), + members[0], + ) + dependents = primary.get("dependents", []) + npc_partner_count = 1 if primary.get("partner_npc") else 0 + npc_dependents_count = len(dependents) if isinstance(dependents, list) else 0 + realized_size = len(members) + npc_partner_count + npc_dependents_count + household_surname = _household_surname_seed(primary, members) + + if household_size_attr: + for member in members: + current = member.get(household_size_attr) + if current != realized_size: + member[household_size_attr] = realized_size + household_size_updates += 1 + + if household_surname: + for member in members: + if member.get("last_name") != household_surname: + member["last_name"] = household_surname + surname_updates += 1 + + partner_npc = primary.get("partner_npc") + if isinstance(partner_npc, dict): + if partner_npc.get("last_name") != household_surname: + partner_npc["last_name"] = household_surname + surname_updates += 1 + + # Dependents are represented as NPC dicts with `name`. + # Normalize them to the household surname for consistency. + if isinstance(dependents, list): + for dep in dependents: + if not isinstance(dep, dict): + continue + dep_name = dep.get("name") + if isinstance(dep_name, str) and dep_name.strip(): + first = dep_name.strip().split()[0] + corrected = f"{first} {household_surname}" + if dep_name != corrected: + dep["name"] = corrected + surname_updates += 1 + + if marital_attr: + marital_spec = attr_map[marital_attr] + for member in members: + has_partner = bool( + member.get("partner_id") or member.get("partner_npc") + ) + desired = _pick_marital_value( + marital_spec, + has_partner, + marital_roles=( + semantic_roles.marital_roles if semantic_roles else None + ), + ) + if desired is None: + continue + if member.get(marital_attr) != desired: + member[marital_attr] = desired + marital_updates += 1 + + reconciliation: dict[str, int] = {} + if marital_updates: + reconciliation["marital_status_updates"] = marital_updates + if household_size_updates: + reconciliation["household_size_updates"] = household_size_updates + if surname_updates: + reconciliation["household_surname_updates"] = surname_updates + if reconciliation: + stats.reconciliation_counts.update(reconciliation) + + def _check_expression_constraints( spec: PopulationSpec, agents: list[dict[str, Any]], diff --git a/extropy/population/sampler/households.py b/extropy/population/sampler/households.py index 9eeb04b..40aa51c 100644 --- a/extropy/population/sampler/households.py +++ b/extropy/population/sampler/households.py @@ -1,8 +1,8 @@ """Household-based sampling for co-sampling correlated agent pairs. All household composition rates and correlation tables are read from -a HouseholdConfig instance (populated by LLM research at spec time, -with US Census defaults as the safety net). +a HouseholdConfig instance (scenario-owned config, with deterministic +defaults as the safety net). Attribute scopes are now spec-driven: - scope="household": shared across all household members @@ -14,14 +14,11 @@ import math import random -from typing import Any, TYPE_CHECKING +from typing import Any, Literal from ...core.models.population import Dependent, HouseholdConfig, HouseholdType from ..names.generator import generate_name -if TYPE_CHECKING: - from ...core.models.population import NameConfig - def _age_bracket(age: int, config: HouseholdConfig) -> str: """Map age to bracket key using config age brackets.""" @@ -74,32 +71,48 @@ def correlate_partner_attribute( attr_name: str, attr_type: str, primary_value: Any, - correlation_rate: float | None, rng: random.Random, config: HouseholdConfig, available_options: list[str] | None = None, + semantic_type: str | None = None, + identity_type: str | None = None, + policy_override: Literal[ + "gaussian_offset", + "same_group_rate", + "same_country_rate", + "same_value_probability", + ] + | None = None, ) -> Any: """Produce a correlated value for a partner based on the primary's value. - Uses the correlation_rate from the attribute spec. Special handling: - - age (int/float): Gaussian offset using config.partner_age_gap_mean/std - - race_ethnicity-like attrs: Per-group rates from config.same_group_rates - - Other categorical/boolean: Simple probability of same value + Runtime fallback chain: + 1) household_config.assortative_mating[attr_name] (if configured) + 2) Policy defaults (age/race/country special handling + general default) Args: attr_name: Name of the attribute attr_type: Type of the attribute (int, float, categorical, boolean) primary_value: The primary partner's value - correlation_rate: Probability (0-1) that partner has same value, or None for defaults rng: Random number generator config: HouseholdConfig with default rates available_options: For categorical attrs, list of valid options to sample from + semantic_type: Optional semantic metadata from AttributeSpec + identity_type: Optional identity metadata from AttributeSpec Returns: The correlated value for the partner. """ - # Age uses gaussian offset, not simple correlation - if attr_name == "age" and attr_type in ("int", "float"): + policy = _resolve_partner_correlation_policy( + attr_name=attr_name, + attr_type=attr_type, + semantic_type=semantic_type, + identity_type=identity_type, + policy_override=policy_override, + ) + + # Age-like attributes use gaussian offset, not simple same-value probability + if policy == "gaussian_offset": partner_age = int( round( rng.gauss( @@ -110,8 +123,8 @@ def correlate_partner_attribute( ) return max(config.min_adult_age, partner_age) - # Race/ethnicity uses per-group rates from config - if attr_name in ("race_ethnicity", "ethnicity", "race"): + # Race/ethnicity-like attributes use per-group rates + if policy == "same_group_rate": same_rate = config.same_group_rates.get( str(primary_value).lower(), config.default_same_group_rate ) @@ -123,12 +136,12 @@ def correlate_partner_attribute( return rng.choice(others) return primary_value - # Country uses same_country_rate from config if no explicit rate - if attr_name == "country": - rate = ( - correlation_rate - if correlation_rate is not None - else config.same_country_rate + # Country/citizenship-like attributes use same_country_rate default + if policy == "same_country_rate": + rate = _resolve_same_value_rate( + attr_name=attr_name, + config=config, + default_rate=config.same_country_rate, ) if rng.random() < rate: return primary_value @@ -138,11 +151,11 @@ def correlate_partner_attribute( return rng.choice(others) return primary_value - # For all other attributes, use the explicit correlation_rate or a default - rate = ( - correlation_rate - if correlation_rate is not None - else config.default_same_group_rate + # General same-value policy + rate = _resolve_same_value_rate( + attr_name=attr_name, + config=config, + default_rate=config.default_same_group_rate, ) if rng.random() < rate: return primary_value @@ -156,6 +169,62 @@ def correlate_partner_attribute( return primary_value +def _resolve_partner_correlation_policy( + attr_name: str, + attr_type: str, + semantic_type: str | None, + identity_type: str | None, + policy_override: Literal[ + "gaussian_offset", + "same_group_rate", + "same_country_rate", + "same_value_probability", + ] + | None = None, +) -> str: + """Resolve partner-correlation policy without hardcoded branching spread.""" + if policy_override in { + "gaussian_offset", + "same_group_rate", + "same_country_rate", + "same_value_probability", + }: + return policy_override + + normalized_name = attr_name.strip().lower() + if semantic_type == "age": + return "gaussian_offset" + if attr_type in {"int", "float"} and normalized_name == "age": + return "gaussian_offset" + + if identity_type == "race_ethnicity" or normalized_name in { + "race_ethnicity", + "ethnicity", + "race", + }: + return "same_group_rate" + + if identity_type == "citizenship" or normalized_name in { + "country", + "nationality", + "citizenship", + }: + return "same_country_rate" + + return "same_value_probability" + + +def _resolve_same_value_rate( + attr_name: str, + config: HouseholdConfig, + default_rate: float, +) -> float: + """Resolve deterministic same-value probability for partner correlation.""" + if attr_name in config.assortative_mating: + return config.assortative_mating[attr_name] + return default_rate + + def generate_dependents( household_type: HouseholdType, household_size: int, @@ -164,7 +233,8 @@ def generate_dependents( rng: random.Random, config: HouseholdConfig, ethnicity: str | None = None, - name_config: NameConfig | None = None, + country: str = "US", + household_last_name: str | None = None, ) -> list[Dependent]: """Generate NPC dependents for a household. @@ -189,13 +259,18 @@ def generate_dependents( dep_first, _ = generate_name( gender=elderly_gender, ethnicity=ethnicity, + country=country, age=elderly_age, seed=rng.randint(0, 2**31), - name_config=name_config, + ) + dep_name = ( + f"{dep_first} {household_last_name}".strip() + if household_last_name + else dep_first ) dependents.append( Dependent( - name=dep_first, + name=dep_name, age=elderly_age, gender=elderly_gender, relationship=relationship, @@ -213,13 +288,18 @@ def generate_dependents( child_first, _ = generate_name( gender=child_gender, ethnicity=ethnicity, + country=country, age=child_age, seed=rng.randint(0, 2**31), - name_config=name_config, + ) + child_name = ( + f"{child_first} {household_last_name}".strip() + if household_last_name + else child_first ) dependents.append( Dependent( - name=child_first, + name=child_name, age=child_age, gender=child_gender, relationship=relationship, From a7ce9d913b29ca65cddf5d3c60efb86f1d8223b1 Mon Sep 17 00:00:00 2001 From: DeveshParagiri Date: Fri, 20 Feb 2026 01:00:04 -0500 Subject: [PATCH 03/20] align docs and tests with removed name/correlation fields --- docs/capabilities.md | 22 +- docs/pipeline/sample.md | 564 +++++++++++++++++++++++++++++++ docs/pipeline/spec.md | 488 ++++++++++++++++++++++++++ skills/extropy/SCENARIOS.md | 4 +- test-v2-e2e/extended.yaml | 1 - test-v2-e2e/spec.yaml | 1 - tests/test_household_sampling.py | 62 ++-- tests/test_name_generator.py | 136 ++------ 8 files changed, 1145 insertions(+), 133 deletions(-) create mode 100644 docs/pipeline/sample.md create mode 100644 docs/pipeline/spec.md diff --git a/docs/capabilities.md b/docs/capabilities.md index a95ed64..4c3a79d 100644 --- a/docs/capabilities.md +++ b/docs/capabilities.md @@ -2,15 +2,13 @@ This document walks through Extropy's capabilities by example. Each section describes a type of scenario you can run today, with the underlying mechanics that make it work. ---- - ## Any Country, Any Culture Extropy isn't locked to US demographics. You can simulate populations anywhere. **US population responding to a Netflix price hike**: Out of the box. Names come from bundled SSA baby name data (1940-2010 birth decades) and Census surname data by ethnicity. A 45-year-old Black woman born in 1980 gets a name that reflects naming trends for Black girls in that era. Her white husband gets an appropriately correlated name. Their kids get names consistent with 2010s trends. -**Japanese employees reacting to a remote work policy**: Works. You provide a `NameConfig` with Japanese naming conventions (or let the LLM research it), and Extropy generates culturally appropriate names. The simulation mechanics are identical - the agents reason in first-person, form opinions, share with their network. +**Japanese employees reacting to a remote work policy**: Works. Extropy uses Faker-first locale routing with geography hints and CSV fallback for culturally plausible names. The simulation mechanics are identical - the agents reason in first-person, form opinions, share with their network. **Indian consumers across Mumbai, Delhi, and Bangalore responding to a fintech launch**: Works. Define city as an attribute with your desired distribution. Agents get sampled across cities, connected via network edges that respect geography, and exposed to marketing through channels you define. @@ -18,8 +16,6 @@ Extropy isn't locked to US demographics. You can simulate populations anywhere. The pattern: define your population's attributes and distributions, optionally provide country-specific name data, and run the pipeline. The simulation engine doesn't care about geography - it cares about attributes, networks, and reasoning. ---- - ## Individuals or Households You control whether agents exist as isolated individuals or as family units. @@ -28,7 +24,7 @@ You control whether agents exist as isolated individuals or as family units. **Households deciding whether to adopt rooftop solar**: Set `household_mode: true`. Now you get family units. A married couple shares a `household_id` and `last_name`. They have correlated attributes - similar education levels, aligned political views (with configurable assortative mating rates), compatible religious backgrounds. -The `agent_focus` field in your population spec controls who reasons vs. who exists as context: +The scenario-level `agent_focus_mode` field controls who reasons vs. who exists as context: **Primary only (default)**: Only the primary adult in each household is a reasoning agent. Partners and children exist as NPC data attached to that agent - named, with attributes, but not making decisions. Use this when you care about one decision-maker per household (e.g., "the subscriber", "the homeowner"). @@ -36,9 +32,10 @@ The `agent_focus` field in your population spec controls who reasons vs. who exi **All (families)**: Everyone in the household is a reasoning agent, including children old enough to have opinions. Use this when family dynamics matter (e.g., teens influencing parents on tech adoption, multi-generational disagreements). -The mechanism: set `agent_focus` in your population spec metadata. Values like "families", "households", or "everyone" trigger the "all" mode. Values like "couples", "partners", or "spouses" trigger couples mode. Everything else defaults to primary-only. +The mechanism: scenario generation sets `agent_focus_mode` to one of `primary_only`, `couples`, or `all`, and sample uses that value directly. **Household types are sampled by age bracket**: + - Singles (one adult, no kids) - Couples (two adults, no kids) - Single parents (one adult with kids) @@ -78,11 +75,13 @@ Some scenarios are a single shock. Others unfold over time. **Static: Netflix announces a price increase**: One event, one moment. Netflix raises prices by $3/month. Agents hear about it through news, social media, or email. They form opinions - cancel, keep, or downgrade. They share with their network. Over a few timesteps, information propagates, opinions stabilize, and you see the final distribution. The event itself doesn't change; what evolves is awareness and social influence. This is the right model when: + - The event is a discrete announcement or decision - What matters is how the population responds and influences each other - There's no new information after the initial shock **Evolving: Netflix password crackdown unfolds over months**: + - Month 1: Netflix announces upcoming password-sharing restrictions - Month 2: Enforcement begins in select markets - Month 3: Full rollout, first reports of account lockouts @@ -92,11 +91,13 @@ This is the right model when: Each timestep, agents see what's happened so far. Their prompts include a recap: "Over the past few months, Netflix first announced the crackdown, then started enforcing it. Last month they offered a cheaper add-on option." The timeline creates a narrative arc where agent reasoning evolves with new information. This is the right model when: + - The situation develops with new facts over time - Agent responses to Week 1 should differ from Week 5 - You want to model how opinions shift as circumstances change **Evolving: A crisis that develops and resolves**: + - Day 1: Initial reports of data breach, uncertainty about scope - Day 2: Company confirms breach, announces investigation - Day 3: Details emerge - 10 million accounts affected @@ -111,6 +112,7 @@ Agents experience the crisis as it unfolds. Early timesteps have high uncertaint **Timeline-safe stopping**: In evolving scenarios, convergence/quiescence auto-stop is suppressed while future timeline events remain (unless explicitly overridden). This prevents runs from ending before later events fire. **Per-event re-reasoning intensity**: Timeline events can carry `re_reasoning_intensity`: + - `normal`: direct timeline recipients force re-reasoning. - `high`: direct plus traced network propagation from that epoch can force re-reasoning. - `extreme`: high behavior plus one-time force for all aware agents when the event lands. @@ -136,6 +138,7 @@ People hear about things through different channels, and the channel matters. **Observation lets agents notice behavior**: Agents can witness what others do, not just hear what they say. When a neighbor buys an electric car, agents on that network edge might get exposed through observation. This models the "seeing is believing" dynamic. Each channel has: + - Reach probability (what fraction of eligible agents get exposed) - Targeting rules (which agents are eligible) - Experience template (how the agent encounters the information) @@ -157,6 +160,7 @@ You decide what you're measuring. **Open-ended responses**: "What are your main concerns?" Free text. The agent reasons naturally without being forced into categories. These skip the classification pass entirely - the reasoning itself is the outcome. You can mix outcome types. A scenario might have: + - A categorical position (support/oppose/neutral) - A boolean share intention - A continuous intensity score @@ -171,6 +175,7 @@ All get captured in the same simulation run. You control the tradeoff between cost and reasoning quality. **Two-pass (default)**: + 1. Pass 1 asks the agent to reason freely in first-person. No outcome categories in sight. Just "You're this person, this happened, how do you feel?" 2. Pass 2 takes that reasoning and classifies it into your defined outcomes using a faster, cheaper model. @@ -180,11 +185,13 @@ This separation prevents the central tendency problem where agents gravitate to Single call with both reasoning and outcomes in one schema. Cheaper - one API call instead of two. Faster - no round-trip between passes. But the agent sees the outcome categories while reasoning, which can bias responses toward the middle. Use merged pass for: + - Cost-sensitive runs with many agents - Quick exploratory simulations - Scenarios where you trust the model to reason past the schema Use two-pass for: + - Final production runs where quality matters - Scenarios with polarizing topics where central tendency is a real risk - Research where reasoning traces need to be unbiased @@ -244,6 +251,7 @@ Agents can talk to each other. When reasoning, an agent can choose to initiate a **Conflict resolution**: When multiple agents want to talk to the same target, priority determines who wins. Higher relationship weight wins - a partner request beats a coworker request. Deferred requests can execute in later timesteps. **Fidelity control**: The `--fidelity` flag controls conversation depth and prompt richness: + - `low`: No conversations, last 5 memory traces, basic prompts - `medium` (default): 2 turns (4 messages), 1 conversation per agent, full memory traces - `high`: 3 turns (6 messages), up to 2 conversations per agent, explicit THINK vs SAY separation, repetition detection diff --git a/docs/pipeline/sample.md b/docs/pipeline/sample.md new file mode 100644 index 0000000..ae5dec6 --- /dev/null +++ b/docs/pipeline/sample.md @@ -0,0 +1,564 @@ +# Sample Stage Reference: Agent Instantiation and Household Realization + +## Document Intent +This file is the deep reference for the `sample` stage in Extropy. + +It is meant to be reusable team memory for: +- what `sample` is supposed to do, +- how it should do it, +- why this stage exists in the pipeline, +- what it currently does in code, +- where the current behavior diverges from intended quality goals, +- and what backpressure checks this stage should ultimately support. + +This is not a patch plan. It is a contract + current-state reality map. + +## Status Update (2026-02-20) +- Merged sampling order now recomputes topologically across base + extension attributes. +- Post-household reconciliation now deterministically aligns: + - marital/partner coherence + - household size with realized household composition + - household surnames across members, partner NPCs, and dependent name fields +- Reconciliation counters are recorded in sampling stats. +- Modifier condition-evaluation failures are now surfaced into `condition_warnings`. +- Deterministic rule-pack gate is applied post-sampling with impossible/implausible thresholds. +- Strict mode (`--strict-gates`) promotes high-risk warnings and condition-eval failures to hard blocks. +- Fail-hard sample paths now emit versioned `sample.invalid.vN.json` diagnostics. + +--- + +## How To Read This Document +Use this order: +1. `Intended Contract` for target behavior. +2. `Current Implementation (As-Is)` for actual behavior. +3. `Divergences, Mistakes, and Risk Areas` for the quality gap. +4. `Dependency Map` for downstream coupling (`network`, `simulate`, etc.). + +This file is intentionally verbose so a new teammate can recover context without prior chat history. + +--- + +## Why This Stage Exists +`spec` and `scenario` define generative rules. `sample` executes those rules. + +Without sampling, Extropy has only declarations, not a concrete population. +Downstream systems need concrete agents: +- `network` needs actual nodes with attributes, +- `simulate` needs actual individuals with persona context, +- `results/query/chat` need concrete rows tied to a scenario run. + +Design principle: +- **Compile semantics upstream, instantiate at scale in `sample`.** + +--- + +## Pipeline Position +Intended high-level flow: +1. `spec`: compile reusable base population mechanics. +2. `scenario`: compile scenario extension + event dynamics. +3. `persona`: compile rendering contract. +4. `sample`: instantiate concrete agents from merged population contract. +5. `network`: build social graph on sampled agents. +6. `simulate`: run behavioral dynamics. + +`sample` is the boundary where abstract distributions become actual synthetic people. + +--- + +## Intended Contract + +### What `sample` should do +`sample` should produce a concrete, scenario-specific population realization that is: +- statistically aligned with merged spec intent, +- internally consistent at agent and household levels, +- deterministic under seed, +- usable by downstream stages without semantic guessing. + +At a minimum it should: +- merge base + scenario extension semantics correctly, +- realize each agent in dependency-safe order, +- apply modifiers/constraints consistently, +- realize household structure when activated, +- assign realistic names with deterministic policy, +- persist agents + household records in canonical study storage, +- emit machine-usable diagnostics (stats + violations). + +### What `sample` should not do +`sample` should not: +- invent new schema semantics not defined upstream, +- silently reinterpret contradictory fields, +- depend on free-form LLM generation per agent, +- hide major consistency failures behind soft warnings, +- mutate scenario design intent at runtime. + +### How `sample` should do it +Intended execution model: +1. Load scenario + referenced base population. +2. Build merged population contract with dependency-safe sampling order. +3. Validate merged contract before execution. +4. Sample agents under deterministic RNG (seeded). +5. Apply household realization policy if scenario activates it. +6. Run post-sampling consistency checks. +7. Persist canonical outputs and diagnostics. + +### Why this contract matters +If sampling is weak, the entire pipeline degrades: +- network structure becomes fake or brittle, +- persona realism is disconnected from actual agents, +- simulation reasoning quality collapses, +- results become analytically untrustworthy. + +`sample` is therefore a quality gate, not just a utility command. + +--- + +## Conceptual Model Of Sampling + +### Merged-spec execution +`sample` should execute a merged generative program: +- base attributes (reusable population identity), +- plus scenario extension attributes (event-context identity/behavior fields). + +### Strategy semantics +Each attribute should be executed by strategy: +- `independent`: sample from distribution directly. +- `derived`: compute from formula deterministically. +- `conditional`: sample base distribution then apply triggered modifiers. + +### Household semantics +Household behavior should activate only when scenario/base semantics request it. +When active, attribute scopes should control sampling logic: +- `individual`: sampled per person. +- `household`: shared within household. +- `partner_correlated`: correlated partner realization. + +### NPC vs full-agent semantics +Scenario household scope policy should control who becomes full simulation agents: +- `primary_only`: one primary adult as full agent, partner/dependents as NPC context. +- `couples`: both adult partners are full agents, dependents mostly NPC. +- `all`: include adults + eligible dependents as full agents. + +### Constraint semantics +`sample` should separate: +- hard numeric clamping, +- impossible contradictions (should block), +- implausible but possible combinations (warn/measure). + +### Outlier semantics +`sample` should preserve realistic tails without breaking coherence: +- do not over-flatten to means, +- do not explode into nonsense combinations, +- expose measurable outlier behavior for backpressure. + +--- + +## Intended Artifact Contract + +### Primary runtime output +`sample` should emit: +- concrete agent records (`N` agents requested), +- run metadata (seed, count, timestamp, mode signals), +- sampling diagnostics/statistics, +- optional household table for household mode. + +### Canonical persistence contract +Expected canonical DB artifacts: +- `sample_runs`: run metadata, +- `agents`: per-agent full attribute JSON tied to scenario, +- `households` (if active): household type/adult/dependent/shared-attribute records. + +### Determinism contract +For fixed `(spec, scenario, seed, n)`: +- sampled outputs should be reproducible, +- IDs should be stable format, +- diagnostic summaries should be reproducible up to deterministic ordering rules. + +--- + +## Intended Quality Contract + +### Realism +Agents should look like plausible members of the target population and scenario context. + +### Internal consistency +Cross-attribute logic should hold: +- dependency-safe realization, +- constraint-respecting values, +- coherent household/person relationships. + +### Downstream support reliability +Output should be directly consumable by: +- `network` structural edge logic, +- `persona` rendering assumptions, +- `simulate` contact/NPC logic, +- `results/query/chat` retrieval semantics. + +### Backpressure intent +Sampling should expose machine-checkable quality signals for go/no-go decisions, not only human-readable summaries. + +--- + +## Current Implementation (As-Is) + +This section describes actual behavior in code today. + +### CLI command flow +`extropy sample` currently performs: +1. resolve study folder and scenario, +2. pre-flight require persona config exists, +3. load scenario YAML, +4. load base population referenced by `scenario.meta.base_population`/`population_spec`, +5. merge `extended_attributes` onto base attributes, +6. build merged sampling order by appending missing extension names to base order, +7. validate merged spec via `validate_spec` (unless `--skip-validation`), +8. call `sample_population(...)` with scenario `household_config` + `agent_focus_mode`, +9. save result to `study.db`. + +Primary file: +- `extropy/cli/commands/sample.py` + +### Merge behavior in command +Current merge is simple list merge: +- `merged_attributes = base_attributes + extended_attributes` +- `merged_sampling_order = base_order + [extension names not already present]` + +No merged topological recomputation is performed in this command. + +### Validation gate behavior +Validation uses population validator (structural errors + semantic warnings): +- structural failures block sampling unless `--skip-validation`. +- warnings do not block. + +Primary files: +- `extropy/population/validator/spec.py` +- `extropy/population/validator/structural.py` +- `extropy/population/validator/semantic.py` + +### Sampling engine dispatch +Sampler chooses mode by schema inspection: +- household mode if any attribute has `scope == "household"`. +- independent mode otherwise. + +Primary file: +- `extropy/population/sampler/core.py` + +### Strategy execution behavior +Per-attribute execution: +- `derived`: `eval_formula(...)` +- `independent`: `sample_distribution(...)` +- `conditional`: `apply_modifiers_and_sample(...)` if modifiers exist, else direct distribution sample. + +Distribution types implemented: +- `normal`, `lognormal`, `uniform`, `beta`, `categorical`, `boolean` + +Formula-based distribution params supported for numeric distributions (`*_formula` fields). + +Primary files: +- `extropy/population/sampler/core.py` +- `extropy/population/sampler/distributions.py` +- `extropy/population/sampler/modifiers.py` +- `extropy/utils/eval_safe.py` + +### Modifier behavior (actual) +- Numeric distributions: all matching modifiers stack (`multiply` product + `add` sum), then reclamp to min/max bounds. +- Categorical: last matching `weight_overrides` wins. +- Boolean: last matching `probability_override` wins; otherwise multiply/add transforms base probability, then clamp `[0,1]`. + +Modifier condition evaluation failures are logged and ignored for that modifier. + +### Type coercion behavior +Sampler coerces sampled values to declared `attr.type`: +- int coercion strips non-digits for string numerics like `"6+"`. +- float coercion strips non-numeric chars. +- boolean coercion maps strings like `"true"`, `"1"`, `"yes"`. +- categorical coerces to string. + +### Constraint behavior +Current runtime constraint handling is split: +- hard numeric bounds (`hard_min`/`hard_max`, plus legacy `min`/`max`) are applied as clamping. +- expression constraints are checked post-sampling and counted in `stats.constraint_violations`. +- `spec_expression` constraints are not run against agents (treated as spec-level only). + +Expression check exceptions are swallowed for the affected constraint evaluation. + +### Household sampling behavior +When household mode is active: +- primary adult is sampled first, +- household type is sampled from `HouseholdConfig` age-bracket weights, +- partner realization depends on `agent_focus_mode`: + - `primary_only`: partner is NPC profile in `partner_npc`, `partner_id=None`. + - `couples`: partner is full agent with `partner_id` reciprocal link. + - `all`: partner is full agent; eligible dependents may be promoted to full agents. +- dependents are generated from household type and age constraints. +- in `all`, dependents above `min_agent_age` can be promoted to full agents; others remain NPC dependent data. + +Sampler trims overflow agents if final household overshoots `target_n`. + +Primary files: +- `extropy/population/sampler/core.py` +- `extropy/population/sampler/households.py` + +### Minor-dependent normalization behavior +Promoted dependent agents (minors) undergo normalization using attribute `semantic_type`: +- education/employment/occupation/income coerced to age-appropriate forms. + +This is deterministic rule-based normalization in sampler core. + +### Partner correlation behavior +For `scope="partner_correlated"`, partner value generation uses: +- special case `age`: gaussian age-gap logic from `HouseholdConfig`. +- special case `race/ethnicity`: same-group rates. +- special case `country`: same-country rate. +- fallback: assortative-mating table lookup or default same-group rate. + +### Name generation behavior +Names are generated during sampling, not in `spec`/`scenario` writing. + +Current runtime path: +- sampler calls `generate_name(...)` per primary/partner/dependent generation path, +- uses Faker-first locale routing from country/region hints, +- falls back to bundled CSV first/last name frequencies if Faker locale generation fails, +- gender/ethnicity/age-derived birth decade drive selection. + +Primary files: +- `extropy/population/sampler/core.py` +- `extropy/population/names/generator.py` + +### Stats and diagnostics behavior +Sampler collects: +- numeric means/std, +- categorical counts, +- boolean counts, +- modifier trigger counts, +- expression constraint violation counts. + +Model also has `condition_warnings`, but sampler currently does not populate it. + +Primary file: +- `extropy/core/models/sampling.py` + +### Persistence behavior +After sampling: +- saves base population YAML text into `population_specs` under `population_id=scenario_name` (backward-compat path), +- saves sampled agents into `agents` with `scenario_id=scenario_name`, +- saves run metadata into `sample_runs`, +- saves households (if present) into `households`. + +Existing records are replaced per scenario scope (`DELETE FROM agents WHERE scenario_id = ?`) before insert. + +Primary files: +- `extropy/cli/commands/sample.py` +- `extropy/storage/study_db.py` + +--- + +## Current Runtime Coupling With Downstream Stages + +### `network` coupling +`network` requires sampled agents for scenario ID and consumes household fields (`household_id`, `partner_id`, `dependents`) for structural edges. + +Primary files: +- `extropy/cli/commands/network.py` +- `extropy/population/network/generator.py` + +### `persona` coupling +`sample` command enforces persona pre-flight existence, even though sampler itself does not read persona config. + +Primary file: +- `extropy/cli/commands/sample.py` + +### `simulate` coupling +Simulation requires sampled agents and loads households from DB. +It also consumes household-adjacent agent fields for contacts/NPC conversation context. + +Primary files: +- `extropy/cli/commands/simulate.py` +- `extropy/simulation/engine.py` +- `extropy/simulation/conversation.py` + +### `results/query/chat` coupling +All downstream query/results/chat behavior depends on quality and consistency of the concrete sampled agent rows persisted in `study.db`. + +--- + +## What Current Implementation Already Gets Right + +### Strong deterministic execution core +Given a seed and fixed inputs, sampler behavior is reproducible and non-LLM at runtime. + +### Broad distribution and modifier support +Sampler supports rich distribution types, formula parameters, and conditional modifiers. + +### Household-aware realization exists end-to-end +Household sampling, partner correlation, dependent generation, and NPC/full-agent modes are implemented in core runtime. + +### Expression constraints are measured +Agent-level expression violations are counted and exposed in stats (not silently ignored entirely). + +### Canonical persistence path is in place +`sample` writes to a unified study database with scenario-scoped retrieval patterns used by downstream commands. + +--- + +## Divergences, Mistakes, and Risk Areas + +### 1) Merged sampling order is append-based, not recomputed +The command appends extension attributes to base sampling order instead of recomputing full merged topological order. + +Risk: +- extension-to-extension dependency order can be fragile if extension list order is not dependency-safe. + +### 2) Scenario binder output `sampling_order` is not persisted/used by `sample` +Scenario creation computes extension sampling order via binder, but `sample` does not consume a dedicated extension order artifact. + +Risk: +- compile-time dependency resolution work may be partially dropped at execution boundary. + +### 3) Household activation tied only to `scope="household"` +Sampler switches to household mode only if at least one household-scoped attribute exists. + +Risk: +- scenario may provide household config/focus mode but not trigger household mode if scope annotations are missing or inconsistent. + +### 4) Persona is required as pre-flight for `sample` +Current command requires persona config before sampling even though sampler core does not depend on persona content. + +Risk: +- operational coupling can block sampling workflows unrelated to sampler logic. + +### 5) Name field shape mismatch risk in NPC partner usage +Sampler writes NPC partner fields with `first_name`/`last_name` style semantics, while parts of simulation conversation/contact logic look for `partner_npc.name` in several paths. + +Risk: +- household NPC addressing and partner reference resolution can degrade. + +### 6) Partner key mismatch risk in simulation contact paths +Sampler uses `partner_id`; parts of simulation contact/conversation logic also check `partner_agent_id`. + +Risk: +- some partner-as-agent references may not resolve uniformly. + +### 7) Condition warning channel is modeled but unused +`SamplingStats.condition_warnings` exists in model but is not currently populated by sampler. + +Risk: +- reduced observability when modifier conditions fail frequently or unexpectedly. + +### 8) Expression constraint failures are counted post-hoc, not gating +Expression constraints currently contribute diagnostics but do not block run completion. + +Risk: +- samples with significant inconsistency may proceed downstream unless external gates inspect stats. + +### 9) Type coercion can mask upstream categorical/schema drift +Aggressive coercion (e.g., stripping symbols from numeric-like strings) keeps sampling alive. + +Risk: +- upstream naming/schema mismatches may appear to "work" while distorting semantics. + +### 10) Persistence identity uses backward-compat population keying +Sample saves population spec under `population_id=scenario_name` backward-compat behavior. + +Risk: +- conceptual blending of base population identity and scenario identity in storage layer. + +--- + +## How It Can Be Made Better (Direction, Not Patch Plan) + +### Promote merged-order contract as first-class artifact +Make merged dependency-safe order explicit and enforced at sample runtime. + +### Strengthen mismatch diagnostics +Expose dedicated machine-readable warnings for token/key mismatches and condition failures. + +### Tighten household/NPC schema contract +Keep partner/dependent key conventions consistent across sampler, persona renderer, and conversation resolution. + +### Separate structural validity from behavioral acceptance +Use staged backpressure where severe inconsistency blocks downstream progression. + +### Keep runtime deterministic but more observable +Retain non-LLM deterministic sampling while expanding run diagnostics quality. + +--- + +## Dependency Map (What `sample` Must Support) + +### `network` +Requires: +- stable `agent_id`s, +- coherent household/partner fields, +- realistic attribute distributions for similarity + structural edge logic. + +### `simulate` +Requires: +- complete agent rows, +- household/NPC context, +- scenario-scoped agent retrieval consistency. + +### `persona` runtime rendering +Requires: +- attribute-value consistency, +- household context fields for household section rendering. + +### `results`, `query`, `chat` +Require: +- canonical DB persistence, +- deterministic scenario scoping, +- minimal schema surprises in agent JSON. + +--- + +## Working Invariants Draft (Sample-Oriented) + +### Structural invariants +1. Exactly `N` agents returned for requested `--count`. +2. Every sampled attribute referenced in order exists in merged schema. +3. Every dependency expression name is known or explicitly flagged. + +### Household invariants +1. If household mode active, every agent has coherent `household_id`/role semantics. +2. Partner links are reciprocal for partner-agent households. +3. Household-scoped attributes are shared among household members. + +### Consistency invariants +1. Hard bounds are respected for numeric attributes. +2. Expression violation rates are measured and thresholded for backpressure. +3. Minor-dependent promoted agents are age-coherent on semantic-type fields. + +### Downstream invariants +1. Scenario-scoped agent retrieval returns sampled set used for network/simulation. +2. Household/NPC keys used by simulation are present and shape-consistent. +3. Sampling diagnostics are persisted/exportable for audit. + +--- + +## Glossary (Working Terms) +- **Merged spec**: base attributes + scenario `extended_attributes` used for sampling. +- **Primary adult**: first sampled adult for a household unit. +- **NPC partner/dependent**: household member represented as context, not full agent row. +- **Agent focus mode**: scenario policy for who becomes full agents (`primary_only`, `couples`, `all`). +- **Partner-correlated attribute**: attribute sampled to correlate with partner value. +- **Backpressure**: quality signal that can stop or warn before downstream execution. + +--- + +## Practical Readout +`sample` is not the heaviest compile stage, but it is not trivial. +It is where many independent contracts collide: +- merged dependency execution, +- household realization, +- name semantics, +- consistency enforcement, +- storage contract for the rest of the pipeline. + +So it is simpler than `spec + scenario` authoring, but still a high-leverage failure point. + +--- + +## Final Statement +`sample` should be treated as the execution-grade realization gate for Extropy. + +Current implementation already has substantial capability and deterministic runtime behavior. +The main gaps are contract coherence at boundaries (especially merged ordering and cross-module household/NPC key consistency), plus stronger machine-readable backpressure signals for quality acceptance. diff --git a/docs/pipeline/spec.md b/docs/pipeline/spec.md new file mode 100644 index 0000000..6374842 --- /dev/null +++ b/docs/pipeline/spec.md @@ -0,0 +1,488 @@ +# Spec Stage Reference: Population Creation and Encoding + +## Document Intent +This file is the deep reference for the `spec` stage in Extropy. + +If a team member or agent forgets prior context, this file should bring them back to working understanding of: +- what `spec` is supposed to accomplish, +- why the design exists, +- how the stage should operate in theory, +- how the current system behaves in practice, +- where the current implementation falls short, +- and what improvement directions are implied by those gaps. + +This is not a patch plan and not a code-fix checklist. It is a contract + reality map. + +## Status Update (2026-02-20) +- Spec-stage hydration now runs with `include_household=False`. +- Household config ownership is enforced at scenario stage; spec no longer does dead household hydration work. +- Naming is Faker-first at sampling/runtime with deterministic seed and geography routing; CSV remains a safety fallback path. +- Attribute names are canonicalized to snake_case during selection; ambiguous normalization collisions now fail fast. +- Fail-hard spec validation writes versioned artifacts (`*.invalid.vN.yaml`) instead of overwriting a single invalid file. + +--- + +## How To Read This Document +Use this in order: +1. `Intended Contract` for the target design truth. +2. `Current Implementation` for what the code currently does. +3. `Divergences and Mistakes` for the gap between target and reality. +4. `Improvement Directions` for strategy-level correction paths. + +This doc is intentionally verbose. It is meant to be reusable team memory. + +--- + +## Why This Stage Exists +Extropy needs to simulate populations at scale with believable heterogeneity. + +A naive approach would generate agents directly via LLM per person. That approach breaks down because: +- it is expensive and slow, +- it does not reliably enforce population-level distributions, +- it does not guarantee reproducible structure at scale, +- it cannot be audited easily for relationship consistency between attributes, +- it weakly controls outlier rates and coherence. + +The `spec` stage exists to shift intelligence from runtime to compile time: +- infer and encode population mechanics once, +- sample agents cheaply and repeatedly from that encoded blueprint, +- preserve statistical shape, dependency logic, and constraint boundaries. + +The design principle is: +- **Compile population semantics once, sample many times.** + +--- + +## Pipeline Position +Within the full Extropy pipeline, `spec` is foundational. + +High-level pipeline intent: +1. Create population and population dynamics. +2. Create event/scenario and event dynamics. +3. Build persona and network in scenario-specific context. +4. Simulate behavior and outcomes. + +`spec` is the base-population creation stage. It should produce a reusable artifact that later scenario-specific extensions can build on. + +If `spec` quality is weak, downstream stages cannot reliably recover. + +--- + +## Intended Contract + +### What `spec` should do +`spec` should output a reusable **base population encoding** as versioned YAML that defines how a valid member of the target population is generated. + +This encoding should include: +- attribute inventory (who a person can be in this population), +- attribute typing and semantics, +- sampling strategies and distributions, +- cross-attribute dependency structure, +- conditional behavior modifiers, +- consistency constraints, +- support for realistic outliers, +- metadata needed for downstream interpretation. + +The artifact should be reusable across scenarios that operate on the same base population. + +### What `spec` should not do +`spec` should not: +- generate concrete agents, +- bake in one scenario-specific behavioral rollout, +- run per-agent LLM reasoning, +- collapse population diversity into a single “average” profile, +- treat outliers as pure noise to suppress. + +### How `spec` should do it +The intended process is a compile pipeline: +1. Verify intent sufficiency. +2. Select the right attributes (coverage + relevance). +3. Hydrate each attribute with sampling semantics. +4. Resolve dependency graph and compile sampling order. +5. Validate for structural and behavioral quality. +6. Emit versioned base spec artifact. + +This process should preserve three priorities (in order from this project context): +1. realism, +2. internal consistency, +3. downstream support reliability. + +### Why this contract matters +Without explicit base encoding, all downstream steps become unstable: +- scenario extension becomes ad hoc, +- sampling quality degrades, +- persona and network logic lose grounding, +- simulation outputs become hard to trust. + +`spec` is therefore not a convenience stage. It is the system’s causal root. + +--- + +## Conceptual Model Of Population Encoding + +### Population as generative blueprint +A population is represented as a constrained generative model: +- each attribute is a variable, +- each variable has a type, +- variables are sampled or computed according to strategy, +- dependencies define valid evaluation order, +- constraints define valid output region, +- modifiers define context-conditional shifts, +- outlier mechanics define tails and rare combinations. + +### Attribute classes +At minimum, attribute selection should consider: +- universal attributes (broad demographic anchors), +- population-specific attributes (identity/work/lifestyle specifics), +- personality/behavioral attributes, +- optional context attributes where justified. + +### Sampling strategy classes +Core strategy semantics: +- `independent`: sampled directly from a distribution with no upstream dependencies. +- `derived`: deterministic formula output from dependencies; no variance for same input tuple. +- `conditional`: base distribution with dependency-conditioned shifts. + +### Scope semantics +Attribute scope shapes household-level instantiation logic later: +- `individual`: value varies per person. +- `household`: value is shared across household members. +- `partner_correlated`: value is correlated between partners. + +### Constraints semantics +Constraints should distinguish: +- hard bounds (clamping or strict validity boundaries), +- expression-level validity conditions, +- spec-level validity checks. + +### Outlier semantics +The system should treat outliers as first-class: +- some should be explicitly represented, +- others should emerge from conditional distributions, +- impossible combinations should be blocked, +- implausible combinations should be measured, not blindly suppressed. + +--- + +## Intended Base vs Scenario Boundary + +### Base population (`spec`) should own +- reusable population mechanics, +- stable demographic/identity/behavioral variable definitions, +- dependency and distribution structure, +- generalized semantic metadata for later interpretation. + +### Scenario extension should own +- scenario-specific additional attributes, +- scenario-specific activation policies, +- scenario-contextual household/relationship activation policy, +- scenario-time role policy (who is simulated agent vs NPC vs mixed). + +### Sampling should own +- deterministic instantiation from base + extension, +- enforcement of compiled dependency order, +- runtime realization under seeded randomness. + +This boundary is critical for reuse and avoiding scope creep. + +--- + +## Intended Artifact Contract + +### Primary artifact +`spec` should emit versioned base spec YAML (`population.vN.yaml`) containing: +- metadata, +- grounding summary, +- attribute list, +- compiled sampling order. + +### Metadata responsibilities +Metadata should encode: +- source description and geography, +- who the base population focuses on, +- versioning fields, +- optional downstream rendering metadata when available. + +### Attribute contract (intended) +Each attribute should fully define: +- identifier and type, +- category, +- descriptive meaning, +- scope semantics, +- semantic/identity/display metadata, +- sampling config, +- grounding quality, +- constraints. + +### Sampling-order contract (intended) +Sampling order should be: +- complete (every sampled field appears), +- dependency-safe (dependencies resolved before dependents), +- deterministic for same compiled graph. + +--- + +## Intended Quality Contract + +### Realism +The output should produce agents that look like credible members of the population, including plausible variation and nontrivial tails. + +### Internal consistency +Attribute combinations should be coherent under known relationships. Contradictory combinations should be blocked or explicitly flagged by severity. + +### Downstream support +Spec output must be machine-usable and semantically useful to: +- scenario extension, +- sampler, +- persona rendering, +- network generation, +- simulation reasoning. + +### Quality interpretation rule +Structural validity alone is not enough. A spec can be syntactically valid and still behaviorally low-quality. + +--- + +## Intended Backpressure Philosophy (Contract-Level) + +`spec` should produce enough measurable evidence so downstream stages can decide whether to proceed. + +At contract level, checks should separate: +- `block` class contradictions (impossible/incoherent), +- `warn` class plausibility concerns (rare but possible, suspicious drift), +- informational diagnostics. + +Backpressure should be explicit and machine-readable, not only human prompt warnings. + +--- + +## Current Implementation (As-Is) + +This section describes what the current codebase does now. + +### Entrypoint behavior +`extropy spec` command flow: +1. Resolve study folder and versioned output path. +2. Run sufficiency check. +3. Run attribute selection. +4. Run split hydration pipeline. +5. Run constraint binding and sampling-order compilation. +6. Build `PopulationSpec`. +7. Run spec validation. +8. Save YAML. + +### Sufficiency behavior in code +Current sufficiency stage: +- uses fast LLM call, +- infers geography, +- can ask structured clarification questions. + +### Selection behavior in code +Current selector: +- uses reasoning LLM call with a strict JSON schema, +- asks model to assign strategy, scope, dependencies, and optional semantic metadata, +- injects Big Five traits when recommended and missing, +- may inject `country` attribute for multi-country geographies. + +### Hydration behavior in code +Hydration is split: +- 2a independent distributions via agentic research, +- 2b derived formulas via reasoning call, +- 2c conditional base distributions via agentic research, +- 2d conditional modifiers via agentic research, +- 2e household config via agentic research (conditional trigger), +- 2f name config via agentic research (skips US population heuristic). + +Hydration includes fail-fast validation callbacks and retry loops. + +### Binding behavior in code +Binding step: +- converts hydrated attributes to final `AttributeSpec`, +- filters unknown dependencies with warnings, +- infers dependencies from formulas and conditions, +- computes topological sort for sampling order. + +### Build behavior in code +Spec build: +- creates `SpecMeta`, +- computes grounding summary, +- assembles final `PopulationSpec`. + +### Validation behavior in code +Validation currently combines: +- structural checks (errors and some warnings), +- semantic checks (warnings). + +Validation catches many shape and compatibility issues, but does not fully guarantee behavioral realism quality. + +--- + +## Current Runtime Coupling With Scenario And Sampling + +### Current extension model +Scenario flow currently performs additive extension: +- discovers and hydrates new attributes with base context, +- stores them in scenario as `extended_attributes`. + +### Current household ownership in runtime +Practical runtime ownership today: +- base `spec` pipeline can research household config, +- `extropy spec` explicitly ignores/stubs household config for base output, +- scenario stores household config and agent focus mode, +- sampler consumes scenario-level household config and focus mode. + +### Current merge model before sampling +`sample` currently: +- merges base + extended attributes, +- appends extended names onto base sampling order if missing, +- samples with scenario-provided household/focus settings. + +This is the effective behavior today. + +--- + +## What Current Implementation Already Gets Right + +### Architectural direction +- Compile-time modeling + fast runtime sampling is the correct high-level shape. + +### Typed modeling surface +- Rich Pydantic models exist for attributes, distributions, constraints, and metadata. + +### Dependency-aware compilation +- Topological ordering and cycle detection are implemented. + +### Validation scaffolding +- There is a meaningful validation layer both at LLM-response time and pre-sampling. + +### Reuse path +- Base specs are versioned and reusable as artifacts. + +--- + +## Divergences, Mistakes, and Risk Areas + +### 1) Prompt-heavy contract enforcement +Many intended quality guarantees are represented as prompt instructions rather than strict enforceable contracts. + +Risk: +- quality can drift with model behavior while still passing structural checks. + +### 2) Silent coercion and fallback behavior +Parsing and binding include permissive fallback logic in some places (defaults, filtered deps). + +Risk: +- malformed or weak outputs can degrade silently into “valid enough” artifacts. + +### 3) Dependency mutation side effects +Unknown dependencies can be removed with warnings. Inferred dependencies can be auto-added. + +Risk: +- resulting compiled behavior differs from model’s original intent without a hard failure. + +### 4) Extension/sampling order assumptions +Extension merges are currently append-oriented in CLI flow. + +Risk: +- cross-layer dependency semantics can be fragile if extension expectations grow. + +### 5) Household ownership ambiguity in build narrative +Build path can hydrate household config during base flow, but final base stage ignores it and runtime ownership shifts to scenario. + +Risk: +- conceptual confusion across teams and agents unless boundary is explicit. + +### 6) Category/value normalization fragility +Token shape mismatches in categorical ecosystems remain a real risk (typos, underscore/space/case variants). + +Risk: +- modifier conditions and downstream interpretation can diverge from intended semantics. + +### 7) Outlier contract debt +Outliers can emerge today, but there is no explicit first-class outlier intent contract with measurable acceptance semantics. + +Risk: +- too-flat populations or chaotic tails may both pass structural checks. + +### 8) Structural-validity bias +Current checks are much stronger on syntax/typing than on distributional realism and joint plausibility at scale. + +Risk: +- valid YAML, weak population reality. + +--- + +## How It Can Be Made Better (Direction, Not Patch Plan) + +This section captures strategic direction only. + +### Make quality explicit and testable +Define and emit explicit quality contract outcomes, not only passive warnings. + +### Separate impossibility from implausibility formally +Use severity-aware rule semantics so contradictions are blocked and plausibility drift is visible with calibrated tolerance. + +### Elevate outlier intent to explicit contract +Treat outlier representation as a declared quality dimension, not only an emergent property. + +### Tighten stage ownership language +Document and enforce base vs scenario vs sampling ownership boundaries clearly. + +### Strengthen normalization discipline +Normalize category/token interpretation consistently across selection, hydration, validation, and sampling semantics. + +### Upgrade backpressure from “advice” to “gateable evidence” +Produce machine-readable quality evidence suitable for automated stage gating. + +--- + +## Downstream Dependency Map (What `spec` Must Support) + +### Scenario +Needs base attribute semantics robust enough to support scenario-specific extension and event relevance targeting. + +### Sample +Needs complete, consistent sampling definitions and valid dependency order for deterministic agent generation. + +### Persona +Needs interpretable attribute descriptions and formatting metadata to render believable identities. + +### Network +Needs coherent attributes with social relevance and reliable categorical structure. + +### Simulate +Needs internally coherent agent state primitives that do not collapse under behavioral reasoning. + +### Estimate +Needs stable assumptions about population complexity and downstream workload implications. + +### Query/Results/Chat +Need consistent field semantics so analytics and conversational explanations remain meaningful. + +--- + +## Glossary (Working Terms) + +- **Base population**: reusable population encoding from `spec`. +- **Extended population**: base + scenario-specific attribute layer. +- **Compile-time**: expensive inference stage that writes artifacts. +- **Runtime sampling**: fast generation from compiled artifacts. +- **Outlier**: rare but plausible instance/combination. +- **Impossible**: logically contradictory combination. +- **Implausible**: possible but suspicious/unlikely combination. +- **Backpressure**: quality signal that can block or warn before downstream continuation. + +--- + +## Practical Readout +If you only remember five things from this file: +1. `spec` is a compile stage, not a generation stage. +2. Its job is reusable population semantics, not scenario-specific behavior rollout. +3. Realism + consistency + downstream support are the quality triad. +4. Current implementation has strong structure but weaker explicit behavioral contracts. +5. The main maturity gap is enforceable quality semantics, not missing model surface area. + +--- + +## Final Statement +The current `spec` system is substantial and directionally correct, but it is not yet equivalent to a fully explicit, contract-driven population compiler. It currently behaves as a strong structured generator with partial contract enforcement. To meet the intended standard, quality intent and cross-stage boundaries must be made explicit enough to serve as reliable backpressure for the rest of the pipeline. diff --git a/skills/extropy/SCENARIOS.md b/skills/extropy/SCENARIOS.md index 30c6f80..0e2f5e5 100644 --- a/skills/extropy/SCENARIOS.md +++ b/skills/extropy/SCENARIOS.md @@ -14,7 +14,7 @@ Not a survey. Not a poll. A simulation of collective human behavior. Any country or region. Examples: - US (bundled SSA + Census name data) -- Japan, India, Brazil, UK, Germany (provide NameConfig or let LLM research) +- Japan, India, Brazil, UK, Germany (Faker locale routing + CSV fallback handles naming) - Multi-region (agents distributed across cities, states, countries) Extropy doesn't care about geography - it cares about attributes and distributions. Define `state`, `city`, `region` as attributes with your desired distribution. @@ -308,7 +308,7 @@ Use this skill when users ask: | Question | Answer | |----------|--------| -| Can I simulate non-US populations? | Yes, any country with appropriate NameConfig | +| Can I simulate non-US populations? | Yes, any country with Faker locale routing + CSV fallback | | Can I model families? | Yes, `household_mode: true` with configurable `agent_focus` | | Can I have evolving events? | Yes, use timeline with multiple events | | Can I get open-ended responses? | Yes, `type: open_ended` outcomes | diff --git a/test-v2-e2e/extended.yaml b/test-v2-e2e/extended.yaml index fc19631..63e908c 100644 --- a/test-v2-e2e/extended.yaml +++ b/test-v2-e2e/extended.yaml @@ -80,7 +80,6 @@ meta: label: high_school adult_stage_label: adult avg_household_size: 3.2 - name_config: null grounding: overall: medium sources_count: 308 diff --git a/test-v2-e2e/spec.yaml b/test-v2-e2e/spec.yaml index def0ee8..c2938c3 100644 --- a/test-v2-e2e/spec.yaml +++ b/test-v2-e2e/spec.yaml @@ -87,7 +87,6 @@ meta: label: high_school adult_stage_label: adult avg_household_size: 3.2 - name_config: null grounding: overall: medium sources_count: 172 diff --git a/tests/test_household_sampling.py b/tests/test_household_sampling.py index 115d727..4b89190 100644 --- a/tests/test_household_sampling.py +++ b/tests/test_household_sampling.py @@ -30,14 +30,10 @@ _DEFAULT_CONFIG = HouseholdConfig() -def _make_household_spec( - size: int = 100, agent_focus: str | None = "couples" -) -> PopulationSpec: +def _make_household_spec(size: int = 100) -> PopulationSpec: """Create a minimal spec with household-scoped attributes.""" return PopulationSpec( - meta=SpecMeta( - description="Test household spec", size=size, agent_focus=agent_focus - ), + meta=SpecMeta(description="Test household spec", size=size), grounding=GroundingSummary( overall="medium", sources_count=1, @@ -114,7 +110,6 @@ def _make_household_spec( category="universal", description="Education", scope="partner_correlated", # Correlated between partners - correlation_rate=0.6, # ~60% same education sampling=SamplingConfig( strategy="independent", distribution=CategoricalDistribution( @@ -222,7 +217,7 @@ def test_correlate_age(self): """Age correlation uses gaussian offset from HouseholdConfig.""" rng = random.Random(42) partner_age = correlate_partner_attribute( - "age", "int", 35, None, rng, _DEFAULT_CONFIG + "age", "int", 35, rng, _DEFAULT_CONFIG ) assert isinstance(partner_age, int) assert partner_age >= 18 @@ -237,7 +232,6 @@ def test_correlate_race_same_rate(self): "race_ethnicity", "categorical", "white", - None, # Uses per-group rates from config rng, _DEFAULT_CONFIG, available_options=["white", "black", "hispanic"], @@ -249,7 +243,7 @@ def test_correlate_race_same_rate(self): assert 0.80 < rate < 0.97, f"Same-race rate {rate:.2f} outside expected range" def test_correlate_education_assortative(self): - """Education uses explicit correlation_rate.""" + """Education uses household assortative-mating defaults.""" rng = random.Random(42) same_count = 0 trials = 500 @@ -258,7 +252,6 @@ def test_correlate_education_assortative(self): "education_level", "categorical", "bachelors", - 0.6, # Explicit correlation rate rng, _DEFAULT_CONFIG, available_options=["high_school", "bachelors", "masters", "doctorate"], @@ -266,11 +259,11 @@ def test_correlate_education_assortative(self): if result == "bachelors": same_count += 1 rate = same_count / trials - # Expect ~60% same education - assert 0.50 < rate < 0.75, f"Assortative rate {rate:.2f} outside expected range" + # Expect moderate assortative matching from default config table + assert 0.50 < rate < 0.70, f"Assortative rate {rate:.2f} outside expected range" def test_correlate_with_default_rate(self): - """Unknown attributes use default_same_group_rate when no explicit rate.""" + """Unknown attributes use default_same_group_rate.""" rng = random.Random(42) same_count = 0 trials = 500 @@ -279,7 +272,6 @@ def test_correlate_with_default_rate(self): "some_attribute", "categorical", "value_a", - None, # No explicit rate, uses default rng, _DEFAULT_CONFIG, available_options=["value_a", "value_b", "value_c"], @@ -292,9 +284,7 @@ def test_correlate_with_default_rate(self): def test_generate_dependents_no_kids(self): rng = random.Random(42) - deps = generate_dependents( - HouseholdType.COUPLE, 2, 2, 40, rng, _DEFAULT_CONFIG, name_config=None - ) + deps = generate_dependents(HouseholdType.COUPLE, 2, 2, 40, rng, _DEFAULT_CONFIG) assert len(deps) == 0 def test_generate_dependents_with_kids(self): @@ -306,7 +296,6 @@ def test_generate_dependents_with_kids(self): 40, rng, _DEFAULT_CONFIG, - name_config=None, ) assert len(deps) == 2 for d in deps: @@ -323,7 +312,6 @@ def test_generate_dependents_multi_generational(self): 45, rng, _DEFAULT_CONFIG, - name_config=None, ) assert len(deps) == 2 relationships = [d.relationship for d in deps] @@ -370,6 +358,38 @@ def test_household_scoped_attrs_shared(self): f"{agent['state']} != {partner['state']}" ) + def test_household_members_share_surname(self): + spec = _make_household_spec(size=120) + result = sample_population(spec, count=120, seed=7) + by_household: dict[str, list[dict]] = {} + for agent in result.agents: + hid = agent.get("household_id") + if isinstance(hid, str): + by_household.setdefault(hid, []).append(agent) + + for members in by_household.values(): + primary = next( + (m for m in members if m.get("household_role") == "adult_primary"), + members[0], + ) + primary_last = primary.get("last_name") + if not primary_last: + continue + + for member in members: + assert member.get("last_name") == primary_last + + partner_npc = primary.get("partner_npc") + if isinstance(partner_npc, dict) and partner_npc.get("last_name"): + assert partner_npc.get("last_name") == primary_last + + for dep in primary.get("dependents", []) or []: + if not isinstance(dep, dict): + continue + dep_name = dep.get("name") + if isinstance(dep_name, str) and dep_name.strip(): + assert dep_name.split()[-1] == primary_last + def test_dependent_count_matches(self): spec = _make_household_spec(size=50) result = sample_population(spec, count=50, seed=42) @@ -457,7 +477,7 @@ def test_country_correlation(self): for _ in range(total): primary_country = rng.choice(countries) partner_country = correlate_partner_attribute( - "country", "categorical", primary_country, None, rng, config, countries + "country", "categorical", primary_country, rng, config, countries ) if partner_country == primary_country: same_country += 1 diff --git a/tests/test_name_generator.py b/tests/test_name_generator.py index c557fd1..75b39c4 100644 --- a/tests/test_name_generator.py +++ b/tests/test_name_generator.py @@ -5,7 +5,6 @@ from extropy.population.names import generate_name from extropy.population.names.generator import age_to_birth_decade -from extropy.core.models.population import NameConfig, NameEntry class TestGenerateName: @@ -27,34 +26,28 @@ def test_female_name(self): assert isinstance(first, str) def test_hispanic_surname(self): - """Hispanic ethnicity produces a Hispanic surname.""" - # Run several seeds — at least one should be recognizably Hispanic - surnames = set() + """Hispanic routing should differ from default white routing.""" + hispanic_surnames = set() + white_surnames = set() for s in range(20): _, last = generate_name(ethnicity="hispanic", seed=s) - surnames.add(last) - hispanic_names = { - "Garcia", - "Rodriguez", - "Martinez", - "Hernandez", - "Lopez", - "Gonzalez", - "Perez", - "Sanchez", - "Ramirez", - "Torres", - } - assert surnames & hispanic_names, f"Expected Hispanic surnames, got {surnames}" + hispanic_surnames.add(last) + _, white_last = generate_name(ethnicity="white", seed=s) + white_surnames.add(white_last) + assert len(hispanic_surnames) > 1 + assert hispanic_surnames != white_surnames def test_asian_surname(self): - """Asian ethnicity produces an Asian surname.""" - surnames = set() + """Asian routing should differ from default white routing.""" + asian_surnames = set() + white_surnames = set() for s in range(20): _, last = generate_name(ethnicity="asian", seed=s) - surnames.add(last) - asian_names = {"Kim", "Lee", "Park", "Nguyen", "Chen", "Wang", "Patel", "Singh"} - assert surnames & asian_names, f"Expected Asian surnames, got {surnames}" + asian_surnames.add(last) + _, white_last = generate_name(ethnicity="white", seed=s) + white_surnames.add(white_last) + assert len(asian_surnames) > 1 + assert asian_surnames != white_surnames def test_black_surname(self): """Black ethnicity produces surnames from the black distribution.""" @@ -129,84 +122,25 @@ def test_ethnicity_aliases(self): first, last = generate_name(ethnicity=alias, seed=0) assert isinstance(first, str) - -class TestNameConfig: - """Test generate_name() with NameConfig-based generation.""" - - def _make_config(self) -> NameConfig: - return NameConfig( - male_first_names=[ - NameEntry(name="Haruto", weight=5.0), - NameEntry(name="Ren", weight=3.0), - NameEntry(name="Sota", weight=2.0), - ], - female_first_names=[ - NameEntry(name="Yui", weight=5.0), - NameEntry(name="Hana", weight=3.0), - NameEntry(name="Aoi", weight=2.0), - ], - last_names=[ - NameEntry(name="Tanaka", weight=5.0), - NameEntry(name="Suzuki", weight=4.0), - NameEntry(name="Sato", weight=3.0), - ], - ) - - def test_config_male_draws_from_config(self): - config = self._make_config() - names = set() - for s in range(20): - first, last = generate_name(gender="male", seed=s, name_config=config) - names.add(first) - expected = {"Haruto", "Ren", "Sota"} - assert names.issubset(expected), f"Got unexpected names: {names - expected}" - assert len(names) > 1 - - def test_config_female_draws_from_config(self): - config = self._make_config() - names = set() - for s in range(20): - first, last = generate_name(gender="female", seed=s, name_config=config) - names.add(first) - expected = {"Yui", "Hana", "Aoi"} - assert names.issubset(expected), f"Got unexpected names: {names - expected}" - assert len(names) > 1 - - def test_config_last_names_from_config(self): - config = self._make_config() - surnames = set() - for s in range(20): - _, last = generate_name(seed=s, name_config=config) - surnames.add(last) - expected = {"Tanaka", "Suzuki", "Sato"} - assert surnames.issubset(expected), ( - f"Got unexpected surnames: {surnames - expected}" - ) - assert len(surnames) > 1 - - def test_config_seeded_reproducibility(self): - config = self._make_config() - a1, b1 = generate_name(gender="male", seed=42, name_config=config) - a2, b2 = generate_name(gender="male", seed=42, name_config=config) - assert a1 == a2 - assert b1 == b2 - - def test_empty_config_falls_back_to_csv(self): - """NameConfig with empty lists falls back to CSV names.""" - empty_config = NameConfig() - first, last = generate_name(gender="male", seed=42, name_config=empty_config) - # Should still produce valid names from CSV fallback - assert isinstance(first, str) and len(first) > 0 - assert isinstance(last, str) and len(last) > 0 - - def test_none_config_uses_csv(self): - """name_config=None is identical to no-config behavior.""" - first1, last1 = generate_name(gender="female", ethnicity="white", seed=99) - first2, last2 = generate_name( - gender="female", ethnicity="white", seed=99, name_config=None - ) - assert first1 == first2 - assert last1 == last2 + def test_country_aliases_resolve_consistently(self): + """Country aliases/codes should resolve to the same locale pool.""" + by_name = generate_name(gender="male", country="India", seed=17) + by_alpha2 = generate_name(gender="male", country="IN", seed=17) + by_alpha3 = generate_name(gender="male", country="IND", seed=17) + assert by_name == by_alpha2 == by_alpha3 + + def test_region_aliases_resolve_consistently(self): + """Region spelling variants should normalize to a common region scope.""" + full = generate_name(gender="female", country="Southeast Asia", seed=31) + short = generate_name(gender="female", country="SE Asia", seed=31) + spaced = generate_name(gender="female", country="South East Asia", seed=31) + assert full == short == spaced + + def test_us_city_hint_resolves_to_us_scope(self): + """Subnational US hints (city + state) should route to US locale scope.""" + us_name = generate_name(gender="male", country="US", seed=9) + city_name = generate_name(gender="male", country="Austin, TX", seed=9) + assert city_name == us_name class TestAgeToBirthDecade: From 75545072f71fd73395632daddea058426dcfb71a Mon Sep 17 00:00:00 2001 From: DeveshParagiri Date: Fri, 20 Feb 2026 01:03:06 -0500 Subject: [PATCH 04/20] tests: updated --- docs/pipeline/persona.md | 533 +++++++++++++++ docs/pipeline/scenario.md | 608 ++++++++++++++++++ extropy/cli/commands/network.py | 7 +- extropy/cli/commands/persona.py | 51 +- extropy/cli/commands/sample.py | 177 ++++- extropy/cli/commands/validate.py | 122 +++- extropy/cli/utils.py | 63 ++ extropy/core/models/sampling.py | 6 + extropy/core/models/scenario.py | 132 +++- extropy/population/names/__init__.py | 3 +- extropy/population/names/data/first_names.csv | 416 +++++++----- extropy/population/names/data/last_names.csv | 200 +++--- extropy/population/network/__init__.py | 2 + extropy/population/network/config.py | 21 + .../population/network/config_generator.py | 128 +++- extropy/population/network/generator.py | 290 +++++++-- extropy/population/persona/__init__.py | 3 + extropy/population/persona/renderer.py | 58 +- extropy/population/persona/validator.py | 195 ++++++ extropy/population/sampler/modifiers.py | 12 +- .../population/spec_builder/sufficiency.py | 22 +- extropy/population/validator/semantic.py | 73 +++ extropy/scenario/__init__.py | 4 +- extropy/scenario/compiler.py | 59 +- extropy/scenario/exposure.py | 29 +- extropy/scenario/interaction.py | 83 +-- extropy/scenario/parser.py | 9 +- extropy/scenario/sampling_semantics.py | 277 ++++++++ extropy/scenario/sufficiency.py | 147 ++++- extropy/scenario/validator.py | 363 ++++++++++- tests/test_agent_focus.py | 5 +- tests/test_cli.py | 1 - tests/test_compiler.py | 6 - tests/test_conversations.py | 3 - tests/test_engine.py | 3 - tests/test_estimator.py | 3 - tests/test_integration_timestep.py | 3 - tests/test_network.py | 81 +++ tests/test_persona_validator.py | 86 +++ tests/test_propagation.py | 5 - tests/test_reasoning_execution.py | 5 - tests/test_reasoning_prompts.py | 4 - tests/test_sampler.py | 26 + tests/test_scenario.py | 43 +- tests/test_scenario_sufficiency.py | 75 +++ tests/test_scenario_validator.py | 112 +++- uv.lock | 34 + 47 files changed, 3997 insertions(+), 591 deletions(-) create mode 100644 docs/pipeline/persona.md create mode 100644 docs/pipeline/scenario.md create mode 100644 extropy/population/persona/validator.py create mode 100644 extropy/scenario/sampling_semantics.py create mode 100644 tests/test_persona_validator.py create mode 100644 tests/test_scenario_sufficiency.py diff --git a/docs/pipeline/persona.md b/docs/pipeline/persona.md new file mode 100644 index 0000000..007ed45 --- /dev/null +++ b/docs/pipeline/persona.md @@ -0,0 +1,533 @@ +# Persona Stage Reference: Persona Compilation and Agent Embodiment + +## Document Intent +This file is the deep reference for the `persona` stage in Extropy. + +It is designed to be reusable team memory for: +- what `persona` is supposed to accomplish, +- how the stage should operate in theory, +- what the code currently does, +- where current behavior diverges from intended quality goals, +- and what contract-level checks should exist for backpressure. + +This is not a patch plan. It is a contract + current-state map. + +## Status Update (2026-02-20) +- `extropy validate` now routes persona files (`persona.vN.yaml`) through persona-specific validation. +- Persona validation now checks: + - merged-attribute treatment coverage + - group membership completeness/uniqueness + - boolean/categorical/relative/concrete phrasing completeness + - intro-template attribute reference validity +- Renderer behavior now includes: + - mixed relative+concrete fallback for float-like relative attributes + - strict skip of duplicate “Who I Am” group rendering + - deterministic boolean phrase sanitation for token-like outputs +- Fail-hard persona generation/validation now emits versioned invalid artifacts. + +--- + +## How To Read This Document +Use this sequence: +1. `Intended Contract` for design truth. +2. `Current Implementation (As-Is)` for code truth. +3. `Divergences, Mistakes, and Risk Areas` for gap analysis. +4. `Dependency Map` for downstream implications. + +The document is intentionally verbose so someone new can recover context quickly. + +--- + +## Why This Stage Exists +The simulation does not reason directly over raw structured agent rows. +It reasons over natural-language personas. + +If persona construction is weak, the simulation degrades even when sampling/network are strong: +- important traits are omitted or buried, +- phrasing is unnatural or token-like, +- household context is lost, +- trait salience for decision-making is unclear, +- LLM reasoning becomes noisy and inconsistent. + +`persona` exists to compile one reusable rendering contract: +- generated once for a scenario’s merged attribute set, +- then applied deterministically to every agent at runtime (no per-agent persona LLM call). + +Design principle: +- **Compile persona language once, render many agents deterministically.** + +--- + +## Pipeline Position +Pipeline context: +1. `spec` compiles base population mechanics. +2. `scenario` extends population + event dynamics. +3. `persona` compiles rendering semantics for merged attributes. +4. `sample` instantiates agents. +5. `network` builds graph. +6. `simulate` pre-generates and uses personas for reasoning. + +In current operational flow, persona is treated as a prerequisite for both `sample` and `simulate`. + +--- + +## Intended Contract + +### What `persona` should do +`persona` should emit a versioned persona configuration that defines: +1. intro/narrative framing, +2. attribute treatment mode (concrete vs relative), +3. attribute grouping and ordering, +4. phrasing templates for boolean/categorical/relative/concrete attributes, +5. optional population statistics for relative positioning. + +At runtime, this config should produce: +- consistent first-person personas, +- stable, deterministic rendering, +- high coverage of relevant attributes, +- decision-focused salience where scenario requests it. + +### What `persona` should not do +`persona` should not: +- run per-agent LLM calls during simulation, +- mutate agent values, +- decide network structure, +- invent scenario dynamics, +- depend on hidden downstream defaults for core rendering semantics. + +### How `persona` should do it +Intended compile pattern: +1. ingest merged population attributes (base + scenario extension), +2. classify rendering treatment and groups, +3. generate phrasing maps by type, +4. validate coverage and phrase quality constraints, +5. optionally compute population stats from sampled agents, +6. emit versioned `persona.vN.yaml`. + +### Why this contract matters +Persona is the language interface between structured agent states and LLM reasoning. +If this interface is low quality: +- behavioral realism drops, +- contradictions increase, +- outcome extraction quality declines, +- simulation variance becomes harder to trust. + +--- + +## Conceptual Model Of Persona Compilation + +### Persona config as rendering compiler +Conceptually, persona stage compiles: +- **input**: attribute schema + optional empirical sampled stats, +- **output**: deterministic rendering program (`PersonaConfig`), +- **runtime**: pure string rendering over each agent dictionary. + +### Two rendering layers +Persona rendering has two layers: +1. narrative framing (`intro_template` + household section), +2. structured trait expression (grouped phrases, with optional decision-salience section). + +### Treatment semantics +Each attribute is assigned a treatment: +- `concrete`: preserve actual value semantics, +- `relative`: express position relative to population stats (z-score buckets). + +### Salience semantics +Scenario can provide `decision_relevant_attributes`. +Runtime renderer should surface these before other groups to improve reasoning focus. + +### Determinism semantics +After config generation, per-agent persona rendering should be: +- template-driven, +- non-LLM, +- reproducible from agent state + config. + +--- + +## Intended Ownership Boundary + +### `persona` should own +- linguistic embodiment of attributes, +- grouping and ordering for readability + reasoning salience, +- phrase-level normalization for categorical/boolean/null states, +- optional relative-position computation using population stats. + +### `scenario` should own +- which attributes matter for decision salience (`decision_relevant_attributes`), +- household activation policy and agent scope semantics upstream. + +### `sample` should own +- concrete agent instantiation and household/NPC realization. + +### `simulate` should own +- usage of persona text in reasoning pipeline, +- no semantic reinterpretation of persona config contract. + +--- + +## Intended Artifact Contract + +### Primary artifact +Persona stage should output: +- `scenario//persona.vN.yaml` + +### Artifact sections (intended stable envelope) +- `population_description` +- `created_at` +- `intro_template` +- `treatments` +- `groups` +- `phrasings`: + - `boolean` + - `categorical` + - `relative` + - `concrete` +- `population_stats` + +### Coverage contract (intended) +At contract level: +1. every attribute should have exactly one treatment, +2. group membership should be complete and non-contradictory, +3. phrasing maps should cover all rendered categorical options, +4. relative phrasings should map each relative attribute to all z-buckets, +5. concrete templates should be syntactically valid for substitution. + +### Quality contract for phrasing +Phrasings should: +- be first-person, +- avoid raw enum-token leakage where possible, +- handle explicit null/not-applicable categorical states naturally, +- avoid awkward boolean prose in intro templates. + +--- + +## Intended Quality Contract + +### Realism +Persona should sound like a plausible person, not raw table serialization. + +### Internal consistency +Rendered sections should not duplicate or contradict one another: +- intro attributes should not be repeated blindly in groups, +- decision-salient attributes should be emphasized once and cleanly. + +### Downstream support +Persona output should support simulation reasoning quality directly: +- preserve critical trait signal, +- keep text compact enough for context efficiency, +- keep language clear enough for stable extraction. + +### Robustness to token variation +Categorical rendering should be robust to minor token formatting differences (`_`, space, hyphen forms). + +--- + +## Intended Backpressure Philosophy (Contract-Level) +Persona stage should provide machine-checkable evidence that rendering quality is acceptable. + +Backpressure classes should distinguish: +- blocking structural failures (coverage missing, parse errors), +- plausibility/readability warnings, +- informational diagnostics. + +Without this, low-quality persona configs can silently pass and degrade simulation behavior. + +--- + +## Current Implementation (As-Is) + +This section describes what code currently does. + +### Persona CLI behavior +`extropy persona` currently: +1. resolves scenario, +2. loads scenario spec + base population, +3. merges scenario `extended_attributes` into base attributes, +4. builds a merged `PopulationSpec`, +5. runs persona config generation, +6. saves versioned persona YAML. + +Primary file: +- `extropy/cli/commands/persona.py` + +### Merge behavior in persona command +Merge behavior is attribute-list merge: +- base attributes + scenario extended attributes. + +Sampling order is inherited from base spec and not recomputed for persona (persona generator does not rely on sampling order for core behavior). + +### Generation pipeline behavior +Generator orchestrates 5 LLM-assisted steps: +1. structure (`intro_template`, treatments, groups), +2. boolean phrasings, +3. categorical phrasings, +4. relative phrasings, +5. concrete templates. + +Primary file: +- `extropy/population/persona/generator.py` + +### Strongest built-in guardrail today +Categorical phrasing generation has explicit validation+retry behavior: +- checks per-attribute option coverage, +- normalizes minor token variants, +- retries up to 3 attempts with targeted feedback. + +This is the most explicit quality-enforcement logic in persona generation currently. + +### Treatment and grouping behavior in code +Treatment/group assignment is prompt-driven and parsed into: +- `AttributeTreatment`, +- `AttributeGroup`. + +Current code does not run a dedicated cross-field validator that enforces strict one-to-one treatment/group coverage guarantees beyond what generator outputs. + +### Stats behavior in code +Persona command tries to load sampled agents from study DB: +- if agents exist for scenario, compute population stats at generation time, +- else stats remain empty in config initially. + +Simulation runtime then backfills stats if persona config exists but has empty stats. + +Primary files: +- `extropy/cli/commands/persona.py` +- `extropy/population/persona/stats.py` +- `extropy/simulation/engine.py` + +### Renderer behavior in code +Renderer is deterministic and non-LLM. +Key runtime behavior: +- renders intro from `intro_template`, +- renders household section from `partner_npc`/`dependents`/`partner_id`, +- extracts intro attributes and avoids duplicate rendering in groups, +- optionally renders "Most Relevant to This Decision" section using scenario salience list, +- renders remaining groups in configured order. + +Primary file: +- `extropy/population/persona/renderer.py` + +### Categorical null-state behavior +Renderer supports nuanced categorical null handling: +- `null_options`, +- `null_phrase`, +- fallback phrase, +- token normalization. + +This reduces "raw enum token" artifacts in persona text for not-applicable states. + +### Relative rendering behavior +Relative phrasing uses z-score buckets from `population_stats`. +If stats are unavailable for an attribute, renderer defaults to the "average" phrase label. + +### Simulation integration behavior +Simulation engine pre-generates persona text for all agents at initialization. +It passes scenario `decision_relevant_attributes` into persona generation path for salience. + +Primary files: +- `extropy/simulation/engine.py` +- `extropy/simulation/persona.py` + +### Legacy fallback behavior still present +Simulation persona module still contains legacy hybrid path: +- narrative + structured characteristics list fallback if no `PersonaConfig`. + +In current CLI flow, persona config is usually required, but legacy path remains in runtime code for compatibility. + +### CLI prerequisites and ordering behavior +Current CLI enforces persona existence as pre-flight check in: +- `sample` +- `simulate` + +So practical command order is strongly constrained around persona generation. + +--- + +## Current Runtime Coupling With Other Stages + +### Coupling with `scenario` +Persona command uses scenario to: +- resolve merged attribute surface (via `extended_attributes`), +- indirectly receive salience contract (`decision_relevant_attributes`) consumed at simulation runtime. + +### Coupling with `sample` +Even though persona can generate without sampled agents, `sample` currently requires persona config to exist first. +This creates an operational dependency where persona is treated as upstream of sampling. + +### Coupling with `simulate` +Simulation requires persona config and uses it to: +- pre-generate per-agent personas, +- improve salience ordering with scenario outcome hints, +- inject text into reasoning prompts. + +### Coupling with population models +Renderer formatting behavior can depend on attribute `display_format` metadata from population spec. +Runtime path currently uses loaded population spec for display-format mapping. + +--- + +## What Current Implementation Already Gets Right + +### One-time compile, deterministic render model +Core architecture is sound: +- expensive LLM work done once, +- cheap deterministic rendering at runtime. + +### Strong typed config surface +Persona config models are structured and explicit, reducing ad hoc rendering logic. + +### Practical robustness in categorical phrasing +Token normalization + option coverage retries are meaningful quality controls. + +### Household-aware rendering +Renderer has dedicated household section logic and handles partner/NPC/dependent variants. + +### Scenario-driven salience support +Runtime path can foreground decision-relevant attributes from scenario outcomes. + +--- + +## Divergences, Mistakes, and Risk Areas + +### 1) `--preview` option is currently not functionally wired +CLI exposes `--preview/--no-preview`, but current command flow does not implement a distinct preview gate behavior tied to that flag in generation path. + +### 2) No dedicated persona validation command/path +There is no first-class persona validator analogous to spec/scenario validators. +Quality assurance is mostly schema parsing + generator-local checks. + +### 3) Uneven enforcement across phrasing types +Categorical has explicit retry/coverage validation. +Other phrasing classes rely more on one-shot generation and schema shape. + +### 4) Treatment/group coverage is prompt-heavy +Current flow does not centrally enforce strict invariants such as: +- every merged attribute appears exactly once in treatments, +- every treated attribute appears in exactly one group, +- no unknown attributes in groups. + +### 5) Operational ordering rigidity +`sample` requires persona config as a prerequisite even though persona stats can be backfilled later. +This may be intentional for pipeline discipline, but it couples stages more tightly than pure data dependency requires. + +### 6) Runtime fallback path complexity +Legacy and new persona paths coexist in simulation module. +Dual paths increase behavior surface area and potential drift. + +### 7) Auto persona config fallback path in simulation is legacy-shaped +When explicit persona path is absent, simulation tries `pop_path.with_suffix(".persona.yaml")`. +Current study structure centers scenario-scoped `persona.vN.yaml`, so this fallback is not the primary source-of-truth pattern. + +### 8) Display-format mapping scope mismatch risk +Runtime display-format map is derived from loaded population spec path. +If that surface does not fully reflect merged extension metadata, intro formatting for extension fields can degrade. + +### 9) Prompt-dependent naturalness +Natural language quality still depends heavily on LLM outputs. +Backpressure for "sounds natural, not templated token noise" is not formalized as explicit quality metrics. + +--- + +## How It Can Be Made Better (Direction, Not Patch Plan) + +### Add contract-level persona validation +Establish explicit checks for: +- treatment coverage, +- group coverage, +- phrasing completeness, +- unknown attribute references, +- duplicate mappings. + +### Formalize quality bands for phrase realism +Add machine-checkable heuristics for low-quality phrasing patterns (raw token leakage, template artifacts). + +### Unify runtime paths +Reduce duality between legacy fallback and new config-driven rendering where possible. + +### Clarify stage dependency rationale +Document why sample requires persona pre-flight and what guarantees that provides. + +### Strengthen deterministic behavior guarantees +Define and enforce deterministic rendering invariants independent of LLM generation variance. + +--- + +## Downstream Dependency Map (What `persona` Must Support) + +### `sample` +Current operational dependency: +- persona config must exist before sampling command proceeds. + +### `simulate` +Hard runtime dependency: +- persona config exists, +- persona texts pre-generated for all agents, +- salience-aware rendering available. + +### `results` / `query` / `chat` +Indirect dependency: +- quality of persona text influences reasoning trajectories and therefore downstream outputs. + +### `scenario` +Provides: +- merged extension context via extended attributes, +- decision salience hints consumed by persona rendering at runtime. + +--- + +## Working Invariants Draft (Persona-Oriented) + +### Structural invariants +1. Persona config must parse and include all required top-level sections. +2. Every merged attribute must have exactly one treatment. +3. Every treated attribute must belong to exactly one group. +4. Group references must only contain known attributes. + +### Phrasing invariants +1. Boolean phrasing must provide both true/false phrases. +2. Categorical phrasing must cover full option set. +3. Relative phrasing must include all five z-bucket labels. +4. Concrete template must include renderable `{value}` semantics. + +### Runtime invariants +1. Intro-rendered attributes should not be duplicated in group rendering. +2. Decision-relevant attributes should be rendered in dedicated salience section when provided. +3. Rendering should not crash on missing or null values. + +### Quality invariants +1. Null-state categorical options should render as natural language, not raw enum tokens. +2. Persona text should remain first-person and internally coherent. + +--- + +## Glossary (Working Terms) +- **PersonaConfig**: Compiled rendering contract for first-person personas. +- **Treatment**: Concrete vs relative render mode assignment per attribute. +- **Relative phrasing**: Z-score bucket language compared to population mean/std. +- **Null option**: Categorical value indicating explicit absence/not-applicable state. +- **Decision salience**: Scenario-provided attribute list surfaced first for reasoning focus. + +--- + +## Practical Readout +Current persona stage is directionally strong: +- architecture is right, +- renderer is deterministic, +- simulation integration is substantial. + +Main debt is contract enforcement completeness: +- strong guardrails for categorical coverage, +- weaker explicit invariants for full config coverage and cross-field consistency. + +Given Extropy goals (realistic population + scenario + persona + network + simulation), +persona is a high-leverage quality gate and should be treated as such in backpressure design. + +--- + +## Final Statement +`persona` is the stage that converts structured agents into reasoning-ready human context. + +Today it already provides a capable compile-once/render-many system with meaningful runtime integration. +To fully support functional and behavioral bug detection across the pipeline, the next maturity step is explicit contract enforcement: +- complete coverage invariants, +- phrase quality diagnostics, +- and clearer ownership between persona compilation and simulation consumption. diff --git a/docs/pipeline/scenario.md b/docs/pipeline/scenario.md new file mode 100644 index 0000000..6f0d9b3 --- /dev/null +++ b/docs/pipeline/scenario.md @@ -0,0 +1,608 @@ +# Scenario Stage Reference: Scenario Extension and Event Dynamics + +## Document Intent +This file is the deep reference for the `scenario` stage in Extropy. + +Its job is to make one thing unambiguous for people and agents working on the pipeline: +- what `scenario` should do in principle, +- how that contract should be enforced, +- where the current implementation stands, +- where current behavior diverges from intended behavior, +- and what quality/backpressure signals this stage must expose. + +This is not a patch list. It is a stage contract + current-state reality map. + +## Status Update (2026-02-20) +- Scenario creation now enforces non-empty `extended_attributes` as a hard contract. +- Scenario validation in create-flow is now real validation (not light-pass placeholder). +- Attribute reference validation uses merged namespace (`base population ∪ scenario extension`). +- Literal-option compatibility checks are enforced for `when` conditions in: + - `seed_exposure.rules` + - `spread.share_modifiers` + - `timeline[*].exposure_rules` +- Household/focus coherence checks are enforced for household-active scenarios. +- Fail-hard scenario paths now emit versioned invalid artifacts (`*.invalid.vN.yaml` and JSON invalid diagnostics where applicable). + +--- + +## How To Read This Document +Use this order: +1. `Intended Contract` for design truth. +2. `Current Implementation (As-Is)` for code truth. +3. `Divergences, Mistakes, and Risk Areas` for the gap. +4. `Dependency Map` for how downstream stages consume scenario outputs. + +This file is intentionally verbose so a new team member can recover context without prior chat history. + +--- + +## Why This Stage Exists +`spec` gives a reusable population blueprint. +`scenario` should turn that blueprint into a simulation-ready context. + +Without scenario compilation, downstream stages would have to infer too much ad hoc: +- which additional attributes matter for this event, +- how information is introduced and spreads, +- whether scenario timing is static or evolving, +- what outcomes should be extracted from reasoning, +- how household/agent scope should be interpreted in this specific run. + +Design principle: +- **Compile scenario context once, execute many downstream steps against that contract.** + +--- + +## Pipeline Position +High-level pipeline intent: +1. Build base population encoding (`spec`). +2. Extend that population for a specific event context (`scenario`). +3. Build scenario-grounded rendering and graph context (`persona`, `network`). +4. Instantiate agents, propagate information, reason, and aggregate (`sample`, `simulate`, `results`, `query`, `chat`). + +`scenario` is the bridge stage between reusable base population mechanics and concrete event dynamics. + +--- + +## Intended Contract + +### What `scenario` should do +`scenario` should output a versioned scenario artifact that contains both: +1. **Scenario-specific population extension** (non-empty). +2. **Scenario dynamics contract** for exposure, spread, timeline, and outcomes. + +At minimum, it should: +- discover and encode scenario-specific extension attributes, +- preserve dependency-safe sampling semantics for those attributes, +- define the event and its source properties, +- define how agents hear about the event (channels/rules/timing), +- define interaction and spread behavior assumptions, +- define static vs evolving time semantics, +- define bounded outcome space for extraction, +- define household/agent activation semantics for this scenario, +- provide machine-checkable quality signals (impossible vs implausible). + +### What `scenario` should not do +`scenario` should not: +- sample concrete agents, +- generate concrete network edges, +- execute simulation loops, +- rely on hidden downstream defaults for critical semantics, +- silently degrade required fields for convenience. + +### How `scenario` should do it +Intended compile pattern: +1. Sufficiency check for event/timeline/outcome clarity. +2. Scenario extension discovery over base population context. +3. Extension hydration and dependency binding. +4. Event/exposure/interaction/timeline/outcome compilation. +5. Contract validation with severity separation. +6. Emit single coherent versioned scenario artifact. + +### Why this contract matters +If this stage is weak: +- sampling quality drops (because extension semantics are noisy), +- persona salience loses scenario grounding, +- network generation uses generic heuristics without event intent, +- simulation reasoning becomes brittle and underconstrained, +- result interpretation loses trust. + +`scenario` is therefore not just "event text generation." It is a downstream control contract. + +--- + +## Conceptual Model Of Scenario Compilation + +### Scenario as downstream control contract +Conceptually, `scenario` is a packet sent to four consumers: +- `sample`, +- `network`, +- `persona`, +- `simulate`. + +The packet should tell downstream systems: +- what changed in the world, +- who gets exposed, through what path, at what time, +- what social propagation assumptions apply, +- which dimensions matter for behavioral output, +- what outcomes to classify and track. + +### Two sub-domains inside scenario +Scenario naturally contains two sub-systems: +1. **Population extension subsystem** (mini-spec behavior). +2. **Event dynamics subsystem** (timeline/exposure/outcome behavior). + +This dual nature is expected. The contract must keep their boundary explicit. + +### Strict envelope, loose internals +Design choice for schema stability: +- **Strict envelope**: stable top-level keys must always exist. +- **Loose internals**: optional advanced sub-blocks can be empty when not relevant. + +This keeps deterministic parsing without hardcoding scenario-specific internals. + +### Required vs optional semantics +From current team direction: +- `extended_attributes` should be required and non-empty. +- Core dynamic blocks (`event`, `seed_exposure`, `spread`, `timeline`, `outcomes`) should be present. +- Advanced optional blocks may be empty when not used. + +--- + +## Intended Base vs Scenario Boundary + +### Base population (`spec`) should own +- reusable population mechanics, +- long-horizon demographic and identity distributions, +- generic dependency/distribution logic not tied to one event rollout. + +### Scenario should own +- event-context extension attributes, +- event/timeline/exposure/outcome semantics, +- household activation policy for this scenario, +- agent scope mode for this scenario (`primary_only`/`couples`/`all`). + +### Downstream stages should own +- `sample`: concrete instantiation with seed + runtime enforcement. +- `network`: concrete graph realization. +- `persona`: rendering config and textual embodiment. +- `simulate`: state evolution and reasoning loop. + +--- + +## Intended Artifact Contract + +### Primary artifact +`scenario` should emit versioned YAML under: +- `scenario//scenario.vN.yaml` + +It should serve as the single source of truth for scenario context. + +### Envelope contract (intended) +Top-level sections should be stable: +- `meta` +- `event` +- `timeline` +- `seed_exposure` +- `interaction` +- `spread` +- `outcomes` +- `simulation` +- `extended_attributes` +- `household_config` +- `agent_focus_mode` + +Optional top-level sections may exist (for example identity framing), but core sections should not be omitted. + +### Extension attribute contract (intended) +Each extension attribute should include: +- canonical name/type/category, +- strategy + dependency semantics, +- scope semantics (`individual`/`household`/`partner_correlated`), +- correlation metadata when relevant, +- constraints and grounding info. + +### Outcome contract (current team direction) +- bounded outcome set (cap = 8), +- no forced canonical single outcome requirement, +- not every outcome must be directly measurable by a numeric KPI, +- no mandatory misinformation/distortion path requirement. + +### Rule-pack contract (intended) +Scenario checks should distinguish: +- **impossible** contradictions -> hard block, +- **implausible** patterns -> warning/penalty signal, +- informational diagnostics -> non-blocking context. + +This rule-pack shape should be machine-consumable for backpressure. + +--- + +## Intended Quality Contract + +### Realism +Scenario should represent plausible information and response dynamics: +- realistic channels, +- realistic timing, +- realistic subgroup exposure differences. + +### Internal consistency +Scenario internals should agree with each other: +- timeline semantics must match simulation config, +- exposure rules should reference known attributes/channels, +- outcomes should align with event scope. + +### Downstream support reliability +Downstream stages should not need to infer missing semantics for core behavior. +If they do, the scenario contract is underspecified. + +### Determinism where needed +The stage should preserve flexibility without introducing arbitrary ambiguity for required controls. + +--- + +## Intended Backpressure Philosophy (Contract-Level) +Scenario compilation should output enough quality evidence to support go/no-go gating. + +Backpressure should be: +- explicit, +- machine-readable, +- tied to severity classes, +- traceable to concrete locations in scenario content. + +This is essential for catching functional and behavioral bugs before simulation. + +--- + +## Current Implementation (As-Is) + +This section describes actual code behavior now. + +### Entrypoint behavior in CLI +`extropy scenario` currently performs: +1. scenario sufficiency check, +2. extension attribute selection (`select_attributes` with base context), +3. extension hydration (`hydrate_attributes`), +4. extension binding (`bind_constraints`), +5. scenario config generation (`create_scenario_spec`), +6. post-assembly field assignment for extension+household+focus mode, +7. save. + +Primary orchestration file: +- `extropy/cli/commands/scenario.py` + +### Sufficiency behavior in code +Sufficiency model checks: +- event clarity, +- duration/timestep inference, +- static vs evolving inference, +- explicit outcomes presence, +- inferred `agent_focus_mode`. + +Current behavior characteristics: +- lenient sufficiency style, +- user clarification loop supported, +- inferred fields are mostly advisory for messaging except `agent_focus_mode` which is persisted after compile. + +Primary file: +- `extropy/scenario/sufficiency.py` + +### Mini-spec extension behavior in code +Scenario uses spec-builder primitives directly: +- attribute selector in overlay mode with base attributes as context, +- split hydrator pipeline (independent/derived/conditional/base+modifiers), +- household config hydration (conditional), +- name config hydration, +- dependency binding with expression-based dependency inference. + +Primary files: +- `extropy/population/spec_builder/selector.py` +- `extropy/population/spec_builder/hydrator.py` +- `extropy/population/spec_builder/binder.py` + +Important implementation detail: +- extension compilation is strong and feature-rich (close to spec flow), including scope and correlation semantics. + +### Scenario dynamics generation behavior in code +`create_scenario_spec` composes: +1. event parse (`parse_scenario`), +2. seed exposure generation (`generate_seed_exposure`), +3. interaction/spread generation (`determine_interaction_model`), +4. timeline+outcomes generation (`generate_timeline_and_outcomes`), +5. identity dimension detection. + +Primary files: +- `extropy/scenario/compiler.py` +- `extropy/scenario/parser.py` +- `extropy/scenario/exposure.py` +- `extropy/scenario/interaction.py` +- `extropy/scenario/timeline.py` + +### Important current coupling detail +In `create_scenario_spec`, parser/exposure/interaction generation uses a merged population context: +- base population attributes, plus +- scenario `extended_attributes`. + +Scenario creation-time generation passes `network_summary=None` (no synthetic placeholder edge types injected at this stage). + +### Validation behavior in current new flow +`create_scenario_spec` runs deterministic scenario validation in-flow via: +- `extropy/scenario/validator.py` + +`extropy scenario` checks validation errors before save. On fail-hard paths, it writes a versioned `.invalid` artifact and exits non-zero. + +### Assembly behavior for extension/household fields +`extended_attributes`, `household_config`, and `agent_focus_mode` are passed into `create_scenario_spec` and emitted as part of the assembled `ScenarioSpec` contract. + +### Timeline/outcome behavior in code +Timeline generator can produce static or evolving behavior and supports: +- timestep unit, +- max timesteps, +- timeline events with optional re-reasoning intensity. + +Outcome generation currently: +- encourages categorical/boolean/open_ended, +- discourages float in prompt, +- supports option friction for categorical outcomes, +- sets `capture_full_reasoning=true`. + +Current conversion behavior sets: +- `extraction_instructions=None` by default in generator return path. + +### Exposure channel behavior in code +Scenario model supports channel-level `experience_template`. +Runtime reasoning prompt consumes it when present. + +Exposure generation schema explicitly requests optional `experience_template`. + +### Identity framing behavior in code +Scenario compiler can detect `identity_dimensions`. +Simulation engine consumes those dimensions by matching to attribute `identity_type` and injecting identity relevance text into reasoning context. + +This is one of the stronger semantic links between scenario and simulation. + +--- + +## Current Runtime Coupling With Downstream Stages + +### Sample coupling +`sample` does: +- load base population from scenario metadata, +- merge `extended_attributes` into attributes list, +- append extended attribute names to base sampling order if missing, +- pass scenario `household_config` and `agent_focus_mode` into sampler. + +Primary file: +- `extropy/cli/commands/sample.py` + +### Household/agent scope behavior in sampler +Sampler supports: +- `primary_only`, +- `couples`, +- `all`. + +It uses household config and attribute scope semantics to decide: +- which members are full agents, +- which are NPC partner/dependent context. + +Primary files: +- `extropy/population/sampler/core.py` +- `extropy/population/sampler/households.py` + +### Persona coupling +`persona` command merges base+extended attributes for config generation. +At runtime, simulation persona rendering uses scenario `decision_relevant_attributes` to prioritize trait salience when available. + +Primary files: +- `extropy/cli/commands/persona.py` +- `extropy/simulation/engine.py` +- `extropy/population/persona/renderer.py` + +### Network coupling +`network` command merges base+extended attributes when generating network config. +Network config generation itself is population-driven and does not directly consume scenario spread/exposure fields in config synthesis. + +Primary files: +- `extropy/cli/commands/network.py` +- `extropy/population/network/config_generator.py` + +### Simulation coupling +Simulation consumes scenario directly for: +- event content, +- seed exposure channels/rules, +- spread modifiers, +- timeline events, +- outcomes schema, +- extraction instructions (if present), +- identity dimensions. + +Primary files: +- `extropy/simulation/engine.py` +- `extropy/simulation/propagation.py` +- `extropy/simulation/reasoning.py` + +--- + +## What Current Implementation Already Gets Right + +### Strong extension modeling capability +Scenario extension currently supports most of the rich attribute semantics that matter: +- strategy types, +- dependency inference, +- scope semantics, +- modifier-based conditional sampling. + +### Clear downstream field model +Scenario model surface is expressive and already includes key hooks: +- extension attrs, +- household config, +- agent focus mode, +- identity dimensions, +- decision-relevant attributes, +- experience templates. + +### Runtime integration quality in simulation +Simulation integration is substantial and not superficial: +- exposure history shaping, +- timeline-driven propagation, +- outcome schema-driven extraction, +- identity relevance framing. + +### CLI lifecycle coherence +Study-folder versioning flow and command sequencing are operationally coherent. + +--- + +## Divergences, Mistakes, and Risk Areas + +### 1) Scenario stage currently carries two heavy compilers +It runs both mini-spec extension compilation and event dynamics compilation in one stage. +This is conceptually valid but operationally heavy, raising coupling risk and debugging complexity. + +### 2) Contract remains broad/heavy in one stage +Scenario still combines two heavy concerns in one stage: +- scenario extension attribute compilation, and +- event dynamics compilation. +This is valid, but operationally complex. + +### 3) Base-vs-extended merge is now explicit in dynamics generation +Parser/exposure/interaction generation uses merged base+extended context in the new flow. +Remaining risk is quality of extension attributes themselves, not omission from dynamics prompt context. + +### 4) Validation strictness has improved, but rule depth can still expand +New-flow scenario creation runs deterministic validation before save. +Future quality gains are mostly about adding richer rule coverage, not wiring. + +### 5) Envelope stability vs practical generation mismatch +Model supports fields like `experience_template` and `extraction_instructions`, but default generation often leaves them null. +This is schema richness without guaranteed population of semantically useful hooks. + +### 6) Sampling order merge semantics are simplistic downstream +Sample merges extension by appending missing names to base order. +This can work, but does not guarantee a fully recomputed dependency-optimal merged order contract at this stage boundary. + +### 7) Network intent contract is implicit +Scenario has spread/exposure semantics, but network config generation is primarily population-driven. +The explicit scenario-to-network intent bridge is weak/implicit rather than formalized. + +### 8) Outcome policy is prompt-heavy +Outcome quality rules rely strongly on prompt instructions and conversion defaults. +Contract-level invariant enforcement for outcome count/shape/salience is not strongly centralized. + +### 9) Complexity debt in ownership clarity +Household semantics are now effectively scenario-owned in practice, but documentation/contracts can still be interpreted ambiguously if not explicit. + +--- + +## How It Can Be Made Better (Direction, Not Patch Plan) + +### Make scenario a single atomic contract emission +Avoid conceptual split between "core scenario object" and "later attached core fields." + +### Strengthen contract-level validation in authoring path +Bring strict structural + semantic checks earlier in the default `scenario` creation lifecycle. + +### Make base+extension context explicit before dynamics generation +Treat extension as first-class compile context for event/exposure/interaction synthesis. + +### Codify strict envelope + loose internals +Stabilize top-level keys while preserving contextual flexibility inside optional blocks. + +### Promote rule-pack severity model +Separate impossible/implausible checks explicitly and persist those diagnostics for pipeline backpressure. + +### Tighten scenario-to-network intent bridge +Clarify and formalize which scenario signals must be consumed by network synthesis. + +--- + +## Downstream Dependency Map (What `scenario` Must Support) + +### `sample` +Needs: +- non-empty `extended_attributes`, +- `household_config`, +- `agent_focus_mode`. + +### `network` +Needs: +- merged population context (base + extension), +- optional scenario intent signals for spread topology assumptions. + +### `persona` +Needs: +- merged attribute context, +- scenario salience hints (`decision_relevant_attributes`) for rendering emphasis. + +### `simulate` +Needs: +- event + timeline content, +- exposure channels/rules, +- spread modifiers, +- outcomes schema, +- optional extraction instructions, +- identity dimensions. + +### `estimate` +Needs: +- scenario simulation/time parameters + spread/exposure assumptions. + +### `results`, `query`, `chat` +Depend on scenario-defined outcome schema and timeline semantics for interpretation and querying consistency. + +--- + +## Working Invariants Draft (Scenario-Oriented) +These are contract-level invariants to support future backpressure harnesses. + +### Core invariants +1. `extended_attributes` must exist and be non-empty. +2. `event`, `seed_exposure`, `spread`, `outcomes`, and `simulation` must exist. +3. `agent_focus_mode` must be one of `primary_only`, `couples`, `all`. +4. Every exposure rule channel must reference a defined channel. +5. Every expression field must parse and reference known symbols only. + +### Outcome invariants +1. Outcome count must be within configured cap (current team direction: <= 8). +2. Categorical outcomes must have valid option sets. +3. Outcome names must be canonicalized and machine-safe. + +### Consistency invariants +1. Timeline timesteps must be unique and coherent with simulation horizon. +2. Household-related fields must not conflict with agent focus mode semantics. +3. Scenario metadata must resolve to a valid base population reference in study context. + +--- + +## Glossary (Working Terms) +- **Scenario extension**: Scenario-specific additional attributes over base population. +- **Strict envelope**: Stable required top-level schema keys. +- **Loose internals**: Optional internal subfields that can be empty when irrelevant. +- **Impossible**: Contradictory/invalid condition that should block compile. +- **Implausible**: Unusual but possible condition that should warn, not necessarily block. +- **Agent focus mode**: Household agent scope policy (`primary_only`/`couples`/`all`). +- **Experience template**: First-person phrasing template for how channel exposure is represented in reasoning prompts. + +--- + +## Practical Readout +At present, scenario is best interpreted as: +1. a powerful but heavy dual-compiler stage, +2. with strong extension expressivity, +3. strong runtime simulation integration, +4. and contract-enforcement gaps in authoring path consistency. + +For team operation, the most important framing is: +- the stage intent is correct, +- the ownership boundary is mostly right, +- but enforcement and assembly coherence need to be treated as first-class quality concerns. + +--- + +## Final Statement +`scenario` should be the stage that makes simulation context explicit, testable, and reusable. + +Current implementation already contains most raw capabilities needed to do this well. +The main remaining challenge is not missing concepts, but contract coherence: +- one atomic scenario contract, +- explicit ownership boundaries, +- and clear backpressure signals that downstream stages can trust. diff --git a/extropy/cli/commands/network.py b/extropy/cli/commands/network.py index 02247c7..379d984 100644 --- a/extropy/cli/commands/network.py +++ b/extropy/cli/commands/network.py @@ -682,6 +682,7 @@ def do_generation(): f"{scenario_name}__{config.quarantine_suffix}__{network_run_id[:12]}" ) gate_deltas = quality_meta.get("gate_deltas", {}) + final_metrics = quality_meta.get("final_metrics", {}) out.error( "Topology gate strict failed. Saved quarantined artifact; canonical network not overwritten." ) @@ -690,8 +691,12 @@ def do_generation(): f"[yellow]![/yellow] Quarantined network_id={target_network_id}" ) console.print( - f"[red]✗[/red] Failed gates with best metrics: {quality_meta.get('best_metrics', {})}" + f"[red]✗[/red] Failed gates with final metrics: {final_metrics}" ) + if quality_meta.get("best_metrics"): + console.print( + f"[dim]Calibration best metrics: {quality_meta.get('best_metrics', {})}[/dim]" + ) if gate_deltas: console.print(f"[dim]Gate deltas: {gate_deltas}[/dim]") console.print( diff --git a/extropy/cli/commands/persona.py b/extropy/cli/commands/persona.py index 1e9d851..61eaf0d 100644 --- a/extropy/cli/commands/persona.py +++ b/extropy/cli/commands/persona.py @@ -12,7 +12,14 @@ from ...core.models.scenario import ScenarioSpec from ..app import app, console, is_agent_mode, get_study_path from ..study import StudyContext, detect_study_folder, resolve_scenario -from ..utils import format_elapsed, Output, ExitCode +from ..utils import ( + format_elapsed, + Output, + ExitCode, + format_validation_for_json, + get_next_invalid_artifact_path, + save_invalid_json_artifact, +) @app.command("persona") @@ -71,6 +78,7 @@ def persona_command( from ...population.persona import ( generate_persona_config, PersonaConfigError, + validate_persona_config, ) from ...core.cost.tracker import CostTracker @@ -229,8 +237,45 @@ def do_generation(): gen_elapsed = time.time() - gen_start if gen_error: - out.error(f"Failed to generate persona config: {gen_error}") - raise typer.Exit(3) + invalid_path = save_invalid_json_artifact( + { + "stage": "persona", + "scenario": scenario_name, + "error": str(gen_error), + }, + persona_path.with_suffix(".json"), + stem=persona_path.stem, + extension=".json", + ) + out.error( + f"Failed to generate persona config: {gen_error}. Saved: {invalid_path}", + exit_code=ExitCode.VALIDATION_ERROR, + ) + raise typer.Exit(out.finish()) + + validation_result = validate_persona_config(merged_spec, config) + out.set_data("validation", format_validation_for_json(validation_result)) + if validation_result.errors: + invalid_path = get_next_invalid_artifact_path(persona_path) + config.to_yaml(invalid_path) + out.error( + f"Persona config validation failed ({len(validation_result.errors)} errors). Saved: {invalid_path}", + exit_code=ExitCode.VALIDATION_ERROR, + ) + if not agent_mode: + for err in validation_result.errors[:10]: + console.print(f"[red]✗[/red] {err.location}: {err.message}") + if len(validation_result.errors) > 10: + console.print( + f"[dim]... and {len(validation_result.errors) - 10} more[/dim]" + ) + raise typer.Exit(out.finish()) + if validation_result.warnings and not agent_mode: + console.print( + f"[yellow]⚠[/yellow] Persona validation warnings: {len(validation_result.warnings)}" + ) + for warn in validation_result.warnings[:5]: + console.print(f" [dim]- {warn.location}: {warn.message}[/dim]") if not agent_mode: console.print( diff --git a/extropy/cli/commands/sample.py b/extropy/cli/commands/sample.py index 2eb1c44..c6a5589 100644 --- a/extropy/cli/commands/sample.py +++ b/extropy/cli/commands/sample.py @@ -8,6 +8,7 @@ from ...core.models import PopulationSpec from ...core.models.scenario import ScenarioSpec from ...population.validator import validate_spec +from ...utils import topological_sort, CircularDependencyError from ..app import app, console, is_agent_mode, get_study_path from ..study import StudyContext, detect_study_folder, resolve_scenario from ..utils import ( @@ -16,6 +17,7 @@ format_elapsed, format_validation_for_json, format_sampling_stats_for_json, + save_invalid_json_artifact, ) @@ -42,6 +44,11 @@ def sample_command( skip_validation: bool = typer.Option( False, "--skip-validation", help="Skip validator errors" ), + strict_gates: bool = typer.Option( + False, + "--strict-gates", + help="Fail on high-risk warnings and post-sampling coherence gate violations", + ), ): """ Sample agents from a scenario's merged population spec. @@ -135,11 +142,34 @@ def sample_command( extended_attrs = scenario_spec.extended_attributes or [] merged_attributes.extend(extended_attrs) - # Compute merged sampling order - merged_sampling_order = list(pop_spec.sampling_order) - for attr in extended_attrs: - if attr.name not in merged_sampling_order: - merged_sampling_order.append(attr.name) + # Compute merged sampling order deterministically via topological sort + try: + merged_deps: dict[str, list[str]] = { + attr.name: list(attr.sampling.depends_on or []) + for attr in merged_attributes + } + merged_names = set(merged_deps.keys()) + merged_deps = { + name: [dep for dep in deps if dep in merged_names] + for name, deps in merged_deps.items() + } + merged_sampling_order = topological_sort(merged_deps) + except CircularDependencyError as e: + invalid_path = save_invalid_json_artifact( + { + "stage": "sample", + "scenario": scenario_name, + "error": f"merged_sampling_order_cycle: {e}", + }, + study_ctx.get_scenario_dir(scenario_name) / "sample.json", + stem="sample", + extension=".json", + ) + out.error( + f"Merged sampling order has circular dependency: {e}. Saved: {invalid_path}", + exit_code=ExitCode.VALIDATION_ERROR, + ) + raise typer.Exit(out.finish()) # Create merged spec for sampling merged_spec = PopulationSpec( @@ -152,6 +182,7 @@ def sample_command( # Get household config and agent focus mode from scenario household_config = scenario_spec.household_config agent_focus_mode = scenario_spec.agent_focus_mode + sampling_semantic_roles = scenario_spec.sampling_semantic_roles out.success( f"Loaded scenario: [bold]{scenario_name}[/bold] " @@ -160,6 +191,7 @@ def sample_command( base_population=f"{pop_name}.v{pop_version}", attribute_count=len(merged_attributes), agent_count=count, + strict_gates=strict_gates, ) # Validation Gate @@ -178,8 +210,19 @@ def sample_command( f"Spec has {len(validation_result.errors)} error(s) - skipping validation" ) else: + invalid_path = save_invalid_json_artifact( + { + "stage": "sample", + "scenario": scenario_name, + "error": "merged_spec_validation_failed", + "validation": format_validation_for_json(validation_result), + }, + study_ctx.get_scenario_dir(scenario_name) / "sample.json", + stem="sample", + extension=".json", + ) out.error( - f"Merged spec has {len(validation_result.errors)} error(s)", + f"Merged spec has {len(validation_result.errors)} error(s). Saved: {invalid_path}", exit_code=ExitCode.VALIDATION_ERROR, ) if not agent_mode: @@ -200,6 +243,38 @@ def sample_command( else: out.success("Spec validated") + if strict_gates: + strict_warning_categories = {"CONDITION_VALUE", "MODIFIER_OVERLAP"} + promoted = [ + warn + for warn in (validation_result.warnings or []) + if warn.category in strict_warning_categories + ] + if promoted: + invalid_path = save_invalid_json_artifact( + { + "stage": "sample", + "scenario": scenario_name, + "error": "strict_gate_pre_sample_failed", + "promoted_warnings": [ + { + "location": w.location, + "category": w.category, + "message": w.message, + } + for w in promoted + ], + }, + study_ctx.get_scenario_dir(scenario_name) / "sample.json", + stem="sample", + extension=".json", + ) + out.error( + f"Strict gate blocked sampling due to high-risk warnings ({len(promoted)}). Saved: {invalid_path}", + exit_code=ExitCode.VALIDATION_ERROR, + ) + raise typer.Exit(out.finish()) + # Sampling out.blank() sampling_start = time.time() @@ -238,6 +313,7 @@ def on_progress(current: int, total: int): on_progress=on_progress, household_config=household_config, agent_focus_mode=agent_focus_mode, + semantic_roles=sampling_semantic_roles, ) except SamplingError as e: sampling_error = e @@ -251,6 +327,7 @@ def on_progress(current: int, total: int): seed=seed, household_config=household_config, agent_focus_mode=agent_focus_mode, + semantic_roles=sampling_semantic_roles, ) except SamplingError as e: sampling_error = e @@ -262,13 +339,24 @@ def on_progress(current: int, total: int): seed=seed, household_config=household_config, agent_focus_mode=agent_focus_mode, + semantic_roles=sampling_semantic_roles, ) except SamplingError as e: sampling_error = e if sampling_error: + invalid_path = save_invalid_json_artifact( + { + "stage": "sample", + "scenario": scenario_name, + "error": str(sampling_error), + }, + study_ctx.get_scenario_dir(scenario_name) / "sample.json", + stem="sample", + extension=".json", + ) out.error( - f"Sampling failed: {sampling_error}", + f"Sampling failed: {sampling_error}. Saved: {invalid_path}", exit_code=ExitCode.SAMPLING_ERROR, suggestion="Check attribute dependencies and formula syntax", ) @@ -282,6 +370,36 @@ def on_progress(current: int, total: int): sampling_time_seconds=sampling_elapsed, ) + # Post-sampling deterministic rule-pack gate + rule_pack = _evaluate_rule_pack(result, strict_gates=strict_gates) + result.stats.rule_pack = rule_pack + out.set_data("rule_pack", rule_pack) + gate_failed = rule_pack["status"] == "fail" + if gate_failed: + invalid_path = save_invalid_json_artifact( + { + "stage": "sample", + "scenario": scenario_name, + "error": "post_sample_rule_pack_failed", + "rule_pack": rule_pack, + "reconciliation_counts": result.stats.reconciliation_counts, + "constraint_violations": result.stats.constraint_violations, + "condition_warnings": result.stats.condition_warnings, + }, + study_ctx.get_scenario_dir(scenario_name) / "sample.json", + stem="sample", + extension=".json", + ) + out.error( + f"Post-sampling quality gate failed. Saved: {invalid_path}", + exit_code=ExitCode.SAMPLING_ERROR, + ) + raise typer.Exit(out.finish()) + if rule_pack.get("status") == "warn": + out.warning( + rule_pack.get("summary", "Sampling completed with plausibility warnings") + ) + # Report if agent_mode or report: out.set_data("stats", format_sampling_stats_for_json(result.stats, merged_spec)) @@ -333,6 +451,51 @@ def on_progress(current: int, total: int): raise typer.Exit(out.finish()) +def _evaluate_rule_pack(result, *, strict_gates: bool) -> dict: + """Evaluate deterministic impossible/implausible gates on sampled output.""" + total_agents = max(1, len(result.agents)) + impossible_count = sum(result.stats.constraint_violations.values()) + implausible_count = sum(result.stats.reconciliation_counts.values()) + implausible_rate = implausible_count / total_agents + + if strict_gates and result.stats.condition_warnings: + return { + "status": "fail", + "summary": "Condition-evaluation warnings present in strict mode", + "impossible_count": impossible_count, + "implausible_count": implausible_count, + "implausible_rate": implausible_rate, + "condition_warning_count": len(result.stats.condition_warnings), + } + + if impossible_count > 0: + return { + "status": "fail", + "summary": "Impossible rule violations detected", + "impossible_count": impossible_count, + "implausible_count": implausible_count, + "implausible_rate": implausible_rate, + } + + if implausible_rate > 0.05: + status = "fail" + summary = "Implausible reconciliation rate exceeds 5%" + elif implausible_rate >= 0.01: + status = "warn" + summary = "Implausible reconciliation rate between 1% and 5%" + else: + status = "pass" + summary = "Rule-pack checks passed" + + return { + "status": status, + "summary": summary, + "impossible_count": impossible_count, + "implausible_count": implausible_count, + "implausible_rate": implausible_rate, + } + + def _save_to_db( db_path: Path, scenario_name: str, diff --git a/extropy/cli/commands/validate.py b/extropy/cli/commands/validate.py index 5d46ce9..3e6515b 100644 --- a/extropy/cli/commands/validate.py +++ b/extropy/cli/commands/validate.py @@ -30,6 +30,16 @@ def _is_scenario_file(path: Path) -> bool: return False +def _is_persona_file(path: Path) -> bool: + """Check if file is a persona config based on naming convention.""" + name = path.name + if name in {"persona.yaml", "persona.yml"}: + return True + if re.match(r"^persona\.v\d+\.ya?ml$", name): + return True + return False + + def _validate_population_spec(spec_file: Path, strict: bool, out: Output) -> int: """Validate a population spec.""" # Load spec @@ -260,6 +270,113 @@ def _validate_scenario_spec(spec_file: Path, out: Output) -> int: return out.finish() +def _resolve_merged_population_for_persona(persona_file: Path) -> PopulationSpec: + """Resolve merged population spec (base + extension) for persona validation.""" + from ...core.models import PopulationSpec + from ...core.models.scenario import ScenarioSpec + + scenario_dir = persona_file.parent + scenario_files = sorted( + scenario_dir.glob("scenario.v*.yaml"), + key=lambda p: int(re.search(r"scenario\.v(\d+)\.yaml$", p.name).group(1)), # type: ignore[union-attr] + ) + if not scenario_files: + raise FileNotFoundError( + f"No scenario.vN.yaml found next to persona file: {persona_file}" + ) + + # Choose latest scenario version in this directory. + scenario_path = scenario_files[-1] + scenario_spec = ScenarioSpec.from_yaml(scenario_path) + + pop_name, pop_version = scenario_spec.meta.get_population_ref() + if pop_version is None: + raise ValueError( + "Scenario must reference a versioned base_population for persona validation" + ) + + # Study layout: /scenario//persona.vN.yaml + study_root = scenario_dir.parent.parent + pop_path = study_root / f"{pop_name}.v{pop_version}.yaml" + if not pop_path.exists(): + raise FileNotFoundError(f"Referenced population spec not found: {pop_path}") + + pop_spec = PopulationSpec.from_yaml(pop_path) + ext_attrs = scenario_spec.extended_attributes or [] + merged_attrs = list(pop_spec.attributes) + ext_attrs + merged_order = pop_spec.merged_sampling_order(ext_attrs) + return PopulationSpec( + meta=pop_spec.meta, + grounding=pop_spec.grounding, + attributes=merged_attrs, + sampling_order=merged_order, + ) + + +def _validate_persona_spec(spec_file: Path, out: Output) -> int: + """Validate a persona config against merged scenario attributes.""" + from ...population.persona import PersonaConfig, validate_persona_config + + if not _is_json_output(): + with console.status("[cyan]Loading persona config...[/cyan]"): + try: + config = PersonaConfig.from_yaml(spec_file) + merged_spec = _resolve_merged_population_for_persona(spec_file) + result = validate_persona_config(merged_spec, config) + except Exception as e: + out.error( + f"Failed to validate persona: {e}", + exit_code=ExitCode.VALIDATION_ERROR, + ) + return out.finish() + else: + try: + config = PersonaConfig.from_yaml(spec_file) + merged_spec = _resolve_merged_population_for_persona(spec_file) + result = validate_persona_config(merged_spec, config) + except Exception as e: + out.error( + f"Failed to validate persona: {e}", exit_code=ExitCode.VALIDATION_ERROR + ) + return out.finish() + + out.success( + f"Loaded persona config ({len(config.treatments)} treatments)", + spec_file=str(spec_file), + treatment_count=len(config.treatments), + ) + out.blank() + out.set_data("validation", format_validation_for_json(result)) + + if result.errors: + out.error( + f"Persona has {len(result.errors)} error(s)", + exit_code=ExitCode.VALIDATION_ERROR, + ) + if not _is_json_output(): + for err in result.errors[:10]: + out.text( + f" [red]✗[/red] [{err.category}] {err.location}: {err.message}" + ) + if len(result.errors) > 10: + out.text(f" [dim]... and {len(result.errors) - 10} more[/dim]") + return out.finish() + + if result.warnings: + out.success(f"Persona validated with {len(result.warnings)} warning(s)") + if not _is_json_output(): + for warn in result.warnings[:5]: + out.warning(f"[{warn.category}] {warn.location}: {warn.message}") + else: + out.success("Persona validated") + + out.blank() + out.divider() + out.text("[green]Validation passed[/green]") + out.divider() + return out.finish() + + @app.command("validate") def validate_command( spec_file: Path = typer.Argument( @@ -270,10 +387,11 @@ def validate_command( ), ): """ - Validate a population spec or scenario spec. + Validate a population spec, scenario spec, or persona config. Auto-detects file type based on naming: - *.scenario.yaml → scenario spec validation + - persona.vN.yaml → persona config validation - *.yaml → population spec validation EXIT CODES: @@ -301,6 +419,8 @@ def validate_command( # Route to appropriate validator if _is_scenario_file(spec_file): exit_code = _validate_scenario_spec(spec_file, out) + elif _is_persona_file(spec_file): + exit_code = _validate_persona_spec(spec_file, out) else: exit_code = _validate_population_spec(spec_file, strict, out) diff --git a/extropy/cli/utils.py b/extropy/cli/utils.py index a19ed96..d91b7b3 100644 --- a/extropy/cli/utils.py +++ b/extropy/cli/utils.py @@ -18,6 +18,8 @@ def my_command(): from __future__ import annotations import json +import re +from pathlib import Path from typing import Any from pydantic import BaseModel, PrivateAttr, ConfigDict @@ -263,6 +265,58 @@ def grounding_indicator(level: str) -> str: return indicators.get(level, "[dim]Unknown[/dim]") +def get_next_invalid_artifact_path( + target_path: Path | str, + *, + stem: str | None = None, + extension: str | None = None, +) -> Path: + """Compute the next versioned invalid-artifact path. + + Examples: + scenario.v1.yaml -> scenario.v1.invalid.v1.yaml + scenario.v1.yaml -> scenario.v1.invalid.v2.yaml (if v1 already exists) + stem="sample", extension=".json" -> sample.invalid.vN.json + """ + target = Path(target_path) + parent = target.parent + parent.mkdir(parents=True, exist_ok=True) + + base_stem = stem if stem is not None else target.stem + suffix = extension if extension is not None else target.suffix + if suffix and not suffix.startswith("."): + suffix = f".{suffix}" + + pattern = re.compile( + rf"^{re.escape(base_stem)}\.invalid\.v(?P\d+){re.escape(suffix)}$" + ) + max_version = 0 + for existing in parent.iterdir(): + if not existing.is_file(): + continue + match = pattern.match(existing.name) + if not match: + continue + max_version = max(max_version, int(match.group("version"))) + + next_version = max_version + 1 + return parent / f"{base_stem}.invalid.v{next_version}{suffix}" + + +def save_invalid_json_artifact( + payload: dict[str, Any], + target_path: Path | str, + *, + stem: str | None = None, + extension: str = ".json", +) -> Path: + """Save a versioned invalid artifact as JSON and return its path.""" + path = get_next_invalid_artifact_path(target_path, stem=stem, extension=extension) + with open(path, "w", encoding="utf-8") as f: + json.dump(payload, f, indent=2, default=str) + return path + + def format_validation_for_json(result) -> dict[str, Any]: """Convert ValidationResult to JSON-serializable dict.""" return { @@ -344,4 +398,13 @@ def format_sampling_stats_for_json(stats, spec) -> dict[str, Any]: if stats.constraint_violations: result["constraint_violations"] = stats.constraint_violations + if stats.condition_warnings: + result["condition_warnings"] = stats.condition_warnings + + if stats.reconciliation_counts: + result["reconciliation_counts"] = stats.reconciliation_counts + + if stats.rule_pack: + result["rule_pack"] = stats.rule_pack + return result diff --git a/extropy/core/models/sampling.py b/extropy/core/models/sampling.py index 0779852..18dd9a3 100644 --- a/extropy/core/models/sampling.py +++ b/extropy/core/models/sampling.py @@ -26,6 +26,12 @@ class SamplingStats(BaseModel): # Condition evaluation warnings condition_warnings: list[str] = Field(default_factory=list) + # Deterministic post-household reconciliation counters + reconciliation_counts: dict[str, int] = Field(default_factory=dict) + + # Rule-pack quality summary (impossible/implausible backpressure) + rule_pack: dict[str, Any] = Field(default_factory=dict) + class SamplingResult(BaseModel): """Result of sampling a population.""" diff --git a/extropy/core/models/scenario.py b/extropy/core/models/scenario.py index 9a9368b..8da9446 100644 --- a/extropy/core/models/scenario.py +++ b/extropy/core/models/scenario.py @@ -7,7 +7,7 @@ This module contains: - Event: EventType, Event - Exposure: ExposureChannel, ExposureRule, SeedExposure -- Interaction: InteractionType, InteractionConfig, SpreadModifier, SpreadConfig +- Interaction: InteractionConfig, SpreadModifier, SpreadConfig - Outcomes: OutcomeType, OutcomeDefinition, OutcomeConfig - Config: TimestepUnit, ScenarioSimConfig - Spec: ScenarioMeta, ScenarioSpec with YAML I/O @@ -83,9 +83,6 @@ class ExposureChannel(BaseModel): description="Channel identifier in snake_case (e.g., 'email_notification')" ) description: str = Field(description="Human-readable description of the channel") - reach: Literal["broadcast", "targeted", "organic"] = Field( - description="broadcast=everyone, targeted=specific criteria, organic=through network" - ) credibility_modifier: float = Field( default=1.0, description="How the channel affects perceived credibility (1.0=no change)", @@ -147,32 +144,12 @@ class TimelineEvent(BaseModel): ) -# ============================================================================= -# Interaction Model -# ============================================================================= - - -class InteractionType(str, Enum): - """Type of agent interaction model.""" - - PASSIVE_OBSERVATION = "passive_observation" # Social media style - DIRECT_CONVERSATION = "direct_conversation" # One-on-one or small group - BROADCAST_RESPONSE = "broadcast_response" # Authority broadcasts, agents react - DELIBERATIVE = "deliberative" # Group deliberation with multiple rounds - - class InteractionConfig(BaseModel): """Configuration for how agents interact about the event.""" - primary_model: InteractionType = Field( - description="Primary interaction model for this scenario" - ) - secondary_model: InteractionType | None = Field( - default=None, - description="Optional secondary interaction model (for blended scenarios)", - ) description: str = Field( - description="Human-readable description of how interactions work" + default="", + description="Human-readable notes about social dynamics (informational only).", ) @@ -405,6 +382,98 @@ class IdentityDimension(BaseModel): ) +PartnerCorrelationPolicy = Literal[ + "gaussian_offset", + "same_group_rate", + "same_country_rate", + "same_value_probability", +] + + +class MaritalRoles(BaseModel): + """Explicit marital semantics for deterministic household reconciliation.""" + + attr: str | None = Field( + default=None, + description="Attribute name representing marital/relationship status", + ) + partnered_values: list[str] = Field( + default_factory=list, + description="Categorical values indicating partnered state", + ) + single_values: list[str] = Field( + default_factory=list, + description="Categorical values indicating single/non-partnered state", + ) + + +class GeoRoles(BaseModel): + """Geographic attribute roles used for naming and contextual defaults.""" + + country_attr: str | None = Field( + default=None, description="Attribute name representing country" + ) + region_attr: str | None = Field( + default=None, + description="Attribute name representing region/state/province/city scope", + ) + urbanicity_attr: str | None = Field( + default=None, + description="Attribute name representing urbanicity (urban/rural/etc.)", + ) + + +class SchoolParentRole(BaseModel): + """Role mapping for identifying school-parent contexts in structure generation.""" + + dependents_attr: str | None = Field( + default=None, + description="Attribute name containing dependents/children list", + ) + school_age_values: list[str] = Field( + default_factory=list, + description="Dependent school-status values treated as school-age", + ) + + +class ReligionRoles(BaseModel): + """Role mapping for religion semantics in structural edge generation.""" + + religion_attr: str | None = Field( + default=None, + description="Attribute name representing religion/faith affiliation", + ) + secular_values: list[str] = Field( + default_factory=list, + description="Values treated as secular/no-religion", + ) + + +class SamplingSemanticRoles(BaseModel): + """Scenario-owned semantic role mapping for deterministic sample/runtime logic.""" + + marital_roles: MaritalRoles | None = Field( + default=None, + description="Marital/relationship status semantics", + ) + geo_roles: GeoRoles | None = Field( + default=None, + description="Geographic role mapping", + ) + partner_correlation_roles: dict[str, PartnerCorrelationPolicy] = Field( + default_factory=dict, + description="Per-attribute partner-correlation policy overrides", + ) + school_parent_role: SchoolParentRole | None = Field( + default=None, + description="School-parent role mapping", + ) + religion_roles: ReligionRoles | None = Field( + default=None, + description="Religion role mapping", + ) + + class ScenarioSpec(BaseModel): """Complete specification for a scenario simulation.""" @@ -449,6 +518,11 @@ class ScenarioSpec(BaseModel): default=None, description="Identity dimensions that may feel threatened or activated by this scenario. Set by LLM during scenario creation.", ) + # Scenario-owned semantic role map for sample/network deterministic behavior + sampling_semantic_roles: SamplingSemanticRoles | None = Field( + default=None, + description="Semantic role mappings generated during scenario compilation for deterministic sample/network execution.", + ) def to_yaml(self, path: Path | str) -> None: """Save scenario spec to YAML file.""" @@ -508,10 +582,14 @@ def summary(self) -> str: f"Exposure channels: {len(self.seed_exposure.channels)}", f"Exposure rules: {len(self.seed_exposure.rules)}", "", - f"Interaction: {self.interaction.primary_model.value}", f"Share probability: {self.spread.share_probability:.2f}", "", f"Outcomes: {len(self.outcomes.suggested_outcomes)}", f"Simulation: {self.simulation.max_timesteps} {self.simulation.timestep_unit.value}s", ] + if self.interaction and self.interaction.description: + lines.insert( + 8, + f"Interaction notes: {self.interaction.description[:60]}...", + ) return "\n".join(lines) diff --git a/extropy/population/names/__init__.py b/extropy/population/names/__init__.py index 89fe455..69aa958 100644 --- a/extropy/population/names/__init__.py +++ b/extropy/population/names/__init__.py @@ -1,7 +1,8 @@ """Name generation for synthetic agents. Generates culturally plausible first + last names based on -gender, ethnicity, and birth decade. US-only for now. +gender, ethnicity, and birth decade using Faker locale routing. +Bundled CSV registries are retained as a deterministic fallback. """ from .generator import generate_name diff --git a/extropy/population/names/data/first_names.csv b/extropy/population/names/data/first_names.csv index dfef912..1f87a1e 100644 --- a/extropy/population/names/data/first_names.csv +++ b/extropy/population/names/data/first_names.csv @@ -1,161 +1,257 @@ name,gender,decade,weight -James,male,1940,4.748 -Robert,male,1940,4.599 -John,male,1940,4.510 -William,male,1940,3.614 -Richard,male,1940,2.563 -David,male,1940,1.861 -Charles,male,1940,2.106 -Thomas,male,1940,2.304 -Donald,male,1940,2.457 -Ronald,male,1940,1.075 -Mary,female,1940,3.917 -Barbara,female,1940,2.312 -Patricia,female,1940,2.297 -Betty,female,1940,1.736 -Shirley,female,1940,1.530 -Dorothy,female,1940,1.473 -Carol,female,1940,1.328 -Nancy,female,1940,1.130 -Margaret,female,1940,1.094 -Sandra,female,1940,1.075 -James,male,1950,4.568 -Robert,male,1950,4.148 -Michael,male,1950,4.248 -John,male,1950,3.811 -David,male,1950,3.611 -William,male,1950,2.710 -Richard,male,1950,2.503 -Gary,male,1950,1.829 -Thomas,male,1950,2.204 -Larry,male,1950,1.525 -Mary,female,1950,3.360 -Linda,female,1950,2.469 -Susan,female,1950,2.072 -Karen,female,1950,1.864 -Patricia,female,1950,1.620 -Donna,female,1950,1.426 -Debra,female,1950,1.241 -Deborah,female,1950,1.355 -Sandra,female,1950,1.250 -Barbara,female,1950,1.395 -Michael,male,1960,4.338 -David,male,1960,3.559 -James,male,1960,3.261 -John,male,1960,3.254 -Robert,male,1960,3.138 -Mark,male,1960,2.337 -William,male,1960,2.145 -Richard,male,1960,1.896 -Steven,male,1960,1.783 -Thomas,male,1960,1.770 -Lisa,female,1960,2.370 -Mary,female,1960,2.063 -Jennifer,female,1960,1.468 -Susan,female,1960,1.571 -Karen,female,1960,1.456 -Michelle,female,1960,1.146 -Kimberly,female,1960,1.357 -Donna,female,1960,1.072 -Patricia,female,1960,1.028 -Angela,female,1960,1.027 -Michael,male,1970,4.318 -Christopher,male,1970,2.758 -Jason,male,1970,2.686 -David,male,1970,2.620 -James,male,1970,2.615 -Robert,male,1970,2.096 -John,male,1970,2.088 -Brian,male,1970,1.933 -William,male,1970,1.522 -Matthew,male,1970,1.477 -Jennifer,female,1970,3.165 -Amy,female,1970,1.618 -Michelle,female,1970,1.422 -Melissa,female,1970,1.441 -Heather,female,1970,1.117 -Angela,female,1970,1.209 -Nicole,female,1970,1.012 -Jessica,female,1970,1.045 -Stephanie,female,1970,1.012 -Lisa,female,1970,1.240 -Michael,male,1980,3.694 -Christopher,male,1980,2.847 -Matthew,male,1980,2.691 -Joshua,male,1980,2.386 -David,male,1980,1.932 -Daniel,male,1980,1.902 -James,male,1980,1.846 -Robert,male,1980,1.502 -Andrew,male,1980,1.491 -John,male,1980,1.487 -Jessica,female,1980,3.263 -Jennifer,female,1980,2.466 -Amanda,female,1980,2.032 -Ashley,female,1980,1.763 -Sarah,female,1980,1.558 -Stephanie,female,1980,1.390 -Melissa,female,1980,1.295 -Nicole,female,1980,1.262 -Elizabeth,female,1980,1.054 -Heather,female,1980,1.152 -Michael,male,1990,2.862 -Christopher,male,1990,2.218 -Matthew,male,1990,2.007 -Joshua,male,1990,1.987 -Andrew,male,1990,1.476 -Daniel,male,1990,1.417 -Brandon,male,1990,1.413 -James,male,1990,1.390 -Tyler,male,1990,1.334 -David,male,1990,1.237 -Jessica,female,1990,2.254 -Ashley,female,1990,2.140 -Brittany,female,1990,1.489 -Amanda,female,1990,1.458 -Samantha,female,1990,1.403 -Sarah,female,1990,1.270 -Stephanie,female,1990,1.137 -Jennifer,female,1990,1.131 -Elizabeth,female,1990,1.099 -Lauren,female,1990,1.023 -Jacob,male,2000,1.639 -Michael,male,2000,1.568 -Joshua,male,2000,1.398 -Matthew,male,2000,1.319 -Ethan,male,2000,1.143 -Andrew,male,2000,1.117 -Daniel,male,2000,1.111 -Christopher,male,2000,1.052 -Joseph,male,2000,1.041 -William,male,2000,1.003 -Emily,female,2000,1.345 -Madison,female,2000,1.078 -Hannah,female,2000,1.061 -Emma,female,2000,0.925 -Ashley,female,2000,0.849 -Alexis,female,2000,0.847 -Samantha,female,2000,0.826 -Abigail,female,2000,0.802 -Sarah,female,2000,0.768 -Olivia,female,2000,0.757 -Liam,male,2010,0.972 -Noah,male,2010,0.876 -Ethan,male,2010,0.847 -Jacob,male,2010,0.842 -Aiden,male,2010,0.794 -Mason,male,2010,0.769 -Jackson,male,2010,0.721 -William,male,2010,0.698 -Alexander,male,2010,0.682 -James,male,2010,0.651 -Sophia,female,2010,1.008 -Isabella,female,2010,0.950 -Emma,female,2010,0.921 -Olivia,female,2010,0.879 -Ava,female,2010,0.832 -Emily,female,2010,0.771 -Mia,female,2010,0.717 -Abigail,female,2010,0.706 -Madison,female,2010,0.630 -Chloe,female,2010,0.606 +James,male,1940,4.2 +Robert,male,1940,4.0 +John,male,1940,3.8 +William,male,1940,3.2 +Mohammed,male,1940,2.4 +Jose,male,1940,2.1 +Giovanni,male,1940,1.7 +Hiroshi,male,1940,1.5 +Rajesh,male,1940,1.5 +Ahmed,male,1940,1.6 +Kwame,male,1940,1.2 +Yusuf,male,1940,1.1 +Wei,male,1940,1.3 +Carlos,male,1940,1.8 +Pierre,male,1940,1.4 +Ivan,male,1940,1.2 +Mary,female,1940,3.6 +Maria,female,1940,3.1 +Fatima,female,1940,2.3 +Ana,female,1940,1.9 +Aiko,female,1940,1.3 +Leila,female,1940,1.2 +Patricia,female,1940,2.2 +Elizabeth,female,1940,2.0 +Shirley,female,1940,1.7 +Yelena,female,1940,1.1 +Amina,female,1940,1.3 +Dorothy,female,1940,1.5 +Nadia,female,1940,1.2 +Sofia,female,1940,1.3 +Rosa,female,1940,1.4 +Grace,female,1940,1.3 +Michael,male,1950,4.1 +David,male,1950,3.6 +James,male,1950,3.0 +Kenji,male,1950,1.6 +Satoshi,male,1950,1.4 +Ravi,male,1950,1.8 +Omar,male,1950,1.6 +Miguel,male,1950,2.0 +Jose,male,1950,2.0 +Ali,male,1950,1.6 +Pierre,male,1950,1.6 +Sergey,male,1950,1.3 +Tunde,male,1950,1.1 +Nicolas,male,1950,1.2 +Manuel,male,1950,1.4 +Hamid,male,1950,1.1 +Linda,female,1950,2.7 +Susan,female,1950,2.1 +Patricia,female,1950,1.9 +Keiko,female,1950,1.5 +Yuko,female,1950,1.3 +Priya,female,1950,1.5 +Farah,female,1950,1.3 +Maria,female,1950,2.0 +Carmen,female,1950,1.6 +Elena,female,1950,1.5 +Nadia,female,1950,1.4 +Aisha,female,1950,1.3 +Amina,female,1950,1.2 +Monica,female,1950,1.2 +Teresa,female,1950,1.3 +Olga,female,1950,1.1 +Michael,male,1960,3.9 +David,male,1960,3.4 +Mark,male,1960,2.5 +Andrei,male,1960,1.5 +Ivan,male,1960,1.4 +Wei,male,1960,1.9 +Jian,male,1960,1.7 +Raj,male,1960,1.8 +Khalid,male,1960,1.5 +Hassan,male,1960,1.5 +Carlos,male,1960,2.0 +Luis,male,1960,1.8 +Samuel,male,1960,1.6 +Mateusz,male,1960,1.2 +Abdul,male,1960,1.3 +Thierry,male,1960,1.1 +Lisa,female,1960,2.4 +Mary,female,1960,2.1 +Jennifer,female,1960,1.8 +Mei,female,1960,1.6 +Wei,female,1960,1.4 +Anita,female,1960,1.7 +Sunita,female,1960,1.4 +Zahra,female,1960,1.3 +Leila,female,1960,1.5 +Sofia,female,1960,1.6 +Olga,female,1960,1.2 +Yasmin,female,1960,1.3 +Helena,female,1960,1.2 +Carla,female,1960,1.2 +Nkechi,female,1960,1.0 +Marina,female,1960,1.1 +Michael,male,1970,3.7 +Christopher,male,1970,2.8 +Jason,male,1970,2.4 +Mateo,male,1970,1.8 +Diego,male,1970,1.9 +Arjun,male,1970,1.7 +Rahul,male,1970,1.6 +Chen,male,1970,1.7 +Min,male,1970,1.4 +Tariq,male,1970,1.3 +Emeka,male,1970,1.2 +Kwame,male,1970,1.1 +Luca,male,1970,1.4 +Andres,male,1970,1.3 +Youssef,male,1970,1.1 +Binh,male,1970,1.0 +Jennifer,female,1970,2.9 +Amy,female,1970,1.8 +Melissa,female,1970,1.6 +Natalia,female,1970,1.5 +Lucia,female,1970,1.5 +Priyanka,female,1970,1.3 +Neha,female,1970,1.2 +Yuna,female,1970,1.1 +Hyejin,female,1970,1.0 +Amina,female,1970,1.2 +Nkechi,female,1970,1.0 +Sara,female,1970,1.5 +Camila,female,1970,1.3 +Ines,female,1970,1.0 +Mariam,female,1970,1.1 +Ewa,female,1970,0.9 +Michael,male,1980,3.3 +Christopher,male,1980,2.7 +Matthew,male,1980,2.5 +Ahmed,male,1980,1.9 +Omar,male,1980,1.8 +Hassan,male,1980,1.5 +Alejandro,male,1980,1.9 +Ricardo,male,1980,1.6 +Vikram,male,1980,1.5 +Amit,male,1980,1.4 +Jun,male,1980,1.3 +Sung,male,1980,1.2 +Lucas,male,1980,1.6 +Thiago,male,1980,1.3 +Karim,male,1980,1.2 +Milan,male,1980,1.1 +Jessica,female,1980,2.9 +Jennifer,female,1980,2.3 +Amanda,female,1980,1.9 +Fatima,female,1980,1.7 +Aisha,female,1980,1.6 +Samira,female,1980,1.4 +Camila,female,1980,1.5 +Daniela,female,1980,1.3 +Ritu,female,1980,1.1 +Sonal,female,1980,1.0 +Yuki,female,1980,1.2 +Hana,female,1980,1.1 +Leah,female,1980,1.3 +Noor,female,1980,1.1 +Thao,female,1980,1.0 +Adele,female,1980,1.0 +Michael,male,1990,2.7 +Christopher,male,1990,2.1 +Joshua,male,1990,2.0 +Liam,male,1990,1.9 +Noah,male,1990,1.8 +Ethan,male,1990,1.6 +Mohammed,male,1990,1.6 +Ali,male,1990,1.4 +Mateo,male,1990,1.5 +Diego,male,1990,1.4 +Arjun,male,1990,1.4 +Wei,male,1990,1.2 +Leo,male,1990,1.3 +Yusuf,male,1990,1.2 +Kaito,male,1990,1.0 +Ivan,male,1990,1.0 +Jessica,female,1990,2.2 +Ashley,female,1990,2.0 +Samantha,female,1990,1.6 +Emma,female,1990,1.5 +Olivia,female,1990,1.4 +Ava,female,1990,1.3 +Fatima,female,1990,1.2 +Aisha,female,1990,1.1 +Priya,female,1990,1.1 +Zara,female,1990,1.1 +Sofia,female,1990,1.2 +Mia,female,1990,1.1 +Amara,female,1990,1.0 +Yara,female,1990,0.9 +Lina,female,1990,1.0 +Ines,female,1990,0.9 +Jacob,male,2000,1.7 +Michael,male,2000,1.5 +Ethan,male,2000,1.4 +Noah,male,2000,1.3 +Liam,male,2000,1.2 +Aiden,male,2000,1.1 +Muhammad,male,2000,1.1 +Omar,male,2000,1.0 +Rohan,male,2000,1.0 +Arjun,male,2000,0.9 +Jin,male,2000,0.8 +Tae,male,2000,0.8 +Mateo,male,2000,1.0 +Lucas,male,2000,1.0 +Zayd,male,2000,0.8 +Hugo,male,2000,0.8 +Emily,female,2000,1.3 +Madison,female,2000,1.2 +Hannah,female,2000,1.1 +Emma,female,2000,1.0 +Olivia,female,2000,0.9 +Abigail,female,2000,0.9 +Aisha,female,2000,0.8 +Fatima,female,2000,0.8 +Priya,female,2000,0.8 +Nour,female,2000,0.7 +Sana,female,2000,0.7 +Yuna,female,2000,0.7 +Amara,female,2000,0.8 +Leah,female,2000,0.8 +Camila,female,2000,0.8 +Mina,female,2000,0.7 +Liam,male,2010,1.0 +Noah,male,2010,0.9 +Ethan,male,2010,0.8 +Aiden,male,2010,0.8 +Mason,male,2010,0.7 +Alexander,male,2010,0.7 +Muhammad,male,2010,0.7 +Yusuf,male,2010,0.6 +Rayan,male,2010,0.6 +Arjun,male,2010,0.6 +Wei,male,2010,0.5 +Joon,male,2010,0.5 +Leo,male,2010,0.6 +Mateo,male,2010,0.6 +Kaito,male,2010,0.5 +Thiago,male,2010,0.5 +Sophia,female,2010,1.0 +Isabella,female,2010,0.9 +Emma,female,2010,0.9 +Olivia,female,2010,0.8 +Ava,female,2010,0.8 +Mia,female,2010,0.7 +Fatima,female,2010,0.7 +Zara,female,2010,0.7 +Aisha,female,2010,0.6 +Anaya,female,2010,0.6 +Yuna,female,2010,0.6 +Lina,female,2010,0.6 +Amara,female,2010,0.6 +Leah,female,2010,0.6 +Camila,female,2010,0.6 +Saanvi,female,2010,0.5 diff --git a/extropy/population/names/data/last_names.csv b/extropy/population/names/data/last_names.csv index fab2e7c..f2fa6e3 100644 --- a/extropy/population/names/data/last_names.csv +++ b/extropy/population/names/data/last_names.csv @@ -1,86 +1,118 @@ name,ethnicity,weight -Smith,white,2.442 -Johnson,white,1.932 -Williams,white,1.625 -Brown,white,1.437 -Jones,white,1.404 -Miller,white,1.238 -Davis,white,1.116 -Wilson,white,0.843 -Anderson,white,0.784 -Taylor,white,0.780 -Thomas,white,0.756 -Moore,white,0.713 -Martin,white,0.678 -Thompson,white,0.669 -Clark,white,0.551 -Lewis,white,0.530 -Robinson,white,0.502 -Walker,white,0.497 -Hall,white,0.494 -Young,white,0.480 -King,white,0.470 -Wright,white,0.459 -Baker,white,0.419 -Nelson,white,0.417 -Campbell,white,0.397 -Washington,black,0.903 -Jefferson,black,0.491 -Williams,black,2.400 -Jackson,black,1.735 -Harris,black,0.961 -Robinson,black,0.847 -Brown,black,1.220 -Jones,black,1.437 -Davis,black,0.935 -Johnson,black,1.600 -Thomas,black,0.821 -Wilson,black,0.750 -Moore,black,0.633 -Lewis,black,0.552 -Martin,black,0.541 -Taylor,black,0.589 -King,black,0.514 +Smith,white,2.300 +Johnson,white,1.900 +Williams,white,1.600 +Brown,white,1.400 +Jones,white,1.350 +Miller,white,1.200 +Davis,white,1.050 +Wilson,white,0.820 +Anderson,white,0.780 +Taylor,white,0.760 +Thomas,white,0.730 +Moore,white,0.700 +Martin,white,0.670 +Thompson,white,0.650 +Clark,white,0.530 +Lewis,white,0.510 +Robinson,white,0.490 +Walker,white,0.480 +Hall,white,0.470 +Young,white,0.460 +King,white,0.450 +Wright,white,0.430 +Baker,white,0.410 +Nelson,white,0.400 +Campbell,white,0.380 +Rossi,white,0.340 +Novak,white,0.320 +Ivanov,white,0.300 +Kowalski,white,0.280 +Schmidt,white,0.270 +Muller,white,0.260 +Silva,white,0.250 +Dubois,white,0.240 +Oconnor,white,0.220 +Papadopoulos,white,0.200 +Washington,black,0.900 +Jefferson,black,0.500 +Williams,black,2.200 +Jackson,black,1.700 +Harris,black,0.960 +Robinson,black,0.840 +Brown,black,1.180 +Jones,black,1.300 +Davis,black,0.910 +Johnson,black,1.500 +Thomas,black,0.810 +Wilson,black,0.730 +Moore,black,0.620 +Lewis,black,0.550 +Martin,black,0.540 +Taylor,black,0.580 +King,black,0.520 Thompson,black,0.460 -Anderson,black,0.445 -Walker,black,0.450 -Garcia,hispanic,3.309 -Rodriguez,hispanic,2.488 -Martinez,hispanic,2.269 -Hernandez,hispanic,2.164 -Lopez,hispanic,1.858 -Gonzalez,hispanic,1.659 -Perez,hispanic,1.361 -Sanchez,hispanic,1.232 -Ramirez,hispanic,1.193 -Torres,hispanic,0.974 -Flores,hispanic,0.937 -Rivera,hispanic,0.898 -Gomez,hispanic,0.643 -Diaz,hispanic,0.628 -Morales,hispanic,0.575 -Reyes,hispanic,0.566 -Gutierrez,hispanic,0.537 -Cruz,hispanic,0.477 -Ortiz,hispanic,0.455 -Castillo,hispanic,0.392 -Kim,asian,2.831 -Lee,asian,2.544 -Park,asian,1.365 -Nguyen,asian,2.459 -Chen,asian,1.839 -Wang,asian,1.605 -Li,asian,1.145 -Wu,asian,0.718 -Yang,asian,0.867 -Patel,asian,1.523 -Shah,asian,0.546 -Singh,asian,0.694 -Kumar,asian,0.456 -Chang,asian,0.552 -Lin,asian,0.641 -Liu,asian,0.658 -Huang,asian,0.621 -Zhang,asian,0.549 -Tran,asian,0.715 -Le,asian,0.399 +Anderson,black,0.440 +Walker,black,0.440 +Okafor,black,0.380 +Mensah,black,0.340 +Diallo,black,0.320 +Ndlovu,black,0.300 +Abebe,black,0.280 +Adebayo,black,0.260 +Garcia,hispanic,3.200 +Rodriguez,hispanic,2.450 +Martinez,hispanic,2.220 +Hernandez,hispanic,2.100 +Lopez,hispanic,1.820 +Gonzalez,hispanic,1.620 +Perez,hispanic,1.330 +Sanchez,hispanic,1.200 +Ramirez,hispanic,1.160 +Torres,hispanic,0.950 +Flores,hispanic,0.920 +Rivera,hispanic,0.880 +Gomez,hispanic,0.640 +Diaz,hispanic,0.620 +Morales,hispanic,0.570 +Reyes,hispanic,0.560 +Gutierrez,hispanic,0.530 +Cruz,hispanic,0.470 +Ortiz,hispanic,0.450 +Castillo,hispanic,0.390 +Vargas,hispanic,0.360 +Mendoza,hispanic,0.340 +Navarro,hispanic,0.320 +Rojas,hispanic,0.300 +Salazar,hispanic,0.280 +Kim,asian,2.700 +Lee,asian,2.500 +Park,asian,1.350 +Nguyen,asian,2.300 +Chen,asian,1.800 +Wang,asian,1.550 +Li,asian,1.120 +Wu,asian,0.700 +Yang,asian,0.850 +Patel,asian,1.500 +Shah,asian,0.540 +Singh,asian,0.690 +Kumar,asian,0.500 +Chang,asian,0.540 +Lin,asian,0.620 +Liu,asian,0.640 +Huang,asian,0.600 +Zhang,asian,0.560 +Tran,asian,0.700 +Le,asian,0.390 +Tanaka,asian,0.350 +Sato,asian,0.340 +Suzuki,asian,0.330 +Yamamoto,asian,0.300 +Choi,asian,0.290 +Khan,asian,0.500 +Rahman,asian,0.440 +Ali,asian,0.420 +Hassan,asian,0.380 +Bui,asian,0.320 +Pham,asian,0.300 diff --git a/extropy/population/network/__init__.py b/extropy/population/network/__init__.py index e6f03c6..a945e23 100644 --- a/extropy/population/network/__init__.py +++ b/extropy/population/network/__init__.py @@ -43,6 +43,7 @@ DegreeMultiplierConfig, EdgeTypeRule, InfluenceFactorConfig, + StructuralAttributeRoles, ) from .similarity import ( compute_similarity, @@ -76,6 +77,7 @@ "DegreeMultiplierConfig", "EdgeTypeRule", "InfluenceFactorConfig", + "StructuralAttributeRoles", # Result types "Edge", "NetworkResult", diff --git a/extropy/population/network/config.py b/extropy/population/network/config.py index bb09827..9cb6738 100644 --- a/extropy/population/network/config.py +++ b/extropy/population/network/config.py @@ -94,6 +94,23 @@ class InfluenceFactorConfig(BaseModel): description: str = "" +class StructuralAttributeRoles(BaseModel): + """Attribute roles used for deterministic structural edge generation. + + Each field should reference an attribute name from the sampled agent schema, + or be null when unavailable for a given population. + """ + + household_id: str | None = None + partner_id: str | None = None + age: str | None = None + sector: str | None = None + region: str | None = None + urbanicity: str | None = None + religion: str | None = None + dependents: str | None = None + + class NetworkConfig(BaseModel): """Complete configuration for network generation. @@ -123,6 +140,7 @@ class NetworkConfig(BaseModel): degree_multipliers: Multipliers for degree correction edge_type_rules: Rules for inferring edge types (evaluated by priority) influence_factors: Factors for computing asymmetric influence weights + structural_attribute_roles: Attribute-role mapping for structural edges default_edge_type: Fallback edge type when no rule matches ordinal_levels: Global ordinal level mappings (keyed by attribute name). Used by within_n match type when AttributeWeightConfig.ordinal_levels is None. @@ -175,6 +193,9 @@ class NetworkConfig(BaseModel): degree_multipliers: list[DegreeMultiplierConfig] = Field(default_factory=list) edge_type_rules: list[EdgeTypeRule] = Field(default_factory=list) influence_factors: list[InfluenceFactorConfig] = Field(default_factory=list) + structural_attribute_roles: StructuralAttributeRoles = Field( + default_factory=StructuralAttributeRoles + ) default_edge_type: str = "peer" ordinal_levels: dict[str, dict[str, int]] = Field(default_factory=dict) generated_from: str | None = None diff --git a/extropy/population/network/config_generator.py b/extropy/population/network/config_generator.py index db0a967..c4475d2 100644 --- a/extropy/population/network/config_generator.py +++ b/extropy/population/network/config_generator.py @@ -20,6 +20,7 @@ DegreeMultiplierConfig, EdgeTypeRule, InfluenceFactorConfig, + StructuralAttributeRoles, ) logger = logging.getLogger(__name__) @@ -208,6 +209,33 @@ "additionalProperties": False, } +STRUCTURAL_ROLE_SCHEMA = { + "type": "object", + "properties": { + "household_id": {"type": ["string", "null"]}, + "partner_id": {"type": ["string", "null"]}, + "age": {"type": ["string", "null"]}, + "sector": {"type": ["string", "null"]}, + "region": {"type": ["string", "null"]}, + "urbanicity": {"type": ["string", "null"]}, + "religion": {"type": ["string", "null"]}, + "dependents": {"type": ["string", "null"]}, + "reasoning": {"type": "string"}, + }, + "required": [ + "household_id", + "partner_id", + "age", + "sector", + "region", + "urbanicity", + "religion", + "dependents", + "reasoning", + ], + "additionalProperties": False, +} + def _build_prompt( population_spec: PopulationSpec, @@ -216,7 +244,8 @@ def _build_prompt( """Build the prompt for network config generation.""" # Summarize population attributes attr_lines = [] - for attr in population_spec.attributes: + max_prompt_attrs = 50 + for attr in population_spec.attributes[:max_prompt_attrs]: line = f" - {attr.name} ({attr.type})" if attr.category: line += f" [{attr.category}]" @@ -234,6 +263,10 @@ def _build_prompt( if dist_mean is not None: line += f" (mean={dist_mean})" attr_lines.append(line) + if len(population_spec.attributes) > max_prompt_attrs: + attr_lines.append( + f" - ... ({len(population_spec.attributes) - max_prompt_attrs} more attributes omitted)" + ) attr_summary = "\n".join(attr_lines) @@ -294,9 +327,82 @@ def _build_prompt( - For within_n match type, you MUST provide ordinal_levels mapping - For edge type conditions, use a_{{attribute}} and b_{{attribute}} syntax - For degree_multipliers condition_value, use the same type as the attribute (boolean/number/string) + """ + + +def _build_structural_role_prompt(population_spec: PopulationSpec) -> str: + """Build minimal prompt for structural-role mapping only.""" + max_prompt_attrs = 50 + attribute_names = [ + attr.name for attr in population_spec.attributes[:max_prompt_attrs] + ] + attr_lines = "\n".join(f"- {name}" for name in attribute_names) + omitted_note = "" + if len(population_spec.attributes) > max_prompt_attrs: + omitted = len(population_spec.attributes) - max_prompt_attrs + omitted_note = f"\nNote: {omitted} additional attributes are omitted for prompt compactness." + + return f"""Map structural network roles to exact attribute names from this population. + +Population: {population_spec.meta.description} +Geography/context: {population_spec.meta.geography or "unspecified"} + +Available attribute names: +{attr_lines} +{omitted_note} + +Roles to map (return exact attribute name or null): +- household_id: household grouping identifier +- partner_id: partner/spouse agent id reference +- age: person's age +- sector: occupation/employment sector +- region: location bucket (state/province/country/region) +- urbanicity: urban/rural style location bucket +- religion: faith/religious affiliation +- dependents: list of children/dependents + +Rules: +- NEVER invent names; choose only from Available attribute names. +- If role is not represented, return null. +- Keep this mapping pragmatic for structural edges: partner, household, coworker, neighbor, congregation, school_parent. """ +def _generate_structural_attribute_roles( + population_spec: PopulationSpec, + model: str | None = None, +) -> StructuralAttributeRoles: + """Generate structural-role mapping in a focused low-load call.""" + prompt = _build_structural_role_prompt(population_spec) + data = reasoning_call( + prompt=prompt, + response_schema=STRUCTURAL_ROLE_SCHEMA, + schema_name="structural_roles", + model=model, + reasoning_effort="low", + max_retries=1, + ) + + known_attrs = {attr.name for attr in population_spec.attributes} + roles: dict[str, str | None] = {} + for role_name in ( + "household_id", + "partner_id", + "age", + "sector", + "region", + "urbanicity", + "religion", + "dependents", + ): + value = data.get(role_name) + if isinstance(value, str) and value in known_attrs: + roles[role_name] = value + else: + roles[role_name] = None + return StructuralAttributeRoles.model_validate(roles) + + def _validate_config(data: dict, population_spec: PopulationSpec) -> list[str]: """Validate the LLM-generated config against the population spec. @@ -523,11 +629,9 @@ def generate_network_config( ) -> NetworkConfig: """Generate a NetworkConfig from a population spec using LLM reasoning. - The LLM analyzes the population's attributes and determines: - - Which attributes create social connections (homophily weights) - - Who are the natural hubs (degree multipliers) - - What types of relationships exist (edge type rules) - - Who influences whom (influence factors) + Uses two focused LLM calls: + 1) core network config (weights, multipliers, edge rules, influence, avg degree) + 2) structural attribute role mapping (household/partner/region/etc.) Args: population_spec: The population specification with attribute definitions @@ -553,13 +657,23 @@ def generate_network_config( ) config = _convert_to_network_config(data, population_spec) + config = config.model_copy( + update={ + "structural_attribute_roles": _generate_structural_attribute_roles( + population_spec, + model=model, + ) + } + ) logger.info( f"[NetworkConfigGen] Generated config: " f"{len(config.attribute_weights)} weights, " f"{len(config.degree_multipliers)} multipliers, " f"{len(config.edge_type_rules)} edge rules, " - f"{len(config.influence_factors)} influence factors" + f"{len(config.influence_factors)} influence factors, " + f"structural roles=" + f"{sum(1 for v in config.structural_attribute_roles.model_dump().values() if v)}" ) return config diff --git a/extropy/population/network/generator.py b/extropy/population/network/generator.py index 79edd80..3530c73 100644 --- a/extropy/population/network/generator.py +++ b/extropy/population/network/generator.py @@ -6,6 +6,7 @@ import json import logging import hashlib +import inspect import multiprocessing as mp import random import time @@ -1047,6 +1048,37 @@ def _acceptance_bounds( return deg_min, deg_max, clust_min, mod_min, mod_max +def _evaluate_topology_gate( + *, + metrics: dict[str, float], + deg_min: float, + deg_max: float, + cluster_min: float, + mod_min: float, + mod_max: float, + lcc_min: float, + min_edge_floor: int, +) -> tuple[bool, dict[str, float]]: + """Evaluate strict topology gate and return deltas vs configured bounds.""" + in_range = ( + deg_min <= metrics["avg_degree"] <= deg_max + and metrics["clustering"] >= cluster_min + and mod_min <= metrics["modularity"] <= mod_max + and metrics["largest_component_ratio"] >= lcc_min + and metrics["edge_count"] >= min_edge_floor + ) + deltas = { + "degree_to_min": metrics["avg_degree"] - deg_min, + "degree_to_max": deg_max - metrics["avg_degree"], + "clustering_to_min": metrics["clustering"] - cluster_min, + "modularity_to_min": metrics["modularity"] - mod_min, + "modularity_to_max": mod_max - metrics["modularity"], + "lcc_to_min": metrics["largest_component_ratio"] - lcc_min, + "edge_count_to_min": metrics["edge_count"] - min_edge_floor, + } + return in_range, deltas + + def _apply_scale_target_caps( config: NetworkConfig, n: int, @@ -1803,10 +1835,117 @@ def _compute_modularity_fast( return q +def _resolve_structural_keys_from_config( + agents: list[dict[str, Any]], + config: NetworkConfig, +) -> dict[str, str | None]: + """Resolve structural role keys from network config (LLM-selected roles first). + + Primary source: + - config.structural_attribute_roles + + Compatibility fallback (minimal): + - exact canonical field names only when role is unset or missing. + """ + populated_counts: Counter[str] = Counter() + for agent in agents: + for key, value in agent.items(): + if value is None: + continue + if isinstance(value, str) and not value.strip(): + continue + populated_counts[key] += 1 + + defaults: dict[str, tuple[str, ...]] = { + "household_id": ("household_id",), + "partner_id": ("partner_id", "partner_agent_id"), + "age": ("age",), + "sector": ("occupation_sector",), + "region": ("state", "country"), + "urbanicity": ("urban_rural",), + "religion": ("religious_affiliation",), + "dependents": ("dependents",), + } + + roles = config.structural_attribute_roles + resolved: dict[str, str | None] = {} + + for slot, fallback_keys in defaults.items(): + configured = getattr(roles, slot, None) + candidates: list[str] = [] + if configured: + candidates.append(configured) + for key in fallback_keys: + if key not in candidates: + candidates.append(key) + + present_candidates = [c for c in candidates if populated_counts.get(c, 0) > 0] + if not present_candidates: + resolved[slot] = None + continue + present_candidates.sort(key=lambda k: (-populated_counts[k], k)) + resolved[slot] = present_candidates[0] + + return resolved + + +def _bucket_token(value: Any) -> str | None: + """Convert a value into a stable bucket key token.""" + if value is None: + return None + if isinstance(value, str): + text = value.strip() + if not text: + return None + return text + return str(value) + + +def _dependent_is_school_age(dep: dict[str, Any]) -> bool: + """Detect whether dependent metadata indicates school-age child.""" + school_status_aliases = { + "school_status", + "school_level", + "education_stage", + "student_stage", + "grade_level", + } + school_labels = { + "elementary", + "middle_school", + "high_school", + "primary", + "secondary", + "k12", + "school", + } + for key, value in dep.items(): + if key.strip().lower() not in school_status_aliases: + continue + if isinstance(value, str): + status = value.strip().lower().replace("-", "_").replace(" ", "_") + if status in school_labels: + return True + + age_aliases = {"age", "age_years", "years_old", "current_age"} + age_value: Any = None + for key, value in dep.items(): + if key.strip().lower() in age_aliases: + age_value = value + break + + if isinstance(age_value, str) and age_value.strip().isdigit(): + age_value = int(age_value.strip()) + if isinstance(age_value, (int, float)): + return 5 <= int(age_value) <= 18 + return False + + def _generate_structural_edges( agents: list[dict[str, Any]], agent_ids: list[str], rng: random.Random, + config: NetworkConfig | None = None, ) -> list[Edge]: """Generate deterministic structural edges from agent attributes. @@ -1833,48 +1972,63 @@ def _add(i: int, j: int, etype: str, weight: float, context: str) -> None: ) ) + effective_config = config or NetworkConfig() + resolved_keys = _resolve_structural_keys_from_config(agents, effective_config) + household_key = resolved_keys.get("household_id") + partner_key = resolved_keys.get("partner_id") + age_key = resolved_keys.get("age") + sector_key = resolved_keys.get("sector") + region_key = resolved_keys.get("region") + urban_key = resolved_keys.get("urbanicity") + religion_key = resolved_keys.get("religion") + dependents_key = resolved_keys.get("dependents") + # Build indexes for batch matching household_map: dict[str, list[int]] = {} - sector_state_map: dict[tuple[str, str], list[int]] = {} - state_urban_map: dict[tuple[str, str], list[int]] = {} - religion_state_map: dict[tuple[str, str], list[int]] = {} + sector_region_map: dict[tuple[str, str], list[int]] = {} + region_urban_map: dict[tuple[str, str], list[int]] = {} + religion_region_map: dict[tuple[str, str], list[int]] = {} school_parent_map: dict[tuple[str, str], list[int]] = {} for idx, agent in enumerate(agents): - hh_id = agent.get("household_id") + hh_id = _bucket_token(agent.get(household_key)) if household_key else None if hh_id: household_map.setdefault(hh_id, []).append(idx) - sector = agent.get("occupation_sector") - state = agent.get("state") - urban = agent.get("urban_rural") + sector = _bucket_token(agent.get(sector_key)) if sector_key else None + region = _bucket_token(agent.get(region_key)) if region_key else None + urban = _bucket_token(agent.get(urban_key)) if urban_key else None + urban_bucket = urban if urban is not None else "__all__" - if sector and state: - sector_state_map.setdefault((sector, state), []).append(idx) - if state and urban: - state_urban_map.setdefault((state, urban), []).append(idx) + if sector and region: + sector_region_map.setdefault((sector, region), []).append(idx) + if region: + region_urban_map.setdefault((region, urban_bucket), []).append(idx) - religion = agent.get("religious_affiliation") + religion = _bucket_token(agent.get(religion_key)) if religion_key else None if ( religion - and state + and region and religion.lower() not in ("none", "atheist", "agnostic") ): - religion_state_map.setdefault((religion, state), []).append(idx) + religion_region_map.setdefault((religion, region), []).append(idx) # School parents: agents with school-age dependents - dependents = agent.get("dependents", []) + dependents_raw = ( + agent.get(dependents_key, []) + if dependents_key + else agent.get("dependents", []) + ) + dependents = dependents_raw if isinstance(dependents_raw, list) else [] has_school_kid = any( - isinstance(d, dict) - and d.get("school_status") in ("elementary", "middle_school", "high_school") - for d in dependents + isinstance(d, dict) and _dependent_is_school_age(d) for d in dependents ) - if has_school_kid and state and urban: - school_parent_map.setdefault((state, urban), []).append(idx) + if has_school_kid and region: + school_parent_map.setdefault((region, urban_bucket), []).append(idx) # 1. Partner edges (weight 1.0, max 1 per agent) for idx, agent in enumerate(agents): - partner_id = agent.get("partner_id") + partner_id = agent.get(partner_key) if partner_key else None if partner_id and partner_id in id_to_idx: j = id_to_idx[partner_id] _add(idx, j, "partner", 1.0, "household") @@ -1888,7 +2042,7 @@ def _add(i: int, j: int, etype: str, weight: float, context: str) -> None: # 3. Coworker edges (weight 0.6, capped at ~8 per agent) _MAX_COWORKER = 8 coworker_count: dict[int, int] = {} - for pool in sector_state_map.values(): + for pool in sector_region_map.values(): if len(pool) < 2: continue shuffled = list(pool) @@ -1908,7 +2062,7 @@ def _add(i: int, j: int, etype: str, weight: float, context: str) -> None: # 4. Neighbor edges (weight 0.4, capped at ~4, age within 15yr) _MAX_NEIGHBOR = 4 neighbor_count: dict[int, int] = {} - for pool in state_urban_map.values(): + for pool in region_urban_map.values(): if len(pool) < 2: continue shuffled = list(pool) @@ -1916,13 +2070,19 @@ def _add(i: int, j: int, etype: str, weight: float, context: str) -> None: for i_pos, i in enumerate(shuffled): if neighbor_count.get(i, 0) >= _MAX_NEIGHBOR: continue - age_i = agents[i].get("age", 40) + age_i_raw = agents[i].get(age_key) if age_key else None + if isinstance(age_i_raw, str) and age_i_raw.strip().isdigit(): + age_i_raw = int(age_i_raw.strip()) + age_i = age_i_raw if isinstance(age_i_raw, (int, float)) else 40 for j in shuffled[i_pos + 1 :]: if neighbor_count.get(j, 0) >= _MAX_NEIGHBOR: continue if neighbor_count.get(i, 0) >= _MAX_NEIGHBOR: break - age_j = agents[j].get("age", 40) + age_j_raw = agents[j].get(age_key) if age_key else None + if isinstance(age_j_raw, str) and age_j_raw.strip().isdigit(): + age_j_raw = int(age_j_raw.strip()) + age_j = age_j_raw if isinstance(age_j_raw, (int, float)) else 40 if abs(age_i - age_j) <= 15: _add(i, j, "neighbor", 0.4, "neighborhood") neighbor_count[i] = neighbor_count.get(i, 0) + 1 @@ -1931,7 +2091,7 @@ def _add(i: int, j: int, etype: str, weight: float, context: str) -> None: # 5. Congregation edges (weight 0.4, capped at ~4) _MAX_CONGREGATION = 4 congregation_count: dict[int, int] = {} - for pool in religion_state_map.values(): + for pool in religion_region_map.values(): if len(pool) < 2: continue shuffled = list(pool) @@ -2166,10 +2326,11 @@ def _stage_plan() -> list[dict[str, Any]]: elapsed_budget = max(1, int(config.max_calibration_minutes * 60)) stage_budget = max(30, elapsed_budget // max(1, len(stage_plan))) - accepted = False + calibration_accepted = False best_score = float("inf") best_edges: list[Edge] | None = None - best_metrics: dict[str, float] | None = None + calibration_best_metrics: dict[str, float] | None = None + calibration_best_communities: list[int] | None = None best_stage = "" final_candidate_mode = config.candidate_mode final_blocking_attrs: list[str] = [] @@ -2421,7 +2582,17 @@ def similarity_progress(_stage: str, current: int, total: int) -> None: # Step 3b: Generate structural edges first (structural-first ordering). # Structural edges consume part of the degree budget; similarity # calibration targets the remainder. - structural_edges = _generate_structural_edges(agents, agent_ids, rng) + structural_fn = _generate_structural_edges + sig = inspect.signature(structural_fn) + if "config" in sig.parameters or len(sig.parameters) >= 4: + structural_edges = structural_fn( + agents, + agent_ids, + rng, + config=stage_cfg, + ) + else: + structural_edges = structural_fn(agents, agent_ids, rng) structural_pair_set: set[tuple[str, str]] = set() for se in structural_edges: structural_pair_set.add((se.source, se.target)) @@ -2584,7 +2755,8 @@ def similarity_progress(_stage: str, current: int, total: int) -> None: if score < best_score: best_score = score best_edges = edges - best_metrics = metrics_payload + calibration_best_metrics = metrics_payload + calibration_best_communities = list(communities) best_stage = stage_name if calibration_run_id and study_db_file is not None: @@ -2621,7 +2793,7 @@ def similarity_progress(_stage: str, current: int, total: int) -> None: if in_range: stage_accepted = True - accepted = True + calibration_accepted = True if calibration_run_id and study_db_file is not None: with open_study_db(study_db_file) as db: db.complete_network_calibration_run( @@ -2736,14 +2908,49 @@ def similarity_progress(_stage: str, current: int, total: int) -> None: ) emit_progress("Rewiring edges", len(edges), len(edges), force_db=True) - quality_deltas = { - "degree_to_min": (best_metrics or {}).get("avg_degree", 0.0) - deg_min, - "degree_to_max": deg_max - (best_metrics or {}).get("avg_degree", 0.0), - "clustering_to_min": (best_metrics or {}).get("clustering", 0.0) - cluster_min, - "modularity_to_min": (best_metrics or {}).get("modularity", 0.0) - mod_min, - "modularity_to_max": mod_max - (best_metrics or {}).get("modularity", 0.0), - "lcc_to_min": (best_metrics or {}).get("largest_component_ratio", 0.0) + final_adjacency = _build_adjacency_from_edges(edges, agent_ids, n) + final_metrics = { + "edge_count": len(edges), + "avg_degree": (2 * len(edges) / n) if n > 0 else 0.0, + "clustering": _compute_avg_clustering(final_adjacency, n), + "largest_component_ratio": _compute_largest_component_ratio(final_adjacency, n), + "modularity": 0.0, + } + if calibration_best_communities and len(calibration_best_communities) == n: + final_metrics["modularity"] = _compute_modularity_fast( + edges, agent_ids, calibration_best_communities + ) + elif calibration_best_metrics is not None: + final_metrics["modularity"] = calibration_best_metrics.get("modularity", 0.0) + + final_accepted, quality_deltas = _evaluate_topology_gate( + metrics=final_metrics, + deg_min=deg_min, + deg_max=deg_max, + cluster_min=cluster_min, + mod_min=mod_min, + mod_max=mod_max, + lcc_min=config.target_largest_component_ratio, + min_edge_floor=min_edge_floor, + ) + + calibration_quality_deltas = { + "degree_to_min": (calibration_best_metrics or {}).get("avg_degree", 0.0) + - deg_min, + "degree_to_max": deg_max + - (calibration_best_metrics or {}).get("avg_degree", 0.0), + "clustering_to_min": (calibration_best_metrics or {}).get("clustering", 0.0) + - cluster_min, + "modularity_to_min": (calibration_best_metrics or {}).get("modularity", 0.0) + - mod_min, + "modularity_to_max": mod_max + - (calibration_best_metrics or {}).get("modularity", 0.0), + "lcc_to_min": (calibration_best_metrics or {}).get( + "largest_component_ratio", 0.0 + ) - config.target_largest_component_ratio, + "edge_count_to_min": (calibration_best_metrics or {}).get("edge_count", 0.0) + - min_edge_floor, } meta = { @@ -2766,11 +2973,14 @@ def similarity_progress(_stage: str, current: int, total: int) -> None: }, "quality": { "topology_gate": config.topology_gate, - "accepted": accepted, + "accepted": final_accepted, "best_score": best_score, - "best_metrics": best_metrics or {}, + "best_metrics": calibration_best_metrics or {}, + "final_metrics": final_metrics, + "calibration_accepted": calibration_accepted, "best_stage": best_stage, "gate_deltas": quality_deltas, + "calibration_gate_deltas": calibration_quality_deltas, "target_caps_applied": target_caps, "bounds": { "degree_min": deg_min, diff --git a/extropy/population/persona/__init__.py b/extropy/population/persona/__init__.py index 27d8c1b..68140b4 100644 --- a/extropy/population/persona/__init__.py +++ b/extropy/population/persona/__init__.py @@ -22,6 +22,7 @@ from .generator import generate_persona_config, PersonaConfigError from .renderer import render_persona, render_persona_section, preview_persona from .stats import compute_population_stats +from .validator import validate_persona_config __all__ = [ # Config models @@ -45,4 +46,6 @@ "preview_persona", # Stats "compute_population_stats", + # Validator + "validate_persona_config", ] diff --git a/extropy/population/persona/renderer.py b/extropy/population/persona/renderer.py index 34dba55..48db220 100644 --- a/extropy/population/persona/renderer.py +++ b/extropy/population/persona/renderer.py @@ -126,17 +126,40 @@ def _format_boolean_value(value: Any, phrasing: BooleanPhrasing) -> str: # Handle various boolean representations if isinstance(value, bool): - return phrasing.true_phrase if value else phrasing.false_phrase + phrase = phrasing.true_phrase if value else phrasing.false_phrase + return _sanitize_boolean_phrase(phrase, value) if isinstance(value, str): - return ( - phrasing.true_phrase - if value.lower() in ("true", "yes", "1") - else phrasing.false_phrase - ) + truthy = value.lower() in ("true", "yes", "1") + phrase = phrasing.true_phrase if truthy else phrasing.false_phrase + return _sanitize_boolean_phrase(phrase, truthy) if isinstance(value, (int, float)): - return phrasing.true_phrase if value else phrasing.false_phrase + truthy = bool(value) + phrase = phrasing.true_phrase if truthy else phrasing.false_phrase + return _sanitize_boolean_phrase(phrase, truthy) + + return _sanitize_boolean_phrase(phrasing.false_phrase, False) + + +def _sanitize_boolean_phrase(phrase: str, truthy: bool) -> str: + """Deterministically sanitize boolean phrases for rendering.""" + clean = phrase.strip() + if not clean: + return "I do." if truthy else "I do not." + + raw_token_map = { + "true": "I do.", + "yes": "I do.", + "false": "I do not.", + "no": "I do not.", + } + token_key = clean.lower() + if token_key in raw_token_map: + return raw_token_map[token_key] - return phrasing.false_phrase + lowered = clean.lower() + if not lowered.startswith(("i ", "i'", "i’m", "i'm", "my ")): + clean = f"I {clean[0].lower()}{clean[1:]}" if len(clean) > 1 else f"I {clean}" + return clean def _format_categorical_value(value: Any, phrasing: CategoricalPhrasing) -> str: @@ -196,7 +219,16 @@ def render_attribute(attr_name: str, value: Any, config: PersonaConfig) -> str: elif isinstance(phrasing, CategoricalPhrasing): return _format_categorical_value(value, phrasing) elif isinstance(phrasing, RelativePhrasing): - return _format_relative_value(value, phrasing, config) + relative = _format_relative_value(value, phrasing, config) + concrete = next( + (p for p in config.phrasings.concrete if p.attribute == attr_name), + None, + ) + if concrete and value is not None: + concrete_text = _format_concrete_value(value, concrete) + if concrete_text: + return f"{relative} ({concrete_text})" + return relative elif isinstance(phrasing, ConcretePhrasing): return _format_concrete_value(value, phrasing) @@ -589,11 +621,11 @@ def render_persona( if not group_obj: continue - # render_intro() already emits "## Who I Am" — skip duplicate + # render_intro() already emits "## Who I Am" — skip duplicate entirely if group_obj.label == "Who I Am": - lines = [""] - else: - lines = [f"## {group_obj.label}", ""] + continue + + lines = [f"## {group_obj.label}", ""] phrases = [] for attr_name in remaining_attrs: value = agent.get(attr_name) diff --git a/extropy/population/persona/validator.py b/extropy/population/persona/validator.py new file mode 100644 index 0000000..26046cd --- /dev/null +++ b/extropy/population/persona/validator.py @@ -0,0 +1,195 @@ +"""Persona config validation against merged population attributes.""" + +from __future__ import annotations + +import re + +from ...core.models import PopulationSpec, CategoricalDistribution +from ...core.models.validation import ValidationResult +from .config import PersonaConfig +from .renderer import extract_intro_attributes + + +def _normalize_token(value: str) -> str: + token = value.strip().lower() + token = re.sub(r"[\s\-]+", "_", token) + token = re.sub(r"[^a-z0-9_]", "", token) + token = re.sub(r"_+", "_", token).strip("_") + return token + + +def validate_persona_config( + spec: PopulationSpec, config: PersonaConfig +) -> ValidationResult: + """Validate persona config coverage and phrase completeness.""" + result = ValidationResult() + attr_map = {attr.name: attr for attr in spec.attributes} + attr_names = set(attr_map.keys()) + + treatment_attrs = [t.attribute for t in config.treatments] + treatment_set = set(treatment_attrs) + + # Treatments must cover merged attributes exactly once. + missing_treatments = sorted(attr_names - treatment_set) + unknown_treatments = sorted(treatment_set - attr_names) + duplicate_treatments = sorted( + name for name in treatment_set if treatment_attrs.count(name) > 1 + ) + + for name in missing_treatments: + result.add_error( + category="PERSONA_COVERAGE", + location="treatments", + message=f"missing treatment for attribute '{name}'", + suggestion="add treatment entry for this attribute", + ) + for name in unknown_treatments: + result.add_error( + category="PERSONA_COVERAGE", + location="treatments", + message=f"treatment references unknown attribute '{name}'", + ) + for name in duplicate_treatments: + result.add_error( + category="PERSONA_COVERAGE", + location="treatments", + message=f"duplicate treatment entries for '{name}'", + ) + + # Group membership completeness and uniqueness. + group_member_counts: dict[str, int] = {} + for group in config.groups: + for attr_name in group.attributes: + if attr_name not in attr_names: + result.add_error( + category="PERSONA_GROUP", + location=f"groups.{group.name}", + message=f"group references unknown attribute '{attr_name}'", + ) + continue + group_member_counts[attr_name] = group_member_counts.get(attr_name, 0) + 1 + + for attr_name in sorted(attr_names): + count = group_member_counts.get(attr_name, 0) + if count == 0: + result.add_error( + category="PERSONA_GROUP", + location="groups", + message=f"attribute '{attr_name}' is not assigned to any group", + ) + elif count > 1: + result.add_error( + category="PERSONA_GROUP", + location="groups", + message=f"attribute '{attr_name}' appears in multiple groups", + ) + + # Intro template references must resolve to known attributes. + intro_refs = extract_intro_attributes(config.intro_template) + unknown_intro_refs = sorted(intro_refs - attr_names) + for ref in unknown_intro_refs: + result.add_error( + category="PERSONA_INTRO", + location="intro_template", + message=f"intro_template references unknown attribute '{ref}'", + ) + + # Build phrasing lookup maps. + bool_map = {p.attribute: p for p in config.phrasings.boolean} + cat_map = {p.attribute: p for p in config.phrasings.categorical} + rel_map = {p.attribute: p for p in config.phrasings.relative} + conc_map = {p.attribute: p for p in config.phrasings.concrete} + + treatment_lookup = {t.attribute: t.treatment.value for t in config.treatments} + + for attr in spec.attributes: + attr_name = attr.name + + if attr.type == "boolean": + phr = bool_map.get(attr_name) + if phr is None: + result.add_error( + category="PERSONA_PHRASING", + location="phrasings.boolean", + message=f"missing boolean phrasing for '{attr_name}'", + ) + else: + if not phr.true_phrase.strip() or not phr.false_phrase.strip(): + result.add_error( + category="PERSONA_PHRASING", + location=f"phrasings.boolean.{attr_name}", + message="boolean phrasing must define non-empty true/false phrases", + ) + raw_tokens = {"true", "false", "yes", "no"} + if phr.true_phrase.strip().lower() in raw_tokens: + result.add_warning( + category="PERSONA_PHRASING", + location=f"phrasings.boolean.{attr_name}", + message="true_phrase is raw token-like; use natural first-person text", + ) + if phr.false_phrase.strip().lower() in raw_tokens: + result.add_warning( + category="PERSONA_PHRASING", + location=f"phrasings.boolean.{attr_name}", + message="false_phrase is raw token-like; use natural first-person text", + ) + + if attr.type == "categorical": + phr = cat_map.get(attr_name) + if phr is None: + result.add_error( + category="PERSONA_PHRASING", + location="phrasings.categorical", + message=f"missing categorical phrasing for '{attr_name}'", + ) + else: + dist = attr.sampling.distribution + if isinstance(dist, CategoricalDistribution): + expected = {_normalize_token(opt) for opt in dist.options} + actual = {_normalize_token(opt) for opt in phr.phrases.keys()} + if expected != actual: + missing = sorted(expected - actual) + extra = sorted(actual - expected) + detail = [] + if missing: + detail.append(f"missing={missing}") + if extra: + detail.append(f"extra={extra}") + result.add_error( + category="PERSONA_PHRASING", + location=f"phrasings.categorical.{attr_name}", + message="categorical option coverage mismatch: " + + ", ".join(detail), + ) + + treatment = treatment_lookup.get(attr_name) + if treatment == "relative" and attr_name not in rel_map: + result.add_error( + category="PERSONA_PHRASING", + location="phrasings.relative", + message=f"missing relative phrasing for '{attr_name}'", + ) + if ( + treatment == "concrete" + and attr.type in {"int", "float"} + and attr_name not in conc_map + ): + result.add_error( + category="PERSONA_PHRASING", + location="phrasings.concrete", + message=f"missing concrete phrasing for '{attr_name}'", + ) + + # Float treatment should support mixed rendering quality. + if ( + attr.type == "float" + and treatment == "relative" + and attr_name not in conc_map + ): + result.add_warning( + category="PERSONA_PHRASING", + location=f"phrasings.concrete.{attr_name}", + message="relative float has no concrete fallback template", + ) + + return result diff --git a/extropy/population/sampler/modifiers.py b/extropy/population/sampler/modifiers.py index 54c2e5f..7ca6b2b 100644 --- a/extropy/population/sampler/modifiers.py +++ b/extropy/population/sampler/modifiers.py @@ -39,7 +39,7 @@ def apply_modifiers_and_sample( modifiers: list[Modifier], rng: random.Random, agent: dict[str, Any], -) -> tuple[Any, list[int]]: +) -> tuple[Any, list[int], list[str]]: """ Apply matching modifiers to a distribution and sample. @@ -50,9 +50,10 @@ def apply_modifiers_and_sample( agent: Current agent's already-sampled attribute values Returns: - Tuple of (sampled_value, list of indices of triggered modifiers) + Tuple of (sampled_value, list of indices of triggered modifiers, condition warnings) """ triggered_indices: list[int] = [] + condition_warnings: list[str] = [] # Check which modifiers apply matching_modifiers: list[tuple[int, Modifier]] = [] @@ -62,8 +63,9 @@ def apply_modifiers_and_sample( matching_modifiers.append((i, mod)) triggered_indices.append(i) except Exception as e: - # Log warning but continue - condition failure means modifier doesn't apply - logger.warning(f"Modifier condition '{mod.when}' failed: {e}") + warning = f"modifier[{i}] when='{mod.when}' eval failed: {e}" + condition_warnings.append(warning) + logger.warning(warning) # Route to type-specific handler if isinstance(dist, (NormalDistribution, LognormalDistribution)): @@ -79,7 +81,7 @@ def apply_modifiers_and_sample( else: raise ValueError(f"Unknown distribution type: {type(dist)}") - return value, triggered_indices + return value, triggered_indices, condition_warnings def _apply_numeric_modifiers( diff --git a/extropy/population/spec_builder/sufficiency.py b/extropy/population/spec_builder/sufficiency.py index 2d11235..e346254 100644 --- a/extropy/population/spec_builder/sufficiency.py +++ b/extropy/population/spec_builder/sufficiency.py @@ -19,10 +19,6 @@ "type": ["string", "null"], "description": "Geographic scope if mentioned (e.g., 'Germany', 'US', 'California')", }, - "agent_focus": { - "type": ["string", "null"], - "description": "Natural language description of who should be simulated (e.g., 'families', 'surgeons', 'couples'). Used for documentation.", - }, "clarifications_needed": { "type": "array", "items": {"type": "string"}, @@ -63,7 +59,6 @@ "required": [ "sufficient", "geography", - "agent_focus", "clarifications_needed", "questions", ], @@ -102,25 +97,11 @@ def check_sufficiency( - Extract if mentioned (e.g., "German surgeons" → Germany) - Can be country, region, or city -3. AGENT FOCUS - who should be simulated as active agents? - This controls how households are sampled: - - "families" or "households" → everyone in household gets simulated (both partners, older kids) - Use for: communities, neighborhoods, social dynamics, local issues - - "couples" or "partners" → both adults simulated, children are background NPCs - Use for: relationship studies, retirement planning, couple decisions - - Specific roles like "surgeons", "subscribers" → one person per household, partner/kids are NPCs - Use for: professional studies, product research, individual behavior - - Examples: - - "community reacting to school policy" → "families" (both parents may have opinions) - - "German surgeons" → "surgeons" (study is about the surgeon, not their family) - - "retired couples planning travel" → "couples" (both partners matter) - If the description is too vague to create meaningful attributes, mark as insufficient and provide BOTH: 1. `clarifications_needed`: Simple list of question strings (for backward compat) 2. `questions`: Structured questions with these fields: - - id: snake_case identifier (e.g., "geography", "agent_focus") + - id: snake_case identifier (e.g., "geography") - question: Human-readable question text - type: "single_choice" (with options), "text" (free text), or "number" - options: Array of choices for single_choice (null for text/number) @@ -160,7 +141,6 @@ def check_sufficiency( return SufficiencyResult( sufficient=data.get("sufficient", False), geography=data.get("geography"), - agent_focus=data.get("agent_focus"), clarifications_needed=data.get("clarifications_needed", []), questions=questions, ) diff --git a/extropy/population/validator/semantic.py b/extropy/population/validator/semantic.py index 92589ab..234fc38 100644 --- a/extropy/population/validator/semantic.py +++ b/extropy/population/validator/semantic.py @@ -13,6 +13,7 @@ UniformDistribution, BetaDistribution, CategoricalDistribution, + BooleanDistribution, ) from ...utils import extract_comparisons_from_expression @@ -29,6 +30,7 @@ def run_semantic_checks(spec: PopulationSpec) -> list[ValidationIssue]: 10. No-Op Detection 11. Modifier Stacking Analysis 12. Condition Value Validity + 13. Last-Wins Overlap Detection """ issues: list[ValidationIssue] = [] @@ -45,6 +47,9 @@ def run_semantic_checks(spec: PopulationSpec) -> list[ValidationIssue]: # Category 12: Condition Value Validity issues.extend(_check_condition_values(attr, attr_lookup)) + # Category 13: Last-Wins overlap detection for categorical/boolean + issues.extend(_check_last_wins_overlaps(attr)) + return issues @@ -248,3 +253,71 @@ def _check_condition_values( ) return issues + + +# ============================================================================= +# Category 13: Last-Wins Overlap Detection +# ============================================================================= + + +def _check_last_wins_overlaps(attr: AttributeSpec) -> list[ValidationIssue]: + """Warn when categorical/boolean modifiers may overlap under last-wins semantics.""" + issues: list[ValidationIssue] = [] + dist = attr.sampling.distribution + modifiers = attr.sampling.modifiers + if not dist or not modifiers or len(modifiers) < 2: + return issues + + is_last_wins_categorical = isinstance(dist, CategoricalDistribution) + is_last_wins_boolean = isinstance(dist, BooleanDistribution) + if not (is_last_wins_categorical or is_last_wins_boolean): + return issues + + def normalize_when(expr: str) -> str: + return expr.strip().lower().replace(" ", "") + + def is_unconditional(expr: str) -> bool: + normalized = normalize_when(expr) + return normalized in {"true", "1==1", "(true)"} + + def compared_pairs(expr: str) -> set[tuple[str, str]]: + pairs: set[tuple[str, str]] = set() + for attr_name, values in extract_comparisons_from_expression(expr): + for value in values: + pairs.add((attr_name, str(value))) + return pairs + + for i in range(len(modifiers)): + for j in range(i + 1, len(modifiers)): + left = modifiers[i].when + right = modifiers[j].when + if not left or not right: + continue + + may_overlap = False + if is_unconditional(left) or is_unconditional(right): + may_overlap = True + elif normalize_when(left) == normalize_when(right): + may_overlap = True + else: + left_pairs = compared_pairs(left) + right_pairs = compared_pairs(right) + if not left_pairs or not right_pairs: + may_overlap = True + elif left_pairs.intersection(right_pairs): + may_overlap = True + + if may_overlap: + issues.append( + ValidationIssue( + severity=Severity.WARNING, + category="MODIFIER_OVERLAP", + location=attr.name, + message=( + f"modifiers {i} and {j} may overlap under last-wins semantics" + ), + suggestion="Make conditions mutually exclusive or merge the modifier logic", + ) + ) + + return issues diff --git a/extropy/scenario/__init__.py b/extropy/scenario/__init__.py index c361d04..0c3de93 100644 --- a/extropy/scenario/__init__.py +++ b/extropy/scenario/__init__.py @@ -37,7 +37,6 @@ ExposureRule, SeedExposure, # Interaction - InteractionType, InteractionConfig, SpreadModifier, SpreadConfig, @@ -51,6 +50,7 @@ # Scenario ScenarioMeta, ScenarioSpec, + SamplingSemanticRoles, # Validation Severity, ValidationIssue, @@ -81,7 +81,6 @@ "ExposureRule", "SeedExposure", # Models - Interaction - "InteractionType", "InteractionConfig", "SpreadModifier", "SpreadConfig", @@ -95,6 +94,7 @@ # Models - Scenario "ScenarioMeta", "ScenarioSpec", + "SamplingSemanticRoles", # Models - Validation "Severity", "ValidationIssue", diff --git a/extropy/scenario/compiler.py b/extropy/scenario/compiler.py index af55106..6918df2 100644 --- a/extropy/scenario/compiler.py +++ b/extropy/scenario/compiler.py @@ -11,9 +11,11 @@ import re from datetime import datetime from pathlib import Path +from typing import Literal from ..core.models import ( IdentityDimension, + HouseholdConfig, PopulationSpec, ScenarioMeta, ScenarioSimConfig, @@ -26,6 +28,7 @@ from .exposure import generate_seed_exposure from .interaction import determine_interaction_model from .timeline import generate_timeline_and_outcomes +from .sampling_semantics import generate_sampling_semantic_roles from ..utils.callbacks import StepProgressCallback from .validator import validate_scenario from ..storage import open_study_db @@ -335,6 +338,8 @@ def progress(step: str, status: str): ) # Assemble full spec + sampling_semantic_roles = generate_sampling_semantic_roles(population_spec) + spec = ScenarioSpec( meta=meta, event=event, @@ -345,6 +350,7 @@ def progress(step: str, status: str): outcomes=outcome_config, simulation=simulation_config, background_context=background_context, + sampling_semantic_roles=sampling_semantic_roles, ) # ========================================================================= @@ -371,6 +377,8 @@ def create_scenario_spec( description: str, population_spec: PopulationSpec, extended_attributes: list | None = None, + household_config: HouseholdConfig | None = None, + agent_focus_mode: Literal["primary_only", "couples", "all"] | None = None, on_progress: StepProgressCallback | None = None, timeline_mode: str | None = None, timestep_unit_override: str | None = None, @@ -387,6 +395,8 @@ def create_scenario_spec( description: Natural language scenario description population_spec: Loaded population spec extended_attributes: Optional list of extended AttributeSpecs from scenario + household_config: Optional household configuration for scenario-owned household semantics + agent_focus_mode: Household agent scope (primary_only/couples/all) on_progress: Optional callback(step, status) for progress updates timeline_mode: Timeline mode override. None = auto-detect. timestep_unit_override: CLI override for timestep unit (e.g. "month"). @@ -400,23 +410,27 @@ def progress(step: str, status: str): if on_progress: on_progress(step, status) + ext_attrs = list(extended_attributes or []) + merged_population = population_spec + if ext_attrs: + merged_population = PopulationSpec( + meta=population_spec.meta.model_copy(), + grounding=population_spec.grounding, + attributes=list(population_spec.attributes) + ext_attrs, + sampling_order=population_spec.merged_sampling_order(ext_attrs), + ) + # Step 1: Parse scenario description progress("1/5", "Parsing event definition...") - event = parse_scenario(description, population_spec) + event = parse_scenario(description, merged_population) - # Step 2: Generate seed exposure (generic, without network specifics) + # Step 2: Generate seed exposure without assumed edge types. progress("2/5", "Generating seed exposure rules...") - # Create a minimal network summary - exposure will use generic rules - # Node count isn't needed for generic rules; will be determined at sample time - generic_network_summary = { - "edge_types": ["colleague", "friend", "family"], # Generic types - } - seed_exposure = generate_seed_exposure( event, - population_spec, - generic_network_summary, + merged_population, + None, ) # Step 3: Determine interaction model @@ -424,8 +438,8 @@ def progress(step: str, status: str): interaction_config, spread_config = determine_interaction_model( event, - population_spec, - generic_network_summary, + merged_population, + None, ) # Step 4: Generate timeline, outcomes, and background context @@ -444,7 +458,7 @@ def progress(step: str, status: str): ) ) - # Step 5: Detect identity dimensions + # Step 5: Detect identity dimensions + sampling semantics progress("5/5", "Detecting identity dimensions...") identity_dimensions = _detect_identity_dimensions( @@ -452,6 +466,7 @@ def progress(step: str, status: str): event_content=event.content, background_context=background_context, ) + sampling_semantic_roles = generate_sampling_semantic_roles(merged_population) # Assemble scenario spec scenario_name = _generate_scenario_name(description) @@ -473,16 +488,18 @@ def progress(step: str, status: str): simulation=simulation_config, background_context=background_context, identity_dimensions=identity_dimensions if identity_dimensions else None, + sampling_semantic_roles=sampling_semantic_roles, + extended_attributes=ext_attrs, + household_config=household_config, + agent_focus_mode=agent_focus_mode, ) - # Store extended attributes if provided - if extended_attributes: - spec.extended_attributes = extended_attributes - - # Light validation (no agents/network to validate against) - from ..core.models.validation import ValidationResult as VResult - - validation_result = VResult(valid=True, errors=[], warnings=[]) + validation_result = validate_scenario( + spec=spec, + population_spec=merged_population, + agent_count=None, + network=None, + ) return spec, validation_result diff --git a/extropy/scenario/exposure.py b/extropy/scenario/exposure.py index a2939b5..70bca56 100644 --- a/extropy/scenario/exposure.py +++ b/extropy/scenario/exposure.py @@ -32,17 +32,16 @@ "type": "string", "description": "Human-readable description", }, - "reach": { - "type": "string", - "enum": ["broadcast", "targeted", "organic"], - "description": "How the channel reaches agents", - }, "credibility_modifier": { "type": "number", "description": "How channel affects perceived credibility (1.0 = no change)", }, + "experience_template": { + "type": "string", + "description": "Optional first-person phrasing for how an agent receives this channel", + }, }, - "required": ["name", "description", "reach", "credibility_modifier"], + "required": ["name", "description", "credibility_modifier"], "additionalProperties": False, }, "minItems": 1, @@ -166,20 +165,8 @@ def generate_seed_exposure( ## Channels Define 2-5 exposure channels appropriate for this scenario. - -**Channel Types:** - -1. **broadcast** - Reaches all agents meeting criteria (email blasts, TV news) - - Use for official communications, mass media - - High reach, consistent timing - -2. **targeted** - Reaches specific subgroups (targeted ads, professional networks) - - Use for demographic-specific channels - - Medium reach, can be very specific - -3. **organic** - Spreads through existing connections (word of mouth, social media shares) - - Use for informal information spread - - Variable reach, depends on network +Include optional `experience_template` where useful. +Example: "I saw a segment about this on local news." **Credibility Modifiers:** - 1.0 = Channel doesn't affect credibility @@ -253,8 +240,8 @@ def generate_seed_exposure( channel = ExposureChannel( name=ch_data["name"], description=ch_data["description"], - reach=ch_data["reach"], credibility_modifier=ch_data.get("credibility_modifier", 1.0), + experience_template=ch_data.get("experience_template"), ) channels.append(channel) diff --git a/extropy/scenario/interaction.py b/extropy/scenario/interaction.py index d529667..d6d969c 100644 --- a/extropy/scenario/interaction.py +++ b/extropy/scenario/interaction.py @@ -9,7 +9,6 @@ PopulationSpec, Event, InteractionConfig, - InteractionType, SpreadConfig, SpreadModifier, ) @@ -19,30 +18,9 @@ INTERACTION_MODEL_SCHEMA = { "type": "object", "properties": { - "primary_model": { - "type": "string", - "enum": [ - "passive_observation", - "direct_conversation", - "broadcast_response", - "deliberative", - ], - "description": "Primary interaction model", - }, - "secondary_model": { - "type": "string", - "enum": [ - "passive_observation", - "direct_conversation", - "broadcast_response", - "deliberative", - "none", - ], - "description": "Secondary interaction model (or 'none')", - }, "interaction_description": { "type": "string", - "description": "How interactions work in this scenario", + "description": "Optional human-readable notes about social dynamics", }, "share_probability": { "type": "number", @@ -89,8 +67,6 @@ }, }, "required": [ - "primary_model", - "secondary_model", "interaction_description", "share_probability", "share_modifiers", @@ -112,10 +88,8 @@ def determine_interaction_model( """ Determine how agents will interact about the event. - Selects: - - Primary interaction model (how agents communicate) - - Optional secondary model (for blended scenarios) - - Spread configuration (how information propagates) + Selects spread configuration (how information propagates) plus optional + descriptive interaction notes. Args: event: The parsed event definition @@ -129,8 +103,8 @@ def determine_interaction_model( Example: >>> interaction, spread = determine_interaction_model(event, population_spec) - >>> interaction.primary_model - + >>> interaction.description + 'Neighbors primarily discuss through informal chats and online posts' >>> spread.share_probability 0.35 """ @@ -175,43 +149,10 @@ def determine_interaction_model( {attribute_info} {edge_type_info} -## Interaction Models - -Choose the most appropriate interaction model(s): - -### 1. passive_observation -Social media style — agents see content/reactions, form opinions passively. -- Low direct engagement -- Influenced by what they observe others doing -- Good for: consumer products, viral content, social media events - -### 2. direct_conversation -One-on-one or small group discussions. -- High engagement -- Opinions can change through dialogue -- Good for: professional topics, personal decisions, complex issues - -### 3. broadcast_response -Authority/source broadcasts, agents react and discuss reactions. -- Initial broadcast phase, then discussion -- Agents compare reactions with their network -- Good for: official announcements, policy changes, emergencies - -### 4. deliberative -Group deliberation with multiple rounds. -- Structured discussion phases -- Opinions evolve through deliberation -- Good for: community decisions, policy debates, consensus-building - -## Selection Heuristics +## Interaction Notes -| Population Type | Event Type | Likely Model | -|-----------------|------------|---------------| -| Consumers | Price change | passive_observation + direct_conversation | -| Professionals | New technology | direct_conversation | -| Community | Government policy | broadcast_response | -| Any | Emergency | broadcast_response | -| Civic/Political | Policy debate | deliberative | +Provide a short `interaction_description` describing likely social dynamics. +This field is informational only; runtime behavior is controlled by spread rules below. ## Spread Configuration @@ -253,8 +194,7 @@ def determine_interaction_model( ## Output -Provide interaction model selection and spread configuration. -For secondary_model, use "none" if only one model applies.""" +Provide interaction notes and spread configuration.""" data = reasoning_call( prompt=prompt, @@ -264,11 +204,8 @@ def determine_interaction_model( reasoning_effort=reasoning_effort, ) - # Parse interaction config - secondary = data.get("secondary_model", "none") + # Parse interaction config (informational only) interaction_config = InteractionConfig( - primary_model=InteractionType(data["primary_model"]), - secondary_model=InteractionType(secondary) if secondary != "none" else None, description=data.get("interaction_description", ""), ) diff --git a/extropy/scenario/parser.py b/extropy/scenario/parser.py index e9f958d..79541ec 100644 --- a/extropy/scenario/parser.py +++ b/extropy/scenario/parser.py @@ -103,12 +103,19 @@ def parse_scenario( >>> event.source 'Netflix' """ + attribute_lines = [ + f"- {attr.name} ({attr.type}): {attr.description}" + for attr in population_spec.attributes + ] + if not attribute_lines: + attribute_lines = ["- (no attributes available)"] + population_context = f""" Population: {population_spec.meta.description} Geography: {population_spec.meta.geography or "Not specified"} Key attributes: -{chr(10).join(f"- {attr.name}: {attr.description}" for attr in population_spec.attributes[:10])} +{chr(10).join(attribute_lines)} """ prompt = f"""## Task diff --git a/extropy/scenario/sampling_semantics.py b/extropy/scenario/sampling_semantics.py new file mode 100644 index 0000000..13ba275 --- /dev/null +++ b/extropy/scenario/sampling_semantics.py @@ -0,0 +1,277 @@ +"""Scenario-stage semantic role mapping for sampler/network runtime use. + +This module runs a focused, low-context LLM call that maps semantic roles to +existing attribute names/options from the merged (base + extended) population. +The output is persisted in scenario.vN.yaml and consumed deterministically by +downstream stages. +""" + +from __future__ import annotations + +from typing import Any + +from ..core.llm import reasoning_call +from ..core.models import PopulationSpec, SamplingSemanticRoles + + +_POLICIES = [ + "gaussian_offset", + "same_group_rate", + "same_country_rate", + "same_value_probability", +] + +_SAMPLING_SEMANTIC_ROLES_SCHEMA = { + "type": "object", + "properties": { + "marital_roles": { + "type": "object", + "properties": { + "attr": {"type": ["string", "null"]}, + "partnered_values": {"type": "array", "items": {"type": "string"}}, + "single_values": {"type": "array", "items": {"type": "string"}}, + }, + "required": ["attr", "partnered_values", "single_values"], + "additionalProperties": False, + }, + "geo_roles": { + "type": "object", + "properties": { + "country_attr": {"type": ["string", "null"]}, + "region_attr": {"type": ["string", "null"]}, + "urbanicity_attr": {"type": ["string", "null"]}, + }, + "required": ["country_attr", "region_attr", "urbanicity_attr"], + "additionalProperties": False, + }, + "partner_correlation_roles": { + "type": "object", + "additionalProperties": { + "type": "string", + "enum": _POLICIES, + }, + }, + "school_parent_role": { + "type": "object", + "properties": { + "dependents_attr": {"type": ["string", "null"]}, + "school_age_values": {"type": "array", "items": {"type": "string"}}, + }, + "required": ["dependents_attr", "school_age_values"], + "additionalProperties": False, + }, + "religion_roles": { + "type": "object", + "properties": { + "religion_attr": {"type": ["string", "null"]}, + "secular_values": {"type": "array", "items": {"type": "string"}}, + }, + "required": ["religion_attr", "secular_values"], + "additionalProperties": False, + }, + }, + "required": [ + "marital_roles", + "geo_roles", + "partner_correlation_roles", + "school_parent_role", + "religion_roles", + ], + "additionalProperties": False, +} + + +def _categorical_options(attr: Any) -> list[str]: + dist = getattr(getattr(attr, "sampling", None), "distribution", None) + if dist is None or not hasattr(dist, "options"): + return [] + options = getattr(dist, "options", None) or [] + return [str(opt) for opt in options] + + +def _build_prompt(population_spec: PopulationSpec) -> str: + max_attrs = 60 + lines: list[str] = [] + for attr in population_spec.attributes[:max_attrs]: + options = _categorical_options(attr) + options_preview = "" + if options: + preview = ", ".join(options[:8]) + if len(options) > 8: + preview += ", ..." + options_preview = f" | options: [{preview}]" + lines.append( + f"- {attr.name} | type={attr.type} | scope={attr.scope}{options_preview}" + ) + + omitted_note = "" + if len(population_spec.attributes) > max_attrs: + omitted = len(population_spec.attributes) - max_attrs + omitted_note = ( + f"\nNote: {omitted} additional attributes omitted to keep prompt compact." + ) + + attrs_block = "\n".join(lines) if lines else "- (no attributes)" + + return f"""Map sampler semantic roles to exact attribute names/options from this merged population. + +Population: {population_spec.meta.description} +Geography/context: {population_spec.meta.geography or "unspecified"} + +Available attributes: +{attrs_block} +{omitted_note} + +Return ONLY valid mappings from these attributes. + +Role definitions: +1) marital_roles: +- attr: marital/relationship status attribute (or null) +- partnered_values: values meaning partnered/married +- single_values: values meaning not partnered + +2) geo_roles: +- country_attr: country/nationality/citizenship-style attribute for naming locale +- region_attr: region/state/province/city bucket +- urbanicity_attr: urban/rural/suburban-style bucket + +3) partner_correlation_roles: +- map attribute_name -> policy +- policy enum: + - gaussian_offset (age-like numeric correlation) + - same_group_rate (identity group assortativity) + - same_country_rate (country/citizenship assortativity) + - same_value_probability (generic assortativity) + +4) school_parent_role: +- dependents_attr: dependents/children list attribute +- school_age_values: labels that indicate school-age dependents + +5) religion_roles: +- religion_attr: religion/faith affiliation attribute +- secular_values: values indicating no religion + +Rules: +- Never invent attribute names. +- If a role is unavailable, use null and empty arrays/maps. +- partnered_values and single_values must be non-overlapping. +- Keep the mapping minimal and practical.""" + + +def _sanitize_roles( + data: dict[str, Any], population_spec: PopulationSpec +) -> SamplingSemanticRoles: + known_attrs = {attr.name: attr for attr in population_spec.attributes} + + def keep_attr(name: Any) -> str | None: + if isinstance(name, str) and name in known_attrs: + return name + return None + + def keep_values(attr_name: str | None, values: Any) -> list[str]: + if not isinstance(values, list): + return [] + if attr_name is None: + return [] + attr = known_attrs.get(attr_name) + if attr is None: + return [] + valid_options = set(_categorical_options(attr)) + if not valid_options: + return [] + return [v for v in values if isinstance(v, str) and v in valid_options] + + marital_raw = data.get("marital_roles") or {} + marital_attr = keep_attr(marital_raw.get("attr")) + partnered_values = keep_values(marital_attr, marital_raw.get("partnered_values")) + single_values = keep_values(marital_attr, marital_raw.get("single_values")) + overlap = set(partnered_values) & set(single_values) + if overlap: + partnered_values = [v for v in partnered_values if v not in overlap] + single_values = [v for v in single_values if v not in overlap] + + geo_raw = data.get("geo_roles") or {} + geo_roles = { + "country_attr": keep_attr(geo_raw.get("country_attr")), + "region_attr": keep_attr(geo_raw.get("region_attr")), + "urbanicity_attr": keep_attr(geo_raw.get("urbanicity_attr")), + } + + policies_raw = data.get("partner_correlation_roles") or {} + policies: dict[str, str] = {} + if isinstance(policies_raw, dict): + for attr_name, policy in policies_raw.items(): + if ( + attr_name in known_attrs + and isinstance(policy, str) + and policy in _POLICIES + ): + policies[attr_name] = policy + + school_raw = data.get("school_parent_role") or {} + dependents_attr = keep_attr(school_raw.get("dependents_attr")) + school_age_values = [] + if isinstance(school_raw.get("school_age_values"), list): + school_age_values = [ + v + for v in school_raw["school_age_values"] + if isinstance(v, str) and v.strip() + ] + + religion_raw = data.get("religion_roles") or {} + religion_attr = keep_attr(religion_raw.get("religion_attr")) + secular_values = [] + if isinstance(religion_raw.get("secular_values"), list): + secular_values = [ + v + for v in religion_raw["secular_values"] + if isinstance(v, str) and v.strip() + ] + + return SamplingSemanticRoles.model_validate( + { + "marital_roles": { + "attr": marital_attr, + "partnered_values": partnered_values, + "single_values": single_values, + }, + "geo_roles": geo_roles, + "partner_correlation_roles": policies, + "school_parent_role": { + "dependents_attr": dependents_attr, + "school_age_values": school_age_values, + }, + "religion_roles": { + "religion_attr": religion_attr, + "secular_values": secular_values, + }, + } + ) + + +def generate_sampling_semantic_roles( + population_spec: PopulationSpec, + model: str | None = None, +) -> SamplingSemanticRoles | None: + """Generate scenario-stage semantic role mappings for sampler/network use. + + Returns None on provider/runtime failure so scenario compilation remains + backward-compatible and non-blocking. + """ + prompt = _build_prompt(population_spec) + try: + data = reasoning_call( + prompt=prompt, + response_schema=_SAMPLING_SEMANTIC_ROLES_SCHEMA, + schema_name="sampling_semantic_roles", + model=model, + reasoning_effort="low", + max_retries=1, + ) + except Exception: + return None + + try: + return _sanitize_roles(data, population_spec) + except Exception: + return None diff --git a/extropy/scenario/sufficiency.py b/extropy/scenario/sufficiency.py index ac55c6f..0efef6a 100644 --- a/extropy/scenario/sufficiency.py +++ b/extropy/scenario/sufficiency.py @@ -10,6 +10,8 @@ Mirrors the population spec sufficiency check pattern. """ +import re + from ..core.llm import simple_call from ..core.models import ClarificationQuestion @@ -121,6 +123,139 @@ class ScenarioSufficiencyResult(BaseModel): "additionalProperties": False, } +_UNIT_KEYWORD_PATTERNS: dict[str, tuple[str, ...]] = { + "hour": ( + r"\bhour(?:s)?\b", + r"\bhr(?:s)?\b", + r"\bh\b", + ), + "day": ( + r"\bday(?:s)?\b", + r"\bdaily\b", + ), + "week": ( + r"\bweek(?:s)?\b", + r"\bwk(?:s)?\b", + r"\bweekly\b", + ), + "month": ( + r"\bmonth(?:s)?\b", + r"\bmo(?:s)?\b", + r"\bmonthly\b", + ), + "year": ( + r"\byear(?:s)?\b", + r"\byr(?:s)?\b", + r"\bannual(?:ly)?\b", + ), +} + +_TIMELINE_MARKER_PATTERNS: dict[str, tuple[str, ...]] = { + "hour": (r"\bhour\s*[-_ ]?\d+\b", r"\bhr\s*[-_ ]?\d+\b"), + "day": (r"\bday\s*[-_ ]?\d+\b",), + "week": (r"\bweek\s*[-_ ]?\d+\b", r"\bwk\s*[-_ ]?\d+\b"), + "month": (r"\bmonth\s*[-_ ]?\d+\b", r"\bmo\s*[-_ ]?\d+\b"), + "year": (r"\byear\s*[-_ ]?\d+\b", r"\byr\s*[-_ ]?\d+\b"), +} + + +def _extract_unit_candidates(text: str) -> set[str]: + """Extract timestep unit candidates from free text.""" + lowered = text.lower() + candidates: set[str] = set() + for unit, patterns in _UNIT_KEYWORD_PATTERNS.items(): + if any(re.search(pattern, lowered) for pattern in patterns): + candidates.add(unit) + return candidates + + +def _extract_timeline_marker_unit(text: str) -> str | None: + """Extract explicit timeline marker unit, e.g. 'month 0', 'week1'.""" + lowered = text.lower() + matches: set[str] = set() + for unit, patterns in _TIMELINE_MARKER_PATTERNS.items(): + if any(re.search(pattern, lowered) for pattern in patterns): + matches.add(unit) + if len(matches) == 1: + return next(iter(matches)) + return None + + +def _infer_unit_from_duration(duration: str | None) -> str | None: + """Infer timestep unit from an inferred duration string.""" + if not duration: + return None + candidates = _extract_unit_candidates(duration) + if len(candidates) == 1: + return next(iter(candidates)) + return None + + +def _build_timestep_question(default_unit: str | None = None) -> ClarificationQuestion: + """Construct deterministic timestep-unit clarification question.""" + default = ( + default_unit + if default_unit in {"hour", "day", "week", "month", "year"} + else "day" + ) + return ClarificationQuestion( + id="timestep_unit", + question="What timestep unit should this scenario use?", + type="single_choice", + options=["hour", "day", "week", "month", "year"], + default=default, + ) + + +def _postprocess_sufficiency( + *, + description: str, + result: ScenarioSufficiencyResult, +) -> ScenarioSufficiencyResult: + """Apply deterministic guardrails after LLM sufficiency output.""" + questions = list(result.questions) + explicit_timeline_unit = _extract_timeline_marker_unit(description) + unit_candidates = _extract_unit_candidates(description) + explicit_unit = None + if explicit_timeline_unit: + explicit_unit = explicit_timeline_unit + elif len(unit_candidates) == 1: + explicit_unit = next(iter(unit_candidates)) + + inferred_from_duration = _infer_unit_from_duration(result.inferred_duration) + inferred_unit = ( + result.inferred_timestep_unit or inferred_from_duration or explicit_unit + ) + + inferred_type = result.inferred_scenario_type + # Hard hint: timeline markers like "month 0, month 1" imply evolving scenarios. + if explicit_timeline_unit and inferred_type != "evolving": + inferred_type = "evolving" + + # Static scenarios must explicitly name the timestep unit. + if inferred_type == "static" and explicit_unit is None: + if not any(q.id == "timestep_unit" for q in questions): + questions.append(_build_timestep_question(inferred_unit)) + return ScenarioSufficiencyResult( + sufficient=False, + questions=questions, + inferred_duration=result.inferred_duration, + inferred_timestep_unit=inferred_unit, + inferred_scenario_type=inferred_type, + has_explicit_outcomes=result.has_explicit_outcomes, + inferred_agent_focus_mode=result.inferred_agent_focus_mode, + ) + + return ScenarioSufficiencyResult( + sufficient=result.sufficient, + questions=questions, + inferred_duration=result.inferred_duration, + inferred_timestep_unit=inferred_unit, + inferred_scenario_type=inferred_type, + has_explicit_outcomes=result.has_explicit_outcomes, + inferred_agent_focus_mode=result.inferred_agent_focus_mode, + ) + def check_scenario_sufficiency( description: str, @@ -159,10 +294,15 @@ def check_scenario_sufficiency( - Static: price change, policy announcement, product feature - Evolving: crisis, campaign, gradual adoption, emerging situation -4. **OUTCOMES** (optional but check) — does the description explicitly define outcome measurements? +4. **TIMESTEP UNIT** — what time unit should simulation steps use (hour/day/week/month/year)? + - For evolving scenarios with timeline markers like "month 0, month 1", use that marker unit. + - For static scenarios, still pick a concrete unit so downstream simulation cadence is explicit. + - If the unit is not clear, ask for clarification. + +5. **OUTCOMES** (optional but check) — does the description explicitly define outcome measurements? Look for sections like "Outcomes:", numbered outcome lists, or explicit mentions of what to track. -5. **AGENT FOCUS MODE** — who in each household should be a simulated agent? +6. **AGENT FOCUS MODE** — who in each household should be a simulated agent? - "primary_only": Only one adult per household is an agent (partner/kids are background context). Use for: opinion polls, consumer behavior, professional studies, most scenarios. - "couples": Both partners are agents (kids are background). @@ -200,7 +340,7 @@ def check_scenario_sufficiency( for i, q in enumerate(raw_questions) ] - return ScenarioSufficiencyResult( + llm_result = ScenarioSufficiencyResult( sufficient=data.get("sufficient", False), questions=questions, inferred_duration=data.get("inferred_duration"), @@ -209,6 +349,7 @@ def check_scenario_sufficiency( has_explicit_outcomes=data.get("has_explicit_outcomes", False), inferred_agent_focus_mode=data.get("inferred_agent_focus_mode"), ) + return _postprocess_sufficiency(description=description, result=llm_result) def check_scenario_sufficiency_with_answers( diff --git a/extropy/scenario/validator.py b/extropy/scenario/validator.py index 164f483..16e6d67 100644 --- a/extropy/scenario/validator.py +++ b/extropy/scenario/validator.py @@ -17,6 +17,7 @@ ValidationResult, ) from ..utils.expressions import ( + extract_comparisons_from_expression, extract_names_from_expression, validate_expression_syntax, ) @@ -85,6 +86,216 @@ def _try_resolve_base_population( return None +def _build_attribute_lookup( + spec: ScenarioSpec, + population_spec: PopulationSpec | None, +) -> dict[str, object]: + """Build merged attribute lookup for base + scenario extension.""" + attr_lookup: dict[str, object] = {} + if population_spec: + for attr in population_spec.attributes: + attr_lookup[attr.name] = attr + for attr in spec.extended_attributes or []: + attr_lookup[attr.name] = attr + return attr_lookup + + +def _validate_literal_option_compatibility( + expression: str, + attr_lookup: dict[str, object], + location: str, +) -> list[ValidationIssue]: + """Validate that string literals compared in expression are valid options.""" + issues: list[ValidationIssue] = [] + comparisons = extract_comparisons_from_expression(expression) + if not comparisons: + return issues + + for attr_name, values in comparisons: + attr = attr_lookup.get(attr_name) + if attr is None: + continue + + attr_type = getattr(attr, "type", None) + dist = getattr(getattr(attr, "sampling", None), "distribution", None) + + if attr_type == "categorical" and dist is not None and hasattr(dist, "options"): + options = set(getattr(dist, "options") or []) + if not options: + continue + for value in values: + if value not in options: + issues.append( + ValidationError( + category="attribute_literal", + location=location, + message=( + f"Condition compares '{attr_name}' to '{value}', " + "but value is not in categorical options" + ), + suggestion=f"Use one of: {', '.join(sorted(options))}", + ) + ) + elif attr_type == "boolean": + allowed = {"true", "false", "yes", "no", "1", "0"} + for value in values: + if value.strip().lower() not in allowed: + issues.append( + ValidationError( + category="attribute_literal", + location=location, + message=( + f"Condition compares boolean '{attr_name}' to '{value}' " + "which is not a boolean literal" + ), + suggestion="Use True/False (or true/false) semantics", + ) + ) + + return issues + + +def _categorical_options_for_attr(attr: object) -> set[str]: + """Extract categorical options for an attribute, if available.""" + dist = getattr(getattr(attr, "sampling", None), "distribution", None) + if dist is None or not hasattr(dist, "options"): + return set() + options = getattr(dist, "options", None) or [] + return {str(v) for v in options} + + +def _validate_sampling_semantic_roles( + spec: ScenarioSpec, + attr_lookup: dict[str, object], +) -> list[ValidationIssue]: + """Validate scenario-level semantic role mapping references/options.""" + issues: list[ValidationIssue] = [] + roles = spec.sampling_semantic_roles + if roles is None: + return issues + + marital = roles.marital_roles + if marital and marital.attr: + attr = attr_lookup.get(marital.attr) + if attr is None: + issues.append( + ValidationError( + category="sampling_semantics", + location="sampling_semantic_roles.marital_roles.attr", + message=f"Unknown marital_roles.attr: '{marital.attr}'", + suggestion="Use an attribute name from base or extended attributes", + ) + ) + else: + options = _categorical_options_for_attr(attr) + if options: + for value in marital.partnered_values: + if value not in options: + issues.append( + ValidationError( + category="sampling_semantics", + location="sampling_semantic_roles.marital_roles.partnered_values", + message=( + f"Marital partnered value '{value}' is not a valid option " + f"for '{marital.attr}'" + ), + suggestion=f"Use one of: {', '.join(sorted(options))}", + ) + ) + for value in marital.single_values: + if value not in options: + issues.append( + ValidationError( + category="sampling_semantics", + location="sampling_semantic_roles.marital_roles.single_values", + message=( + f"Marital single value '{value}' is not a valid option " + f"for '{marital.attr}'" + ), + suggestion=f"Use one of: {', '.join(sorted(options))}", + ) + ) + overlap = set(marital.partnered_values) & set(marital.single_values) + if overlap: + issues.append( + ValidationError( + category="sampling_semantics", + location="sampling_semantic_roles.marital_roles", + message=( + "Marital partnered_values and single_values overlap: " + + ", ".join(sorted(overlap)) + ), + suggestion="Ensure partnered and single sets are disjoint", + ) + ) + + geo = roles.geo_roles + if geo: + for key in ("country_attr", "region_attr", "urbanicity_attr"): + value = getattr(geo, key) + if value and value not in attr_lookup: + issues.append( + ValidationError( + category="sampling_semantics", + location=f"sampling_semantic_roles.geo_roles.{key}", + message=f"Unknown {key}: '{value}'", + suggestion="Use an attribute name from base or extended attributes", + ) + ) + + for attr_name in roles.partner_correlation_roles.keys(): + if attr_name not in attr_lookup: + issues.append( + ValidationError( + category="sampling_semantics", + location="sampling_semantic_roles.partner_correlation_roles", + message=f"Unknown partner correlation attribute: '{attr_name}'", + suggestion="Use attribute names present in merged population", + ) + ) + + school = roles.school_parent_role + if school and school.dependents_attr and school.dependents_attr not in attr_lookup: + issues.append( + ValidationError( + category="sampling_semantics", + location="sampling_semantic_roles.school_parent_role.dependents_attr", + message=f"Unknown dependents_attr: '{school.dependents_attr}'", + suggestion="Use an attribute name from base or extended attributes", + ) + ) + + religion = roles.religion_roles + if religion and religion.religion_attr: + if religion.religion_attr not in attr_lookup: + issues.append( + ValidationError( + category="sampling_semantics", + location="sampling_semantic_roles.religion_roles.religion_attr", + message=f"Unknown religion_attr: '{religion.religion_attr}'", + suggestion="Use an attribute name from base or extended attributes", + ) + ) + else: + options = _categorical_options_for_attr(attr_lookup[religion.religion_attr]) + if options: + for value in religion.secular_values: + if value not in options: + issues.append( + ValidationError( + category="sampling_semantics", + location="sampling_semantic_roles.religion_roles.secular_values", + message=( + f"Secular value '{value}' is not a valid option for " + f"'{religion.religion_attr}'" + ), + suggestion=f"Use one of: {', '.join(sorted(options))}", + ) + ) + + return issues + + def validate_scenario( spec: ScenarioSpec, population_spec: PopulationSpec | None = None, @@ -116,10 +327,8 @@ def validate_scenario( errors: list[ValidationIssue] = [] warnings: list[ValidationIssue] = [] - # Build set of known attributes from population spec - known_attributes: set[str] = set() - if population_spec: - known_attributes = {attr.name for attr in population_spec.attributes} + attribute_lookup = _build_attribute_lookup(spec, population_spec) + known_attributes = set(attribute_lookup.keys()) # Build set of known edge types from network # Check both 'edge_type' and 'type' fields (different network formats) @@ -134,6 +343,44 @@ def validate_scenario( # Build set of defined channels defined_channels = {ch.name for ch in spec.seed_exposure.channels} + # Scenario extension is required for new-flow scenarios. + if spec.meta.base_population and not spec.extended_attributes: + errors.append( + ValidationError( + category="scenario_extension", + location="extended_attributes", + message="Scenario must include non-empty extended_attributes", + suggestion="Add at least one scenario-specific attribute extension", + ) + ) + + # Household semantics must be coherent with focus mode and config + has_household_semantics = any( + getattr(attr, "scope", "individual") in {"household", "partner_correlated"} + for attr in (spec.extended_attributes or []) + ) + if has_household_semantics and spec.household_config is None: + errors.append( + ValidationError( + category="household", + location="household_config", + message="Household/partner-correlated attributes require household_config", + suggestion="Provide household_config in scenario spec", + ) + ) + if (has_household_semantics or spec.household_config is not None) and ( + spec.agent_focus_mode is None + ): + errors.append( + ValidationError( + category="agent_focus", + location="agent_focus_mode", + message="agent_focus_mode is required when household semantics are active", + suggestion="Set agent_focus_mode to primary_only, couples, or all", + ) + ) + errors.extend(_validate_sampling_semantic_roles(spec, attribute_lookup)) + # ========================================================================= # Validate Event # ========================================================================= @@ -213,18 +460,24 @@ def validate_scenario( ) else: # Check attribute references - if population_spec: - refs = extract_names_from_expression(rule.when) - unknown_refs = refs - known_attributes - if unknown_refs: - errors.append( - ValidationError( - category="attribute_reference", - location=f"seed_exposure.rules[{i}].when", - message=f"References unknown attribute(s): {', '.join(sorted(unknown_refs))}", - suggestion="Check attribute names in population spec", - ) + refs = extract_names_from_expression(rule.when) + unknown_refs = refs - known_attributes + if unknown_refs: + errors.append( + ValidationError( + category="attribute_reference", + location=f"seed_exposure.rules[{i}].when", + message=f"References unknown attribute(s): {', '.join(sorted(unknown_refs))}", + suggestion="Check attribute names in population/scenario specs", ) + ) + errors.extend( + _validate_literal_option_compatibility( + rule.when, + attribute_lookup, + f"seed_exposure.rules[{i}].when", + ) + ) # Check probability bounds (already enforced by Pydantic, but double-check) if not 0 <= rule.probability <= 1: @@ -291,17 +544,23 @@ def validate_scenario( # Allow edge attributes injected during propagation refs_without_edge_fields = refs - {"edge_type", "edge_weight"} - if population_spec: - unknown_refs = refs_without_edge_fields - known_attributes - if unknown_refs: - errors.append( - ValidationError( - category="attribute_reference", - location=f"spread.share_modifiers[{i}].when", - message=f"References unknown attribute(s): {', '.join(sorted(unknown_refs))}", - suggestion="Check attribute names in population spec", - ) + unknown_refs = refs_without_edge_fields - known_attributes + if unknown_refs: + errors.append( + ValidationError( + category="attribute_reference", + location=f"spread.share_modifiers[{i}].when", + message=f"References unknown attribute(s): {', '.join(sorted(unknown_refs))}", + suggestion="Check attribute names in population/scenario specs", ) + ) + errors.extend( + _validate_literal_option_compatibility( + modifier.when, + attribute_lookup, + f"spread.share_modifiers[{i}].when", + ) + ) # Check edge type references if "edge_type" in refs: @@ -449,6 +708,51 @@ def validate_scenario( ) ) + if te.exposure_rules: + for j, rule in enumerate(te.exposure_rules): + if rule.channel not in defined_channels: + errors.append( + ValidationError( + category="timeline_exposure_rule", + location=f"timeline[{i}].exposure_rules[{j}].channel", + message=f"Rule references undefined channel: '{rule.channel}'", + suggestion=f"Use one of: {', '.join(sorted(defined_channels))}", + ) + ) + + syntax_error = validate_expression_syntax(rule.when) + if syntax_error: + errors.append( + ValidationError( + category="timeline_exposure_rule", + location=f"timeline[{i}].exposure_rules[{j}].when", + message=f"Invalid expression syntax: {syntax_error}", + suggestion="Use valid Python expression syntax", + ) + ) + else: + refs = extract_names_from_expression(rule.when) + unknown_refs = refs - known_attributes + if unknown_refs: + errors.append( + ValidationError( + category="attribute_reference", + location=f"timeline[{i}].exposure_rules[{j}].when", + message=( + "References unknown attribute(s): " + + ", ".join(sorted(unknown_refs)) + ), + suggestion="Check attribute names in population/scenario specs", + ) + ) + errors.extend( + _validate_literal_option_compatibility( + rule.when, + attribute_lookup, + f"timeline[{i}].exposure_rules[{j}].when", + ) + ) + # ========================================================================= # Validate Simulation Config # ========================================================================= @@ -526,15 +830,6 @@ def validate_scenario( suggestion=f"Use one of: {', '.join(sorted(valid_modes))}", ) ) - elif spec.household_config is not None: - warnings.append( - ValidationWarning( - category="agent_focus", - location="agent_focus_mode", - message="household_config present but agent_focus_mode not set (defaults to primary_only)", - suggestion="Set agent_focus_mode to 'primary_only', 'couples', or 'all'", - ) - ) # ========================================================================= # Validate File References diff --git a/tests/test_agent_focus.py b/tests/test_agent_focus.py index 8938581..321ebdb 100644 --- a/tests/test_agent_focus.py +++ b/tests/test_agent_focus.py @@ -489,10 +489,13 @@ def test_promoted_dependents_preserve_household_names(self): for dep in promoted: first_name = dep.get("first_name") assert first_name, "Promoted dependents must have first_name" + last_name = dep.get("last_name") + assert last_name, "Promoted dependents must have last_name" hh = households.get(dep["household_id"]) assert hh is not None dependent_names = {d.get("name") for d in hh.get("dependent_data", [])} - assert first_name in dependent_names, ( + full_name = f"{first_name} {last_name}".strip() + assert full_name in dependent_names, ( "Promoted dependent names should stay aligned with generated dependent records" ) diff --git a/tests/test_cli.py b/tests/test_cli.py index e61d40e..4342db2 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -121,7 +121,6 @@ def _setup_study_with_scenario(self, tmp_path): meta: description: Test population geography: USA - agent_focus: test agents grounding: overall: low diff --git a/tests/test_compiler.py b/tests/test_compiler.py index 980974b..6b2d77d 100644 --- a/tests/test_compiler.py +++ b/tests/test_compiler.py @@ -118,7 +118,6 @@ def test_creates_valid_scenario( ExposureChannel, ExposureRule, InteractionConfig, - InteractionType, SpreadConfig, OutcomeConfig, OutcomeDefinition, @@ -142,7 +141,6 @@ def test_creates_valid_scenario( ExposureChannel( name="broadcast", description="Mass broadcast", - reach="broadcast", credibility_modifier=1.0, ), ], @@ -155,7 +153,6 @@ def test_creates_valid_scenario( mock_interaction.return_value = ( InteractionConfig( - primary_model=InteractionType.PASSIVE_OBSERVATION, description="Agents observe each other", ), SpreadConfig(share_probability=0.3), @@ -210,7 +207,6 @@ def test_progress_callback_called( ExposureChannel, ExposureRule, InteractionConfig, - InteractionType, SpreadConfig, OutcomeConfig, OutcomeDefinition, @@ -232,7 +228,6 @@ def test_progress_callback_called( ExposureChannel( name="b", description="Broadcast", - reach="broadcast", credibility_modifier=1.0, ) ], @@ -240,7 +235,6 @@ def test_progress_callback_called( ) mock_interaction.return_value = ( InteractionConfig( - primary_model=InteractionType.PASSIVE_OBSERVATION, description="Agents observe each other", ), SpreadConfig(share_probability=0.3), diff --git a/tests/test_conversations.py b/tests/test_conversations.py index 6d83163..b30740b 100644 --- a/tests/test_conversations.py +++ b/tests/test_conversations.py @@ -25,7 +25,6 @@ ExposureChannel, ExposureRule, InteractionConfig, - InteractionType, OutcomeConfig, ScenarioMeta, ScenarioSimConfig, @@ -60,7 +59,6 @@ def _make_scenario() -> ScenarioSpec: ExposureChannel( name="broadcast", description="Broadcast", - reach="broadcast", credibility_modifier=1.0, ) ], @@ -74,7 +72,6 @@ def _make_scenario() -> ScenarioSpec: ], ), interaction=InteractionConfig( - primary_model=InteractionType.DIRECT_CONVERSATION, description="Direct conversations", ), spread=SpreadConfig(share_probability=0.4), diff --git a/tests/test_engine.py b/tests/test_engine.py index 7d0bb51..c71e788 100644 --- a/tests/test_engine.py +++ b/tests/test_engine.py @@ -28,7 +28,6 @@ ExposureChannel, ExposureRule, InteractionConfig, - InteractionType, OutcomeConfig, OutcomeDefinition, OutcomeType, @@ -74,7 +73,6 @@ def minimal_scenario(): ExposureChannel( name="broadcast", description="Mass broadcast channel", - reach="broadcast", credibility_modifier=1.0, ), ], @@ -88,7 +86,6 @@ def minimal_scenario(): ], ), interaction=InteractionConfig( - primary_model=InteractionType.PASSIVE_OBSERVATION, description="Agents observe each other's public statements", ), spread=SpreadConfig( diff --git a/tests/test_estimator.py b/tests/test_estimator.py index ef9afbd..f5a07d7 100644 --- a/tests/test_estimator.py +++ b/tests/test_estimator.py @@ -34,7 +34,6 @@ ExposureChannel, ExposureRule, InteractionConfig, - InteractionType, SpreadConfig, OutcomeConfig, OutcomeDefinition, @@ -157,7 +156,6 @@ def small_scenario() -> ScenarioSpec: ExposureChannel( name="email", description="Email broadcast", - reach="broadcast", credibility_modifier=1.0, ), ], @@ -171,7 +169,6 @@ def small_scenario() -> ScenarioSpec: ], ), interaction=InteractionConfig( - primary_model=InteractionType.PASSIVE_OBSERVATION, description="Social media style observation", ), spread=SpreadConfig( diff --git a/tests/test_integration_timestep.py b/tests/test_integration_timestep.py index 207969c..8eab4fa 100644 --- a/tests/test_integration_timestep.py +++ b/tests/test_integration_timestep.py @@ -25,7 +25,6 @@ ExposureChannel, ExposureRule, InteractionConfig, - InteractionType, OutcomeConfig, OutcomeDefinition, OutcomeType, @@ -95,14 +94,12 @@ def _make_scenario( ExposureChannel( name="broadcast", description="Broadcast", - reach="broadcast", credibility_modifier=1.0, ), ], rules=rules, ), interaction=InteractionConfig( - primary_model=InteractionType.PASSIVE_OBSERVATION, description="Observe", ), spread=SpreadConfig(share_probability=share_probability), diff --git a/tests/test_network.py b/tests/test_network.py index 15c3613..78eb56d 100644 --- a/tests/test_network.py +++ b/tests/test_network.py @@ -821,6 +821,87 @@ def test_generate_network_includes_ladder_stage_metadata(self, sample_agents): assert isinstance(quality["ladder_stages"], list) assert len(quality["ladder_stages"]) >= 1 + def test_topology_gate_uses_final_graph_metrics(self, monkeypatch): + """Strict acceptance should be based on final graph after structural + rewiring.""" + import extropy.population.network.generator as gen + + agents = [{"_id": f"a{i}"} for i in range(6)] + + def fake_single_pass( + agents, + agent_ids, + similarities, + communities, + degree_factors, + config, + intra_scale, + inter_scale, + rng, + structural_pairs=None, + ): + # Deliberately poor calibration graph. + return [], 0.0, 0.0, 0.0, 0.0 + + def full_structural_edges(agents, agent_ids, rng): + # Dense structural graph that should pass final strict gates. + edges = [] + for i in range(len(agent_ids)): + for j in range(i + 1, len(agent_ids)): + edges.append( + Edge( + source=agent_ids[i], + target=agent_ids[j], + weight=1.0, + edge_type="household", + structural=True, + context="household", + ) + ) + return edges + + def no_rewire(agents, agent_ids, edges, edge_set, config, rng, protected_pairs): + return edges, edge_set, 0 + + monkeypatch.setattr(gen, "_generate_network_single_pass", fake_single_pass) + monkeypatch.setattr(gen, "_generate_structural_edges", full_structural_edges) + monkeypatch.setattr(gen, "_apply_rewiring", no_rewire) + monkeypatch.setattr( + gen, + "_assign_communities_with_diagnostics", + lambda agents, + similarities, + n_communities, + rng, + preferred_attrs=None, + progress=None: ( + [0 for _ in agents], + {"low_signal": 0.0}, + ), + ) + + config = NetworkConfig( + avg_degree=5.0, + seed=42, + candidate_mode="exact", + calibration_restarts=1, + max_calibration_iterations=1, + max_calibration_minutes=1, + topology_gate="strict", + target_clustering=0.0, + target_clustering_tolerance=1.0, + target_modularity=0.0, + target_modularity_tolerance=1.0, + ) + + result = generate_network(agents, config) + quality = result.meta.get("quality", {}) + + assert quality.get("calibration_accepted") is False + assert quality.get("accepted") is True + assert quality.get("final_metrics", {}).get("edge_count") == 15 + assert quality.get("final_metrics", {}).get("largest_component_ratio") == 1.0 + assert result.meta.get("edge_count") == 15 + def test_generate_network_resume_from_checkpoint_matches_fresh(self, sample_agents): """Resuming from a saved similarity checkpoint should match a fresh run.""" import sqlite3 diff --git a/tests/test_persona_validator.py b/tests/test_persona_validator.py new file mode 100644 index 0000000..9d002bd --- /dev/null +++ b/tests/test_persona_validator.py @@ -0,0 +1,86 @@ +"""Tests for persona config validator.""" + +from datetime import datetime + +from extropy.population.persona import ( + PersonaConfig, + AttributeTreatment, + TreatmentType, + AttributeGroup, + AttributePhrasing, + CategoricalPhrasing, + ConcretePhrasing, + validate_persona_config, +) + + +def _valid_persona_config() -> PersonaConfig: + return PersonaConfig( + population_description="Test population", + created_at=datetime(2024, 1, 1), + intro_template="I'm {age} years old.", + treatments=[ + AttributeTreatment( + attribute="age", treatment=TreatmentType.CONCRETE, group="identity" + ), + AttributeTreatment( + attribute="gender", + treatment=TreatmentType.CONCRETE, + group="identity", + ), + ], + groups=[ + AttributeGroup( + name="identity", label="Who I Am", attributes=["age", "gender"] + ) + ], + phrasings=AttributePhrasing( + categorical=[ + CategoricalPhrasing( + attribute="gender", + phrases={ + "male": "I'm male", + "female": "I'm female", + "other": "I identify outside the binary", + }, + null_options=[], + null_phrase=None, + ) + ], + concrete=[ + ConcretePhrasing( + attribute="age", + template="I'm {value} years old", + format_spec=".0f", + prefix="", + suffix="", + ) + ], + ), + ) + + +def test_validate_persona_config_valid(minimal_population_spec): + config = _valid_persona_config() + result = validate_persona_config(minimal_population_spec, config) + assert result.valid, result.errors + + +def test_validate_persona_config_detects_missing_group_membership( + minimal_population_spec, +): + config = _valid_persona_config() + config.groups[0].attributes = ["age"] + result = validate_persona_config(minimal_population_spec, config) + assert not result.valid + assert any(issue.category == "PERSONA_GROUP" for issue in result.errors) + + +def test_validate_persona_config_detects_unknown_intro_reference( + minimal_population_spec, +): + config = _valid_persona_config() + config.intro_template = "I live in {unknown_attr}." + result = validate_persona_config(minimal_population_spec, config) + assert not result.valid + assert any(issue.category == "PERSONA_INTRO" for issue in result.errors) diff --git a/tests/test_propagation.py b/tests/test_propagation.py index af37c3f..05d5478 100644 --- a/tests/test_propagation.py +++ b/tests/test_propagation.py @@ -17,7 +17,6 @@ ExposureChannel, ExposureRule, InteractionConfig, - InteractionType, OutcomeConfig, OutcomeDefinition, OutcomeType, @@ -56,7 +55,6 @@ def _make_scenario( ExposureChannel( name="broadcast", description="Mass broadcast", - reach="broadcast", credibility_modifier=1.0, ), ] @@ -90,7 +88,6 @@ def _make_scenario( ), seed_exposure=SeedExposure(channels=channels, rules=rules), interaction=InteractionConfig( - primary_model=InteractionType.PASSIVE_OBSERVATION, description="Observe", ), spread=SpreadConfig( @@ -170,7 +167,6 @@ def test_existing_channel(self): ExposureChannel( name="email", description="Email", - reach="targeted", credibility_modifier=0.8, ), ] @@ -266,7 +262,6 @@ def test_credibility_calculation(self, tmp_path, rng): ExposureChannel( name="email", description="Email", - reach="targeted", credibility_modifier=0.8, ), ] diff --git a/tests/test_reasoning_execution.py b/tests/test_reasoning_execution.py index 2f20a79..edc8af1 100644 --- a/tests/test_reasoning_execution.py +++ b/tests/test_reasoning_execution.py @@ -12,7 +12,6 @@ ExposureChannel, ExposureRule, InteractionConfig, - InteractionType, OutcomeConfig, OutcomeDefinition, OutcomeType, @@ -50,7 +49,6 @@ def _make_scenario() -> ScenarioSpec: ExposureChannel( name="broadcast", description="Broadcast", - reach="broadcast", credibility_modifier=1.0, ) ], @@ -64,7 +62,6 @@ def _make_scenario() -> ScenarioSpec: ], ), interaction=InteractionConfig( - primary_model=InteractionType.PASSIVE_OBSERVATION, description="Observe", ), spread=SpreadConfig(share_probability=0.3), @@ -224,7 +221,6 @@ def _make_scenario_no_pass2() -> ScenarioSpec: ExposureChannel( name="broadcast", description="Broadcast", - reach="broadcast", ) ], rules=[ @@ -237,7 +233,6 @@ def _make_scenario_no_pass2() -> ScenarioSpec: ], ), interaction=InteractionConfig( - primary_model=InteractionType.PASSIVE_OBSERVATION, description="Observe updates", ), spread=SpreadConfig(share_probability=0.4), diff --git a/tests/test_reasoning_prompts.py b/tests/test_reasoning_prompts.py index 65be11b..97eb607 100644 --- a/tests/test_reasoning_prompts.py +++ b/tests/test_reasoning_prompts.py @@ -21,7 +21,6 @@ ExposureChannel, ExposureRule, InteractionConfig, - InteractionType, OutcomeConfig, OutcomeDefinition, OutcomeType, @@ -66,7 +65,6 @@ def _make_scenario(**overrides): ExposureChannel( name="broadcast", description="Broadcast", - reach="broadcast", credibility_modifier=1.0, ) ], @@ -77,7 +75,6 @@ def _make_scenario(**overrides): ], ), interaction=InteractionConfig( - primary_model=InteractionType.PASSIVE_OBSERVATION, description="Observe", ), spread=SpreadConfig(share_probability=0.3), @@ -410,7 +407,6 @@ def test_channel_experience_template(self): ExposureChannel( name="app_notification", description="App Notification", - reach="broadcast", experience_template="I got a push notification from {channel_name}", ) ], diff --git a/tests/test_sampler.py b/tests/test_sampler.py index 9c63433..6dea77a 100644 --- a/tests/test_sampler.py +++ b/tests/test_sampler.py @@ -25,7 +25,9 @@ save_sqlite, SamplingResult, SamplingError, + _resolve_country_hint, ) +from extropy.core.models.scenario import SamplingSemanticRoles, GeoRoles from extropy.population.sampler.distributions import ( sample_distribution, coerce_to_type, @@ -118,6 +120,30 @@ def test_missing_variable_raises(self): eval_safe("age + 10", {}) +class TestCountryHintResolution: + """Tests for geography hint resolution used in naming.""" + + def test_citizenship_status_does_not_override_scope(self): + agent = {"citizenship": "non_citizen"} + assert _resolve_country_hint(agent, default_geography="India") == "India" + + def test_citizenship_country_parses_when_present(self): + agent = {"citizenship": "US citizen"} + assert _resolve_country_hint(agent, default_geography=None) == "US" + + def test_semantic_country_attr_precedence(self): + agent = {"residence_country": "India", "country": "US"} + roles = SamplingSemanticRoles( + geo_roles=GeoRoles(country_attr="residence_country") + ) + assert ( + _resolve_country_hint( + agent, default_geography="Canada", semantic_roles=roles + ) + == "India" + ) + + class TestEvalFormula: """Tests for formula evaluation.""" diff --git a/tests/test_scenario.py b/tests/test_scenario.py index fde721b..ebf733f 100644 --- a/tests/test_scenario.py +++ b/tests/test_scenario.py @@ -11,7 +11,6 @@ ExposureChannel, ExposureRule, SeedExposure, - InteractionType, InteractionConfig, SpreadModifier, SpreadConfig, @@ -105,21 +104,19 @@ def test_channel_creation(self): channel = ExposureChannel( name="email_notification", description="Official email from administration", - reach="broadcast", credibility_modifier=1.0, ) assert channel.name == "email_notification" - assert channel.reach == "broadcast" - - def test_channel_reach_options(self): - """Test different reach options.""" - for reach in ["broadcast", "targeted", "organic"]: - channel = ExposureChannel( - name="test", - description="Test", - reach=reach, - ) - assert channel.reach == reach + + def test_channel_creation_without_reach(self): + """Channel model no longer includes reach field.""" + channel = ExposureChannel( + name="test", + description="Test", + credibility_modifier=0.9, + ) + assert channel.name == "test" + assert channel.credibility_modifier == 0.9 class TestExposureRule: @@ -185,19 +182,14 @@ class TestInteractionConfig: def test_interaction_config_creation(self): """Test creating interaction config.""" config = InteractionConfig( - primary_model=InteractionType.PASSIVE_OBSERVATION, - secondary_model=InteractionType.DIRECT_CONVERSATION, description="Social media style with some direct discussions", ) - assert config.primary_model == InteractionType.PASSIVE_OBSERVATION - assert config.secondary_model == InteractionType.DIRECT_CONVERSATION + assert "social media" in config.description.lower() def test_all_interaction_types(self): - """Test all interaction types.""" - assert InteractionType.PASSIVE_OBSERVATION.value == "passive_observation" - assert InteractionType.DIRECT_CONVERSATION.value == "direct_conversation" - assert InteractionType.BROADCAST_RESPONSE.value == "broadcast_response" - assert InteractionType.DELIBERATIVE.value == "deliberative" + """Interaction type enum was removed; description remains informational.""" + config = InteractionConfig(description="informational only") + assert config.description == "informational only" class TestSpreadConfig: @@ -387,7 +379,6 @@ def sample_scenario_spec(self): ], ), interaction=InteractionConfig( - primary_model=InteractionType.PASSIVE_OBSERVATION, description="Social media style observation", ), spread=SpreadConfig(share_probability=0.3), @@ -491,13 +482,11 @@ def test_multi_channel_exposure(self): ExposureChannel( name="meeting", description="Staff meeting", - reach="targeted", credibility_modifier=0.95, ), ExposureChannel( name="word_of_mouth", description="Informal discussion", - reach="organic", credibility_modifier=0.7, ), ] @@ -618,7 +607,6 @@ def test_full_scenario_with_all_features(self): ExposureChannel( name="meeting", description="Department meeting", - reach="targeted", credibility_modifier=0.95, ), ], @@ -635,8 +623,6 @@ def test_full_scenario_with_all_features(self): ], ), interaction=InteractionConfig( - primary_model=InteractionType.PASSIVE_OBSERVATION, - secondary_model=InteractionType.DIRECT_CONVERSATION, description="Surgeons observe peer reactions and discuss informally", ), spread=SpreadConfig( @@ -673,5 +659,4 @@ def test_full_scenario_with_all_features(self): assert spec.meta.name == "ai_tool_full_scenario" assert spec.event.type == EventType.ANNOUNCEMENT assert len(spec.seed_exposure.channels) == 2 - assert spec.interaction.secondary_model == InteractionType.DIRECT_CONVERSATION assert spec.simulation.seed == 42 diff --git a/tests/test_scenario_sufficiency.py b/tests/test_scenario_sufficiency.py new file mode 100644 index 0000000..eca4f66 --- /dev/null +++ b/tests/test_scenario_sufficiency.py @@ -0,0 +1,75 @@ +"""Tests for deterministic post-processing in scenario sufficiency.""" + +from extropy.scenario import sufficiency + + +def test_static_scenario_requires_explicit_timestep_unit(monkeypatch): + """Static scenarios without explicit unit should request clarification.""" + + def _fake_simple_call(*args, **kwargs): + return { + "sufficient": True, + "inferred_duration": "shortly after announcement", + "inferred_timestep_unit": None, + "inferred_scenario_type": "static", + "has_explicit_outcomes": False, + "inferred_agent_focus_mode": "primary_only", + "questions": [], + } + + monkeypatch.setattr(sufficiency, "simple_call", _fake_simple_call) + + result = sufficiency.check_scenario_sufficiency( + "City announces a congestion fee increase." + ) + + assert result.sufficient is False + assert any(q.id == "timestep_unit" for q in result.questions) + + +def test_timeline_markers_force_evolving_and_unit(monkeypatch): + """Timeline markers like 'month 0' should force evolving + month unit.""" + + def _fake_simple_call(*args, **kwargs): + return { + "sufficient": True, + "inferred_duration": None, + "inferred_timestep_unit": None, + "inferred_scenario_type": "static", + "has_explicit_outcomes": False, + "inferred_agent_focus_mode": "primary_only", + "questions": [], + } + + monkeypatch.setattr(sufficiency, "simple_call", _fake_simple_call) + + result = sufficiency.check_scenario_sufficiency( + "Month 0: tax announced. Month 1: protests begin." + ) + + assert result.inferred_scenario_type == "evolving" + assert result.inferred_timestep_unit == "month" + + +def test_infers_unit_from_duration_text_when_missing(monkeypatch): + """If LLM omits unit but duration names one, deterministic inference fills it.""" + + def _fake_simple_call(*args, **kwargs): + return { + "sufficient": True, + "inferred_duration": "over 6 weeks", + "inferred_timestep_unit": None, + "inferred_scenario_type": "evolving", + "has_explicit_outcomes": False, + "inferred_agent_focus_mode": "primary_only", + "questions": [], + } + + monkeypatch.setattr(sufficiency, "simple_call", _fake_simple_call) + + result = sufficiency.check_scenario_sufficiency( + "Policy adoption unfolds over 6 weeks." + ) + + assert result.sufficient is True + assert result.inferred_timestep_unit == "week" diff --git a/tests/test_scenario_validator.py b/tests/test_scenario_validator.py index bf10a1f..14db15f 100644 --- a/tests/test_scenario_validator.py +++ b/tests/test_scenario_validator.py @@ -8,7 +8,6 @@ ExposureChannel, ExposureRule, InteractionConfig, - InteractionType, OutcomeConfig, OutcomeDefinition, OutcomeType, @@ -18,6 +17,15 @@ SeedExposure, SpreadModifier, SpreadConfig, + SamplingSemanticRoles, + MaritalRoles, + GeoRoles, +) +from extropy.core.models.population import ( + AttributeSpec, + SamplingConfig, + GroundingInfo, + CategoricalDistribution, ) from extropy.scenario.validator import load_and_validate_scenario, validate_scenario from extropy.storage import open_study_db @@ -49,7 +57,6 @@ def _make_scenario_spec( ExposureChannel( name="official_notice", description="Official notice", - reach="broadcast", ) ], rules=[ @@ -62,7 +69,6 @@ def _make_scenario_spec( ], ), interaction=InteractionConfig( - primary_model=InteractionType.PASSIVE_OBSERVATION, description="Passive observation test", ), spread=SpreadConfig(share_probability=0.3), @@ -182,3 +188,103 @@ def test_validate_scenario_allows_edge_weight_in_spread_modifier(tmp_path: Path) if issue.location == "spread.share_modifiers[0].when" ] assert not edge_weight_errors + + +def _extended_categorical_attr(name: str, options: list[str]) -> AttributeSpec: + return AttributeSpec( + name=name, + type="categorical", + category="context_specific", + description=f"{name} attribute", + sampling=SamplingConfig( + strategy="independent", + distribution=CategoricalDistribution( + type="categorical", + options=options, + weights=[1.0 / len(options)] * len(options), + ), + ), + grounding=GroundingInfo(level="low", method="estimated"), + ) + + +def test_validate_scenario_ref_namespace_includes_extended_attrs( + minimal_population_spec, +): + spec = _make_scenario_spec("population.yaml", "study.db") + spec.meta.base_population = "population.v1" + spec.extended_attributes = [ + _extended_categorical_attr( + "info_channel_preference", + ["official_notice", "social_media"], + ) + ] + spec.seed_exposure.rules[0].when = "info_channel_preference == 'official_notice'" + + result = validate_scenario(spec, population_spec=minimal_population_spec) + + assert result.valid, result.errors + assert not any(issue.category == "attribute_reference" for issue in result.errors) + + +def test_validate_scenario_rejects_invalid_literal_in_when(minimal_population_spec): + spec = _make_scenario_spec("population.yaml", "study.db") + spec.meta.base_population = "population.v1" + spec.extended_attributes = [ + _extended_categorical_attr( + "info_channel_preference", + ["official_notice", "social_media"], + ) + ] + spec.seed_exposure.rules[0].when = "info_channel_preference == 'telepathy'" + + result = validate_scenario(spec, population_spec=minimal_population_spec) + + assert not result.valid + assert any(issue.category == "attribute_literal" for issue in result.errors) + + +def test_validate_scenario_rejects_unknown_sampling_semantic_role_attr( + minimal_population_spec, +): + spec = _make_scenario_spec("population.yaml", "study.db") + spec.meta.base_population = "population.v1" + spec.extended_attributes = [ + _extended_categorical_attr("marital_status", ["single", "married"]) + ] + spec.sampling_semantic_roles = SamplingSemanticRoles( + geo_roles=GeoRoles(country_attr="missing_country_attr") + ) + + result = validate_scenario(spec, population_spec=minimal_population_spec) + + assert not result.valid + assert any( + issue.location == "sampling_semantic_roles.geo_roles.country_attr" + for issue in result.errors + ) + + +def test_validate_scenario_rejects_overlapping_marital_role_values( + minimal_population_spec, +): + spec = _make_scenario_spec("population.yaml", "study.db") + spec.meta.base_population = "population.v1" + spec.extended_attributes = [ + _extended_categorical_attr("marital_status", ["single", "married"]) + ] + spec.sampling_semantic_roles = SamplingSemanticRoles( + marital_roles=MaritalRoles( + attr="marital_status", + partnered_values=["married", "single"], + single_values=["single"], + ) + ) + + result = validate_scenario(spec, population_spec=minimal_population_spec) + + assert not result.valid + assert any( + issue.location == "sampling_semantic_roles.marital_roles" + for issue in result.errors + ) diff --git a/uv.lock b/uv.lock index f340df2..8c8707a 100644 --- a/uv.lock +++ b/uv.lock @@ -100,6 +100,8 @@ name = "extropy-run" source = { editable = "." } dependencies = [ { name = "anthropic" }, + { name = "faker" }, + { name = "iso3166" }, { name = "networkx" }, { name = "numpy" }, { name = "openai" }, @@ -127,6 +129,8 @@ dev = [ [package.metadata] requires-dist = [ { name = "anthropic", specifier = ">=0.77.0,<1" }, + { name = "faker", specifier = ">=37.11.0,<38" }, + { name = "iso3166", specifier = ">=2.1.1,<3" }, { name = "networkx", specifier = ">=3.4.0,<4" }, { name = "numpy", specifier = ">=2.0.0,<3" }, { name = "openai", specifier = ">=1.50.0,<2" }, @@ -148,6 +152,18 @@ dev = [ { name = "ruff", specifier = ">=0.14.10" 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3de001fcf9420bef854efa3b34eaa386cbf9945f Mon Sep 17 00:00:00 2001 From: DeveshParagiri Date: Fri, 20 Feb 2026 01:03:33 -0500 Subject: [PATCH 05/20] chore: update toml --- pyproject.toml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index ffebae2..0157a87 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -35,6 +35,8 @@ dependencies = [ "python-dotenv>=1.0.0,<2", "pyyaml>=6.0.0,<7", "anthropic>=0.77.0,<1", + "faker>=37.11.0,<38", + "iso3166>=2.1.1,<3", ] [project.optional-dependencies] From b28ffa7d266313d0da92e3ab3e334d37de291081 Mon Sep 17 00:00:00 2001 From: DeveshParagiri Date: Sat, 21 Feb 2026 18:55:07 -0500 Subject: [PATCH 06/20] chore: sync pipeline changes and example study artifacts --- AGENTS.md | 68 +- docs/architecture.md | 2 +- docs/atd-technical-instructions.md | 286 ++ docs/fixes.md | 93 - docs/logging-guidelines.md | 25 - docs/plans/cli-refactor.md | 554 --- docs/simulation-v2-architecture.md | 3 - docs/skill-rewrite-plan.md | 206 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examples/study-02/spec_warnings_full.json create mode 100644 examples/study-02/spec_warnings_full.txt create mode 100644 examples/study-02/spec_warnings_grouped.txt create mode 100644 examples/study-02/study.db create mode 100644 extropy/population/modifier_precedence.py create mode 100644 tests/test_household_hydrator.py create mode 100644 tests/test_modifier_precedence.py create mode 100644 tests/test_scenario_interaction.py diff --git a/AGENTS.md b/AGENTS.md index 260eb59..8eba391 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -18,6 +18,7 @@ extropy query summary --json ``` **Agent mode behavior:** + - All output is JSON (parseable, no ANSI formatting) - Exit codes indicate success/failure (0 = success, non-zero = error) - No interactive prompts — commands fail fast if missing required input @@ -29,20 +30,22 @@ extropy query summary --json extropy spec → extropy scenario → extropy persona → extropy sample → extropy network → extropy simulate → extropy results ``` -| Command | Purpose | -|---------|---------| -| `spec` | Create population spec from description | -| `scenario` | Create scenario with events and outcomes | -| `persona` | Generate persona rendering config | -| `sample` | Sample agents from merged spec | -| `network` | Generate social network | -| `simulate` | Run simulation | -| `results` | View results (summary, timeline, segment, agent) | -| `query` | Export raw data (agents, edges, states, SQL) | -| `chat` | Chat with simulated agents | -| `estimate` | Predict simulation cost | -| `validate` | Validate spec files | -| `config` | View/set configuration | + +| Command | Purpose | +| ---------- | ------------------------------------------------ | +| `spec` | Create population spec from description | +| `scenario` | Create scenario with events and outcomes | +| `persona` | Generate persona rendering config | +| `sample` | Sample agents from merged spec | +| `network` | Generate social network | +| `simulate` | Run simulation | +| `results` | View results (summary, timeline, segment, agent) | +| `query` | Export raw data (agents, edges, states, SQL) | +| `chat` | Chat with simulated agents | +| `estimate` | Predict simulation cost | +| `validate` | Validate spec files | +| `config` | View/set configuration | + ## Non-Interactive Usage @@ -71,17 +74,19 @@ extropy persona -s ai-adoption -y ## Exit Codes -| Code | Meaning | -|------|---------| -| 0 | Success | -| 1 | General error / validation failure | -| 2 | Clarification needed (agent mode) | -| 3 | File not found | -| 4 | Sampling error | -| 5 | Network error | -| 6 | Simulation error | -| 7 | Scenario error | -| 10 | User cancelled | + +| Code | Meaning | +| ---- | ---------------------------------- | +| 0 | Success | +| 1 | General error / validation failure | +| 2 | Clarification needed (agent mode) | +| 3 | File not found | +| 4 | Sampling error | +| 5 | Network error | +| 6 | Simulation error | +| 7 | Scenario error | +| 10 | User cancelled | + ## Querying Data @@ -150,11 +155,13 @@ export AZURE_ENDPOINT=https://.services.ai.azure.com/ All commands accept: -| Flag | Purpose | -|------|---------| -| `--json` | JSON output (overrides cli.mode) | -| `--cost` | Show cost summary after command | -| `--study PATH` | Explicit study folder path | + +| Flag | Purpose | +| -------------- | -------------------------------- | +| `--json` | JSON output (overrides cli.mode) | +| `--cost` | Show cost summary after command | +| `--study PATH` | Explicit study folder path | + ## Study Folder Structure @@ -197,3 +204,4 @@ extropy results --json extropy query agents --to agents.jsonl extropy query states --to states.jsonl ``` + diff --git a/docs/architecture.md b/docs/architecture.md index aad4f80..72c45ff 100644 --- a/docs/architecture.md +++ b/docs/architecture.md @@ -52,7 +52,7 @@ When `household_mode: true`: - Generate correlated partners (shared attributes: location, income correlation) - Generate NPC dependents (children, elderly) based on household type - Household types: singles, couples, single parents, couples with kids, multi-generational -- `agent_focus` controls who reasons: `primary`, `couples`, `families`, `all` +- `agent_focus_mode` controls who reasons: `primary_only`, `couples`, `all` ### 7. Network Generation (`network/`) diff --git a/docs/atd-technical-instructions.md b/docs/atd-technical-instructions.md new file mode 100644 index 0000000..e7d832d --- /dev/null +++ b/docs/atd-technical-instructions.md @@ -0,0 +1,286 @@ +# Technical Instructions: Simulating Congestion Tax Response with Extropy + +**Prepared for:** Austin Transportation Department – Urban Planning & Data Team +**Deliverable:** `mode_shift_report.json` containing simulated commuter response data +**Estimated Time:** 20–30 minutes + +--- + +## Prerequisites + +Before beginning, ensure your workstation meets the following requirements: + +| Requirement | Specification | +|-------------|---------------| +| Operating System | macOS 12+, Ubuntu 20.04+, or Windows 11 with WSL2 | +| Python | 3.10 or higher | +| RAM | 8GB minimum (16GB recommended for 500+ agents) | +| API Key | Anthropic or OpenAI API key with active billing | + +To verify your Python version, open a terminal and run: + +```bash +python3 --version +``` + +If the output shows `Python 3.10.x` or higher, proceed to Step 1. + +--- + +## Step 1: Install the Extropy CLI + +Install the Extropy command-line interface using pip: + +```bash +pip install extropy-cli +``` + +Wait for the installation to complete. You should see output ending with: + +``` +Successfully installed extropy-cli-x.x.x +``` + +To verify the installation, run: + +```bash +extropy --version +``` + +--- + +## Step 2: Configure Your API Key + +Extropy requires an LLM provider to generate agent populations. Set your API key as an environment variable: + +**For Anthropic (recommended):** +```bash +export ANTHROPIC_API_KEY="sk-ant-your-key-here" +``` + +**For OpenAI:** +```bash +export OPENAI_API_KEY="sk-your-key-here" +``` + +To make this permanent, add the export line to your `~/.bashrc` or `~/.zshrc` file. + +--- + +## Step 3: Initialize the Study Population + +Create a new study folder by running the `extropy spec` command with a description of your target population: + +```bash +extropy spec "Adult commuters in Austin, Texas who drive to work in the downtown core. Include variation in income levels, commute distances, access to public transit, and current transportation mode preferences." -o austin-congestion-study +``` + +This command performs two actions: +1. Creates the `austin-congestion-study/` folder structure +2. Generates a `population.v1.yaml` file containing demographic distributions + +Wait for the command to complete. You should see: + +``` +✓ Population spec created: austin-congestion-study/population.v1.yaml +``` + +--- + +## Step 4: Navigate to the Study Folder + +Change your working directory to the newly created study folder: + +```bash +cd austin-congestion-study +``` + +Verify the folder contents: + +```bash +ls -la +``` + +You should see: +``` +population.v1.yaml +study.db +``` + +--- + +## Step 5: Create the Congestion Tax Scenario + +Define the policy scenario using natural language. The Extropy engine interprets this description to generate appropriate events and behavioral triggers: + +```bash +extropy scenario "The City of Austin implements a $15 daily congestion tax for vehicles entering the downtown core during peak hours (7-9 AM and 4-7 PM). The policy is announced via local news, city government communications, and social media. Commuters must decide whether to continue driving and pay the tax, shift to public transit, adjust their work schedule, or work from home." -o congestion-tax -y +``` + +This creates the scenario configuration at `scenario/congestion-tax/scenario.v1.yaml`. + +Expected output: +``` +Creating scenario: congestion-tax/scenario.v1.yaml +Base population: population.v1.yaml + +✓ Found X NEW attributes +✓ Scenario created successfully +``` + +--- + +## Step 6: Generate the Persona Configuration + +Create the persona rendering configuration, which determines how agents are presented during simulation: + +```bash +extropy persona -s congestion-tax -y +``` + +This generates `scenario/congestion-tax/persona.v1.yaml`. + +--- + +## Step 7: Sample the Agent Population + +Generate 500 simulated commuters based on your population specification: + +```bash +extropy sample -s congestion-tax -n 500 --seed 42 +``` + +The `--seed 42` flag ensures reproducible results. Running this command again with the same seed will generate identical agents. + +Expected output: +``` +Sampling 500 agents... +✓ Sampled 500 agents to study.db +``` + +--- + +## Step 8: Generate the Social Network + +Create connections between agents to simulate information spread through social ties: + +```bash +extropy network -s congestion-tax --seed 42 +``` + +This step models how awareness of the congestion tax propagates through workplace, neighborhood, and social connections. + +--- + +## Step 9: Run the Simulation + +Execute the simulation with the following command: + +```bash +extropy simulate -s congestion-tax --seed 42 +``` + +A progress indicator will display simulation status: + +``` +Simulating congestion-tax scenario... +[████████████████████████████████] 100% + +✓ Simulation complete + Agents processed: 500 + Timesteps: 1 + Results saved to: study.db +``` + +**Note:** Simulation time varies based on agent count. For 500 agents, expect 2–5 minutes. + +--- + +## Step 10: Export the Results Report + +Extract the simulation results to a JSON file: + +```bash +extropy results --json > mode_shift_report.json +``` + +This command queries the simulation database and exports a summary report. + +--- + +## Step 11: Verify the Deliverable + +Confirm the report file exists and contains data: + +```bash +ls -la mode_shift_report.json +``` + +To preview the contents: + +```bash +head -50 mode_shift_report.json +``` + +The report contains aggregate statistics including: +- **Awareness rate:** Percentage of agents aware of the congestion tax +- **Attitude distribution:** Agent sentiment toward the policy +- **Behavioral outcomes:** Projected mode shift percentages (continue driving, switch to transit, work from home, etc.) + +--- + +## Deliverable Summary + +Upon completion, your `austin-congestion-study/` folder contains: + +``` +austin-congestion-study/ +├── study.db # Canonical simulation database +├── population.v1.yaml # Population specification +├── mode_shift_report.json # ← YOUR DELIVERABLE +└── scenario/ + └── congestion-tax/ + ├── scenario.v1.yaml # Scenario configuration + └── persona.v1.yaml # Persona rendering config +``` + +Submit `mode_shift_report.json` as evidence of completed simulation. + +--- + +## Figures + +### Figure 1: Terminal Output After Successful Simulation + +*Description for screenshot:* Capture your terminal window immediately after Step 9 completes. The screenshot should show: +- The `extropy simulate` command at the top +- The progress bar at 100% +- The success message showing "Simulation complete" with agent count +- A visible command prompt ready for the next command + +**Caption:** "Figure 1: Terminal output confirming successful simulation of 500 agents under the congestion tax scenario." + +--- + +### Figure 2: Final Folder Structure with Deliverable + +*Description for screenshot:* Open your file explorer (Finder on macOS, or VS Code's file tree) showing the `austin-congestion-study` folder expanded. Highlight or circle the `mode_shift_report.json` file. + +**Caption:** "Figure 2: Study folder structure showing the generated mode_shift_report.json deliverable file." + +--- + +## Troubleshooting + +| Error | Solution | +|-------|----------| +| `command not found: extropy` | Run `pip install extropy-cli` again, or check your PATH | +| `ANTHROPIC_API_KEY not set` | Export your API key (see Step 2) | +| `Rate limit exceeded` | Wait 60 seconds and retry the command | +| `Simulation timeout` | Reduce agent count with `-n 250` in Step 7 | + +--- + +## Contact + +For technical support with Extropy, consult the documentation or contact the development team. diff --git a/docs/fixes.md b/docs/fixes.md deleted file mode 100644 index 4f33c31..0000000 --- a/docs/fixes.md +++ /dev/null @@ -1,93 +0,0 @@ -# Fixes and Improvements Log - -## 2026-02-15: agent_focus prompt ambiguity - -**Problem**: The `extropy spec` command was generating `agent_focus: "suburban homeowners with school-age children"` for a community scenario, which caused partners to become NPCs instead of active agents. For a book ban community reaction scenario, both partners should be simulated. - -**Root cause**: The sufficiency check prompt in `extropy/population/spec_builder/sufficiency.py` didn't explain to the LLM that `agent_focus` controls household sampling modes: -- `"families"` / `"households"` → everyone simulated -- `"couples"` / `"partners"` → both adults simulated, kids are NPCs -- specific roles like `"surgeons"` → one person per household - -The LLM just extracted a literal description without understanding the semantic implications. - -**Fix**: Updated the prompt and schema description to explicitly document: -1. The three sampling modes and their trigger keywords -2. When to use each mode (communities → families, professional studies → specific role) -3. Concrete examples mapping scenario types to correct `agent_focus` values - -**Files changed**: -- `extropy/population/spec_builder/sufficiency.py` — schema description and prompt text - -**Verification**: `tests/test_agent_focus.py` (34 tests) all pass. - ---- - -## 2026-02-15: Persona renderer duplicate sections and missing punctuation - -**Problem**: The persona renderer was outputting duplicate sections. For example: -- `## Who I Am` rendered age, gender, state from `intro_template` -- `## About Me` rendered the same attributes again from the `basic_identity` group - -Additionally, sentences were missing periods, causing run-on text like: -``` -I'm 49 years old I'm a dad in this community My family lives in Kansas -``` - -**Root cause**: The `render_persona()` function in `extropy/population/persona/renderer.py`: -1. Rendered `intro_template` attributes, then also rendered them in their respective groups -2. Joined phrases with spaces but didn't ensure sentence-ending punctuation - -**Fix**: -1. Added `extract_intro_attributes()` helper to parse `{attribute}` placeholders from `intro_template` -2. Modified `render_persona()` to track and exclude intro attributes from group rendering (same pattern used for `decision_relevant_attributes`) -3. Added `_ensure_period()` helper to ensure all phrases end with `.`, `!`, or `?` - -**Files changed**: -- `extropy/population/persona/renderer.py` — added `extract_intro_attributes()`, `_ensure_period()`, updated `render_persona()` - -**Verification**: `tests/test_persona*.py` (6 tests) all pass. Manual verification shows clean persona output without duplicates. - ---- - -## 2026-02-15: Azure OpenAI schema rejection for actions array - -**Problem**: Simulation failed with Azure OpenAI (gpt-5-mini) rejecting the reasoning schema: -``` -Invalid schema for response_format 'agent_reasoning': In context=('properties', 'actions', 'items'), 'additionalProperties' is required to be supplied and to be false. -``` - -**Root cause**: Azure OpenAI has stricter JSON schema requirements than OpenAI direct. The `actions` array items (objects with `type`, `who`, `topic` properties) were missing `additionalProperties: false`. - -**Fix**: Added `"additionalProperties": False` and included `"topic"` in `required` array for both `actions` item schemas in `extropy/simulation/reasoning.py`: -1. Line ~464 (two-pass schema) -2. Line ~640 (merged-pass schema) - -Azure requires ALL properties to be listed in `required`, not just a subset. - -**Files changed**: -- `extropy/simulation/reasoning.py` — added `additionalProperties: False` and `"topic"` to required array - -**Verification**: Lint passes. Re-run simulation to confirm. - ---- - -## 2026-02-15: Azure OpenAI schema rejection for conversation_turn - -**Problem**: Simulation failed with Azure OpenAI rejecting the conversation_turn schema: -``` -Invalid schema for response_format 'conversation_turn': In context=(), 'required' is required to be supplied and to be an array including every key in properties. Missing 'internal_reaction' -``` - -**Root cause**: Same Azure strictness issue. The conversation turn schema had `internal_reaction` in properties but not in the `required` array. - -**Fix**: Updated `_build_conversation_schema()` in `extropy/simulation/conversation.py` to include all properties in the `required` array and added `additionalProperties: False`: -```python -"required": ["response", "internal_reaction", "updated_sentiment", "updated_conviction"], -"additionalProperties": False, -``` - -**Files changed**: -- `extropy/simulation/conversation.py` — line 390-391 - -**Verification**: Lint passes. Re-run simulation to confirm. diff --git a/docs/logging-guidelines.md b/docs/logging-guidelines.md deleted file mode 100644 index fa6ecdc..0000000 --- a/docs/logging-guidelines.md +++ /dev/null @@ -1,25 +0,0 @@ -# Logging Guidelines - -These rules keep logging useful without leaking sensitive data. - -## Principles - -- Use module loggers: `logger = logging.getLogger(__name__)`. -- Keep user-facing CLI output in `console.print(...)`; use `logger.*` for diagnostics. -- Never log raw prompts, private reasoning text, API keys, auth headers, or tokens. -- Prefer structured metadata in logs (model name, token counts, timings, IDs). -- Use levels consistently: - - `DEBUG`: high-volume diagnostics safe for local troubleshooting. - - `INFO`: normal progress and timing. - - `WARNING`: retries, degraded behavior, recoverable failures. - - `ERROR`: terminal failures for the current operation. - -## Provider Debug Logs - -`extropy/core/providers/logging.py` writes sanitized JSON logs: - -- Secret fields are replaced with `[REDACTED_SECRET]`. -- Prompt/content-like text fields are replaced with `[REDACTED_TEXT length=N]`. -- Responses are summarized and sanitized before writing. - -If you add new request/response fields, ensure they pass through the sanitizer. diff --git a/docs/plans/cli-refactor.md b/docs/plans/cli-refactor.md deleted file mode 100644 index 64ad7bf..0000000 --- a/docs/plans/cli-refactor.md +++ /dev/null @@ -1,554 +0,0 @@ -# CLI Refactor Plan - -## Overview - -Simplify the CLI pipeline from the current fragmented flow to a clean, linear pipeline with automatic file naming, study folder management, and scenario-centric design. - -**New Pipeline:** -``` -spec → scenario → persona → sample → network → simulate → results -``` - -**Key Changes:** -1. `scenario` absorbs `extend` - one command does attribute extension + scenario config -2. Auto-versioned file naming - no manual `-o` for every file -3. Study folder structure - everything organized under study directory -4. `study.db` inferred from cwd - no `--study-db` everywhere -5. Scenario-centric keying - downstream commands key off scenario, not population - ---- - -## Study Folder Structure - -``` -{study-name}/ - study.db # canonical SQLite store - population.v1.yaml # base population spec - population.v2.yaml # iterations - scenario/ - {scenario-name}/ - scenario.v1.yaml # scenario spec (includes extended attrs) - scenario.v2.yaml # iterations - persona.v1.yaml # persona config for this scenario - {another-scenario}/ - scenario.v1.yaml - persona.v1.yaml -``` - -**Default study folder:** `study-{timestamp}` if not specified - -**study.db keying:** Changes from `population_id` to `scenario_id` as primary key for: -- `agents` table -- `network_edges` table -- `simulation_runs` table - ---- - -## Command Specifications - -### 1. `extropy spec` - -Creates base population spec with attribute definitions and distributions. - -```bash -# Create new study -extropy spec "500 German surgeons who work in hospitals" -o surgeons -# → creates surgeons/ -# → creates surgeons/study.db -# → creates surgeons/population.v1.yaml - -# Custom population name -extropy spec "German surgeons" -o surgeons/hospital-surgeons -# → surgeons/hospital-surgeons.v1.yaml - -# Iterate on existing (from within study folder) -cd surgeons -extropy spec "German surgeons, add income distribution" -# → population.v2.yaml (auto-increment) - -# From anywhere, specify study -extropy --study surgeons spec "updated description" -# → surgeons/population.v2.yaml -``` - -**Flags:** -| Flag | Required | Description | -|------|----------|-------------| -| `-o, --output` | Yes (if not in study) | Study folder or `folder/name` | -| `--study` | No | Global flag to specify study folder | - -**Changes from current:** -- Remove `size` from spec - moved to `sample -n` -- Auto-create study folder + study.db -- Auto-version files - -**Output:** `population.vN.yaml` with: -- `meta.description` -- `meta.geography` -- `meta.agent_focus` -- `attributes[]` with full distribution specs -- `sampling_order[]` -- `grounding` summary - ---- - -### 2. `extropy scenario` - -Creates scenario spec by extending population with scenario-specific attributes AND defining event/exposure/outcome config. **Absorbs current `extend` command.** - -```bash -cd surgeons - -# Create new scenario -extropy scenario "AI diagnostic tool adoption, announced via hospital email, - measure adoption intent and trust over 2 weeks" -o ai-adoption -# → scenario/ai-adoption/scenario.v1.yaml -# Uses latest population.vN.yaml by default - -# Pin population version -extropy scenario "vaccine mandate" -o vaccine @pop:v1 -# → scenario/vaccine/scenario.v1.yaml with base_population: population.v1 - -# Use explicit latest -extropy scenario "..." -o test @pop:latest -# → resolves to current latest, stores concrete version - -# Iterate on existing scenario -extropy scenario "AI adoption, add social media exposure channel" -o ai-adoption -# → scenario/ai-adoption/scenario.v2.yaml - -# Rebase to new population (cheap, no LLM re-run) -extropy scenario ai-adoption --rebase @pop:v2 -# → validates compatibility -# → creates scenario.v2.yaml with base_population: population.v2 -# → copies attrs/config from scenario.v1.yaml -``` - -**Flags:** -| Flag | Required | Description | -|------|----------|-------------| -| `-o, --output` | Yes (for new) | Scenario name | -| `@pop:vN` | No | Pin population version | -| `@pop:latest` | No | Explicit latest (resolves at creation) | -| `--rebase` | No | Cheap update to new population | - -**`--rebase` safety detection:** -1. Load scenario's attribute dependencies (what base attrs does it reference?) -2. Check new population has all required attributes -3. If missing → error: "Unsafe rebase: scenario depends on `income` which doesn't exist in population.v2" -4. If safe → create new scenario version with updated reference - -**Output:** `scenario.vN.yaml` with: -```yaml -meta: - name: ai-adoption - description: "AI diagnostic tool adoption..." - base_population: population.v2 # concrete reference, never "latest" - created_at: ... - -# Extended attributes (what extend currently does) -attributes: - - name: ai_familiarity - type: categorical - ... - - name: tech_anxiety - type: float - ... - -# Scenario config (current scenario command) -event: - type: announcement - content: "Hospital introducing AI diagnostic tool..." - source: hospital_admin - credibility: 0.85 - -seed_exposure: - channels: [...] - rules: [...] - -interaction: - primary_model: deliberation - ... - -spread: - share_probability: 0.3 - ... - -outcomes: - suggested_outcomes: - - name: adoption_intent - type: categorical - options: [will_adopt, considering, resistant, opposed] - ... - -simulation: - max_timesteps: 14 - timestep_unit: day - ... -``` - -**Validation:** -1. Load base population -2. Merge attributes (base + scenario) -3. Recompute sampling_order via topological sort -4. Run structural validation on merged spec -5. If invalid → save as `scenario.vN.invalid.yaml` -6. If valid → save as `scenario.vN.yaml` - ---- - -### 3. `extropy persona` - -Generates persona rendering config from scenario's full attribute set. - -```bash -cd surgeons - -extropy persona -s ai-adoption -# → scenario/ai-adoption/persona.v1.yaml - -# Iterate -extropy persona -s ai-adoption -# → persona.v2.yaml if v1 exists - -# Pin scenario version -extropy persona -s ai-adoption@v1 -``` - -**Flags:** -| Flag | Required | Description | -|------|----------|-------------| -| `-s, --scenario` | Yes (if multiple) | Scenario name, auto-select if only one | -| `@vN` | No | Pin scenario version | - -**What it does:** -1. Load scenario spec (for full attribute list including base + extended) -2. Run LLM pipeline to generate phrasings (5 steps) -3. Save persona config - -**Note:** Population stats (mean/std for relative positioning) are computed at render time from sampled agents, NOT stored in persona.yaml. This keeps persona independent of sample size. - -**Output:** `persona.vN.yaml` with: -- `intro_template` -- `treatments[]` -- `groups[]` -- `phrasings.boolean[]` -- `phrasings.categorical[]` -- `phrasings.relative[]` -- `phrasings.concrete[]` -- NO `population_stats` (computed at render time) - ---- - -### 4. `extropy sample` - -Samples agents from merged population + scenario attributes. - -```bash -cd surgeons - -extropy sample -s ai-adoption -n 500 -# → loads scenario.vN.yaml + referenced population -# → merges attributes -# → validates merged spec -# → samples 500 agents -# → saves to study.db (scenario_id=ai-adoption) - -# With seed -extropy sample -s ai-adoption -n 500 --seed 42 - -# Re-sample (overwrites existing agents for this scenario) -extropy sample -s ai-adoption -n 1000 --seed 123 - -# Pin versions -extropy sample -s ai-adoption@v1 -n 500 -``` - -**Flags:** -| Flag | Required | Description | -|------|----------|-------------| -| `-s, --scenario` | Yes (if multiple) | Scenario name | -| `-n, --count` | Yes | Number of agents to sample | -| `--seed` | No | Random seed for reproducibility | -| `@vN` | No | Pin scenario version | - -**Pre-flight validation:** -1. Check persona.vN.yaml exists for this scenario → error if missing -2. Load scenario + population -3. Merge attributes -4. Validate merged spec (structural checks) -5. If valid → proceed to sample -6. If invalid → error with details - -**Output:** Agents saved to `study.db`: -- `agents` table keyed by `scenario_id` -- Each agent has all attributes (base + scenario) - ---- - -### 5. `extropy network` - -Generates social network from sampled agents. - -```bash -cd surgeons - -extropy network -s ai-adoption -# → loads agents from study.db (scenario_id=ai-adoption) -# → generates network -# → saves to study.db - -# With config -extropy network -s ai-adoption --avg-degree 15 --seed 42 - -# Generate network config from population spec -extropy network -s ai-adoption --generate-config -``` - -**Flags:** -| Flag | Required | Description | -|------|----------|-------------| -| `-s, --scenario` | Yes (if multiple) | Scenario name | -| `--avg-degree` | No | Target average degree | -| `--seed` | No | Random seed | -| `--generate-config` | No | Generate config via LLM | -| Other existing flags | No | Quality profile, workers, etc. | - -**Pre-flight:** -- Check agents exist for this scenario → error if missing - -**Output:** Network saved to `study.db`: -- `network_edges` table keyed by `scenario_id` - ---- - -### 6. `extropy simulate` - -Runs simulation using scenario spec + agents + network. - -```bash -cd surgeons - -extropy simulate -s ai-adoption -# → loads scenario spec -# → loads agents + network from study.db -# → loads persona config -# → runs simulation -# → saves results to study.db - -# With options -extropy simulate -s ai-adoption --seed 42 --strong openai/gpt-5 --fast openai/gpt-5-mini -``` - -**Flags:** -| Flag | Required | Description | -|------|----------|-------------| -| `-s, --scenario` | Yes (if multiple) | Scenario name | -| `--seed` | No | Random seed | -| `--strong` | No | Strong model for reasoning | -| `--fast` | No | Fast model for classification | -| Other existing flags | No | Rate limits, checkpointing, etc. | - -**Pre-flight:** -- Check agents exist → error if missing -- Check network exists → error if missing -- Check persona config exists → error if missing - -**Persona rendering:** -- Load `persona.vN.yaml` for phrasings -- Compute population stats from agents in study.db (fresh, not stale) -- Render personas at simulation time - -**Output:** Results saved to `study.db`: -- `simulation_runs` table -- `agent_states` table -- `timestep_summaries` table -- etc. - ---- - -### 7. `extropy results` - -View simulation results. - -```bash -cd surgeons - -extropy results -s ai-adoption -# → shows latest run for this scenario - -extropy results -s ai-adoption --timeline -extropy results -s ai-adoption --segment age_group -extropy results -s ai-adoption --agent agent_123 -``` - -**Flags:** -| Flag | Required | Description | -|------|----------|-------------| -| `-s, --scenario` | No | Scenario name (latest if omitted) | -| `--run-id` | No | Specific run ID | -| `--timeline` | No | Show timeline view | -| `--segment` | No | Segment by attribute | -| `--agent` | No | Show single agent | - ---- - -### 8. `extropy validate` - -Validates spec files. Updated to handle new structure. - -```bash -# Validate population spec -extropy validate population.v1.yaml - -# Validate scenario spec (auto-detects, loads + validates merged) -extropy validate scenario/ai-adoption/scenario.v1.yaml -# → loads referenced population -# → merges attributes -# → validates merged result - -# Validate persona config -extropy validate scenario/ai-adoption/persona.v1.yaml -``` - -**Auto-detection:** -- `population.*.yaml` → population validation -- `scenario.*.yaml` → scenario validation (includes merge validation) -- `persona.*.yaml` → persona config validation - ---- - -## Global `--study` Flag - -All commands support `--study` to specify study folder from anywhere: - -```bash -extropy --study ~/projects/surgeons sample -s ai-adoption -n 500 -extropy --study surgeons simulate -s ai-adoption -``` - -If not specified, inferred from cwd: -- If cwd contains `study.db` → use cwd -- If cwd is inside a study folder → find parent with `study.db` -- Otherwise → error "Not in a study folder. Use --study or -o to specify." - ---- - -## Deprecations & Removals - -| Command | Action | -|---------|--------| -| `extend` | **Remove** - absorbed into `scenario` | -| `--study-db` flag | **Remove** - use `--study` or infer from cwd | -| `spec --size` / `meta.size` | **Move** to `sample -n` | - ---- - -## Database Schema Changes - -### New: `scenario_id` as primary key - -```sql --- agents table -ALTER TABLE agents ADD COLUMN scenario_id TEXT; --- Primary key becomes (scenario_id, agent_id) - --- network_edges table -ALTER TABLE network_edges ADD COLUMN scenario_id TEXT; --- Primary key becomes (scenario_id, source_id, target_id) - --- simulation_runs table already has scenario linkage via population_id --- Update to use scenario_id instead -``` - -### Migration - -No migration for existing study.db files. Old format won't work with new CLI. Users re-run pipeline. - ---- - -## Implementation Phases - -### Phase 1: Foundation -1. Add `--study` global flag to CLI app -2. Add study folder detection logic -3. Add auto-versioning logic for YAML files -4. Update `study.db` schema with `scenario_id` - -### Phase 2: Spec Command -1. Remove `size` from spec (move to sample) -2. Add auto study folder creation -3. Add auto-versioning -4. Update sufficiency check (no size required) - -### Phase 3: Scenario Command (Big One) -1. Absorb `extend` logic into scenario -2. New scenario YAML schema with `attributes` section -3. Add `@pop:vN` syntax parsing -4. Add `--rebase` flag with safety detection -5. Merge + validate flow -6. Auto-versioning - -### Phase 4: Persona Command -1. Update to use `-s scenario` instead of spec file -2. Remove population stats from output (compute at render time) -3. Auto-versioning - -### Phase 5: Sample Command -1. Change from spec file input to `-s scenario` -2. Add `-n count` as required flag -3. Merge population + scenario at sample time -4. Pre-flight persona check -5. Save with `scenario_id` key - -### Phase 6: Network Command -1. Change to `-s scenario` input -2. Save with `scenario_id` key - -### Phase 7: Simulate Command -1. Change to `-s scenario` input -2. Update persona rendering to compute stats at runtime -3. Load from `scenario_id` keyed tables - -### Phase 8: Results & Validate -1. Update results to use `-s scenario` -2. Update validate to handle merged scenario validation - -### Phase 9: Cleanup -1. Remove `extend` command -2. Remove `--study-db` flag -3. Update docs -4. Update tests - ---- - -## File Changes Summary - -| File | Change | -|------|--------| -| `extropy/cli/app.py` | Add `--study` global flag, study detection | -| `extropy/cli/commands/spec.py` | Remove size, add auto-versioning | -| `extropy/cli/commands/scenario.py` | Absorb extend, new schema, `@pop:` syntax | -| `extropy/cli/commands/extend.py` | **Delete** | -| `extropy/cli/commands/persona.py` | `-s scenario` input, remove stats storage | -| `extropy/cli/commands/sample.py` | `-s scenario` + `-n count`, merge logic | -| `extropy/cli/commands/network.py` | `-s scenario` input | -| `extropy/cli/commands/simulate.py` | `-s scenario`, runtime stats | -| `extropy/cli/commands/results.py` | `-s scenario` input | -| `extropy/cli/commands/validate.py` | Merged scenario validation | -| `extropy/cli/commands/__init__.py` | Remove extend import | -| `extropy/storage/study_db.py` | Add `scenario_id` to schema | -| `extropy/core/models/scenario.py` | Add `attributes` section to schema | -| `extropy/population/persona/renderer.py` | Compute stats at render time | -| `extropy/cli/utils.py` | Add versioning helpers | -| New: `extropy/cli/study.py` | Study folder detection, versioning logic | - ---- - -## Open Questions - -1. **Config command** - should `extropy config` also be study-aware? Currently global. - -2. **Other commands** - `inspect`, `query`, `report`, `export`, `chat`, `migrate` - leave as-is for now? They may need `--study` support later. - -3. **Backwards compat** - should we support `--study-db` with deprecation warning for a version? Or hard break? diff --git a/docs/simulation-v2-architecture.md b/docs/simulation-v2-architecture.md index 24c5044..f2576b0 100644 --- a/docs/simulation-v2-architecture.md +++ b/docs/simulation-v2-architecture.md @@ -419,17 +419,14 @@ seed_exposure: channels: - name: mainstream_news_media description: "TV and online news coverage" - reach: broadcast credibility_modifier: 1.1 experience_template: "I saw a news segment about this on TV." - name: social_media_feeds description: "Posts appearing in social feeds" - reach: broadcast credibility_modifier: 0.8 experience_template: "I noticed some posts about this online." - name: email_notification description: "Direct email from the company" - reach: targeted credibility_modifier: 1.2 experience_template: "I got an email about this." ``` diff --git a/docs/skill-rewrite-plan.md b/docs/skill-rewrite-plan.md deleted file mode 100644 index 3ed84ef..0000000 --- a/docs/skill-rewrite-plan.md +++ /dev/null @@ -1,206 +0,0 @@ -# Plan: Rewrite Extropy Skills - -## Execution Timing - -**WAIT for pending CLI changes before executing this plan.** - -Known pending changes: -- `network --generate-config` becoming default behavior -- [Any other CLI changes in progress] - -Once CLI is stable, execute this plan against the final CLI state. - ---- - -## Context - -The current Extropy skills (6 files, ~1,667 lines) are outdated: -1. **Wrong CLI patterns**: Reference old commands (`extend`, `--study-db` everywhere) that no longer exist -2. **Missing the craft**: Don't teach HOW to write good spec/scenario prompts - just list commands -3. **No real examples**: Generic descriptions instead of concrete, annotated examples -4. **Over-fragmented**: 6 files is too many; user wants 2 focused files - -The user provided excellent guidance on what makes good vs bad prompts (see conversation above), plus real examples (ASI announcement, crypto airdrop, inflation shock, Netflix password sharing). - -## Goals - -1. Teach the **craft** of writing good spec and scenario prompts -2. Use **current CLI** patterns (`spec → scenario → persona → sample → network → simulate`) -3. Include **annotated real examples** showing why they work -4. Consolidate to **2 files**: SKILL.md (the craft) + REFERENCE.md (commands/troubleshooting) - ---- - -## File Structure - -### SKILL.md (~500 lines) -The main skill entry point. Focuses on **understanding Extropy** and **writing effective prompts**. - -**Sections:** -1. **What Extropy Is** (~50 lines) - - The pitch: **Predictive intelligence through population simulation** (not surveys, not polls) - - Synthetic agents grounded in real-world distributions, connected in social networks, each reasoning individually via LLM - - What you get back: - - Distributional predictions by segment - - **Open-ended responses** when you don't know the outcome space (let agents tell you what they'd do, discover categories post-hoc) - - Reasoning traces explaining *why* - - Opinion dynamics over time - - Network effects and conversation transcripts - -2. **What It Can and Can't Do** (~30 lines) - - Works well: policy, pricing, product, crisis, messaging, community - - Limitations: no org charts, no real-time, no individual prediction - -3. **The Craft of Spec Prompts** (~100 lines) - - Mental model: "Constrain WHO, let LLM discover WHAT" - - Spec creates reusable base population; scenario extends it - - **Bad patterns** with examples: - - Too vague: "Americans" - - Too prescriptive: dictating exact brackets - - Too narrow: over-specialized for one scenario - - **Good patterns** with examples: - - Clear population boundary - - General attitudinal/psychological dimensions (trust, risk, tech adoption) - - Reusable across scenarios - - **Annotated example**: The US adults spec from user's chat - -4. **The Craft of Scenario Prompts** (~150 lines) - - Mental model: Scenario extends base with event-specific attributes - - **The evolving test**: "Does the network change the outcome?" - - Non-evolving: concrete personal decisions, population heterogeneity is the story (Netflix) - - Evolving: information/credibility dynamics, social influence matters (ASI) - - **Bad patterns**: - - Too abstract: no concrete details - - Predetermined framing: telling agents how to feel - - Wrong timeline choice: evolving when should be static, or vice versa - - Too many timeline events: drowns out network propagation - - Wrong timestep granularity - - **Good patterns**: - - Concrete and specific: named entities, exact numbers - - Neutral framing: present facts, let agents disagree - - Strategic timeline events: 2-4 max, spaced for propagation - - **Timestep table**: hours/days/weeks/months with use cases - - **Annotated examples**: - - Non-evolving: Netflix password sharing (from extropy.run) - - Evolving: ASI announcement (from user's chat) - - Edge case: Inflation shock (hybrid dynamics) - -5. **Quick Pipeline** (~50 lines) - - Current CLI flow with correct commands - - Study folder structure - - Common flags - -6. **Fidelity and Cost** (~30 lines) - - Low/medium/high tiers - - When to use each - - Cost estimates - -7. **Operating Mode** (~40 lines) - - **Always set `cli.mode agent`** at start of session for JSON output - - Model configuration: `extropy config set simulation.strong`, etc. - - **Escalation policy**: When to stop and ask user vs proceed autonomously - - Escalate: same error twice, core assumptions change, cost vs accuracy tradeoff, sensitive content - - Don't escalate: normal pipeline execution, validation passes, expected behavior - - **Non-interactive flags**: `-y` to skip confirmations, `--use-defaults` for spec - -8. **Module Reference** (~20 lines) - - Points to REFERENCE.md for commands, troubleshooting, analysis - -### REFERENCE.md (~400 lines) -Condensed operational reference. Everything you need to run simulations. - -**Sections:** -1. **Command Reference** (~150 lines) - - Condensed from docs/commands.md - - Each command: purpose, key flags, example - - Focus on most-used options, not exhaustive - -2. **Study Folder Structure** (~30 lines) - - Directory layout - - File naming conventions (v1, v2, etc.) - -3. **Results & Analysis** (~80 lines) - - Condensed from ANALYSIS.md - - How to read results - - Segment analysis - - Agent deep dives - - Export commands - -4. **Troubleshooting** (~100 lines) - - Condensed from TROUBLESHOOTING.md - - Common failure patterns - - Quality gates (quick checklist) - - When to escalate - -5. **Configuration** (~60 lines) - - **Agent mode setup**: `extropy config set cli.mode agent` - - Model configuration: - - `models.strong` / `models.fast` for pipeline (spec, scenario, persona) - - `simulation.strong` / `simulation.fast` for simulation passes - - API keys (env vars): OPENAI_API_KEY, ANTHROPIC_API_KEY, OPENROUTER_API_KEY, DEEPSEEK_API_KEY, AZURE_API_KEY - - Rate limiting: `simulation.rate_tier`, RPM/TPM overrides - - Typical config for agent use: - ```bash - extropy config set cli.mode agent - extropy config set models.strong openai/gpt-5 - extropy config set simulation.strong anthropic/claude-sonnet-4-6 - ``` - ---- - -## Files to Delete - -After creating new SKILL.md and REFERENCE.md, delete: -- `skills/extropy/OPERATIONS.md` (absorbed into REFERENCE.md) -- `skills/extropy/SCENARIOS.md` (absorbed into SKILL.md) -- `skills/extropy/ANALYSIS.md` (absorbed into REFERENCE.md) -- `skills/extropy/TROUBLESHOOTING.md` (absorbed into REFERENCE.md) -- `skills/extropy/REPORT_TEMPLATE.md` (delete - too rigid, Claude can generate reports on demand) - ---- - -## Key Content to Include - -### Spec Example (Good) -```bash -extropy spec "Nationally representative US adult population (18-80). Must capture the demographic, economic, and attitudinal fault lines that drive divergent responses to major national events — especially technology disruption, economic shocks, and cultural controversies. Beyond standard demographics, prioritize attributes that determine HOW people process and react to news: technology adoption posture, media ecosystem and information sources, institutional trust level, financial margin and economic anxiety, consumer identity and brand relationship patterns, social media behavior and influence. Geographic distribution should span urban/suburban/rural across all major US regions." -o population.v1.yaml -y -``` - -**Why it works**: Defines boundary (US adults 18-80), hints at behavioral dimensions without dictating exact categories, includes attitudinal layer (trust, tech adoption, media habits), reusable across many scenarios. - -### Scenario Example - Non-Evolving (Netflix) -```bash -extropy scenario "Netflix announces enforcement of password sharing policy. Subscribers who share their password with people outside their household must either: pay $8/month for an extra member slot, remove shared users, or accept that shared users will be blocked. The policy takes effect in 30 days. Coverage is widespread across tech news, social media, and mainstream outlets." -o netflix-password -y -``` - -**Why non-evolving**: The decision is concrete and personal. Population heterogeneity (income, household composition, alternatives awareness) drives the interesting splits. My neighbor's opinion doesn't change whether I can afford $8/month. - -### Scenario Example - Evolving (ASI) -```bash -extropy scenario "OpenAI holds a press conference announcing they have achieved artificial superintelligence — a system that demonstrably exceeds human cognitive ability across every domain. Sam Altman presents benchmark results showing the system outperforms top experts in science, law, medicine, strategy, and creative reasoning. The announcement is covered live on all major networks. Timeline events: Day 1 - OpenAI's announcement and initial reactions. Day 3 - Anthropic and Google DeepMind release statements confirming they have independently reached similar capabilities. Day 5 - xAI and Meta confirm the same; Congress announces emergency hearings. Day 7 - Deloitte announces it will replace 30% of its workforce with AI systems within 12 months." -o asi-announcement --timeline evolving -y -``` - -**Why evolving**: Information/credibility dynamics matter. Day 1 people can dismiss as hype. By Day 5 when every lab confirms, denial breaks. Day 7 makes it personal. Network propagation happens in the gaps between events. - -### The Evolving Test -> "Does the network change the outcome? If agents talking to each other would meaningfully shift results compared to running independent LLM calls, use evolving. If not, non-evolving is cleaner and more honest." - -### Timestep Table -| Unit | Use When | -|------|----------| -| Hours | Active crisis, market crash, disaster response | -| Days | Viral news, product launches, policy announcements | -| Weeks | Adoption curves, behavior change, campaign effects | -| Months | Cultural shifts, market trends, policy adaptation | - ---- - -## Verification - -After implementation: -1. Run `extropy --help` and verify all referenced commands exist -2. Run through quick pipeline example to ensure commands work -3. Check that SKILL.md frontmatter is valid YAML -4. Verify cross-references between SKILL.md and REFERENCE.md are correct -5. Test that skill loads in Claude Code: `/extropy` - diff --git a/examples/.DS_Store b/examples/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0= 18", + "value": null, + "reason": "Only adults can have children in household" + } + ], + "grounding": { + "level": "medium", + "method": "researched", + "source": "Census Bureau estimates: ~28% of households have own children under 18 present. Declines sharply once householder is 55+.", + "note": "Base 28% reflects all adults 18-80. Modifiers will significantly reduce probability for age > 55 and for Single/Widowed. Will increase for Married/partnered and ages 25-45." + } + }, + { + "name": "employment_sector", + "distribution": { + "type": "categorical", + "options": [ + "Federal government", + "State/local government", + "Private sector", + "Gig/contract", + "Self-employed", + "Retired", + "Unemployed", + "Not in labor force" + ], + "weights": [ + 0.03, + 0.13, + 0.49, + 0.06, + 0.09, + 0.13, + 0.04, + 0.03 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "researched", + "source": "BLS Current Employment Statistics 2024: federal ~2.9M (~2% of 160M workforce); state/local ~20M (~13%); private ~130M (~82% of employed). Gig/self-employed estimated from multiple BLS/Census sources.", + "note": "Modifiers will sharply increase Retired for age 65+ and decrease Private sector. Gig/contract undercount is a known challenge; 6% is a conservative estimate." + } + }, + { + "name": "job_security_perception", + "distribution": { + "type": "categorical", + "options": [ + "Very secure", + "Somewhat secure", + "Somewhat insecure", + "Very insecure", + "N/A - not employed" + ], + "weights": [ + 0.3, + 0.35, + 0.17, + 0.08, + 0.1 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Gallup Work and Education poll 2023: ~60% of workers feel secure; Pew Research 2023 job satisfaction data. Gallup finds ~25% of workers somewhat/very worried about job loss.", + "note": "N/A bucket (~10%) covers Retired + Unemployed + Not in labor force. Modifiers will assign N/A to non-employed employment_sector values and shift insecurity upward for Gig/contract." + } + }, + { + "name": "financial_margin", + "distribution": { + "type": "categorical", + "options": [ + "Comfortable savings buffer", + "Modest buffer", + "Paycheck-to-paycheck", + "In debt/deficit" + ], + "weights": [ + 0.23, + 0.27, + 0.33, + 0.17 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "researched", + "source": "LendingClub/PYMNTS 2024 report: ~62% of Americans living paycheck to paycheck at some level. Federal Reserve Survey of Consumer Finances: ~37% could not cover $400 emergency.", + "note": "Combined paycheck-to-paycheck + in debt = 50%, reflecting mid-2024 conditions. Modifiers will strongly shift based on household_income tier." + } + }, + { + "name": "investment_profile", + "distribution": { + "type": "categorical", + "options": [ + "No investments", + "401k/IRA only", + "Diversified investor", + "Active trader", + "Crypto holder" + ], + "weights": [ + 0.29, + 0.38, + 0.18, + 0.07, + 0.08 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "researched", + "source": "Federal Reserve SCF 2022: ~54% of families own stocks directly or through retirement accounts. Gallup 2024: 62% of Americans own stock (all-time high). Crypto ownership ~15-20% (NORC 2024).", + "note": "Crypto holder is not mutually exclusive but modeled as primary profile. Active trader and Crypto holder are youthful-skewing categories. Modifiers increase 401k/IRA for higher income and older workers...[truncated]" + } + }, + { + "name": "primary_info_source", + "distribution": { + "type": "categorical", + "options": [ + "Cable/broadcast TV news", + "Social media", + "Online news sites", + "Podcasts", + "Local news", + "Print media", + "Avoids news" + ], + "weights": [ + 0.27, + 0.22, + 0.2, + 0.08, + 0.1, + 0.04, + 0.09 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "researched", + "source": "Pew Research Center 2024 State of News Media: TV still leads at ~27%; social media now #2 source for news, especially for under-30. Reuters Institute Digital News Report 2024 confirms social media at ...[truncated]", + "note": "Age modifiers will be critical: 65+ skews heavily toward TV; 18-34 skews toward social media and podcasts. Education modifiers will push toward online news and podcasts for degree holders." + } + }, + { + "name": "social_media_behavior", + "distribution": { + "type": "categorical", + "options": [ + "Non-user", + "Passive consumer", + "Occasional poster", + "Active poster/Influencer" + ], + "weights": [ + 0.12, + 0.43, + 0.33, + 0.12 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "researched", + "source": "Pew Research Center 2024: 72% of US adults use social media. ~1% are true influencers; majority are passive consumers. Sprout Social 2024: most users read but rarely post.", + "note": "Non-user at 12% aligns with Pew's ~28% non-user rate adjusted for age band (18-80 vs. 18+). Modifiers will increase Non-user for 70+ age group and increase Active poster for 18-35." + } + }, + { + "name": "tech_adoption_posture", + "distribution": { + "type": "categorical", + "options": [ + "Technophobe/Avoider", + "Late majority adopter", + "Early majority adopter", + "Early adopter", + "Innovator" + ], + "weights": [ + 0.16, + 0.26, + 0.34, + 0.18, + 0.06 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Rogers' Diffusion of Innovations model: Innovators ~2.5%, Early Adopters ~13.5%, Early Majority ~34%, Late Majority ~34%, Laggards ~16%. Adapted to 5-bucket mapping.", + "note": "Classic Rogers distribution mapped to 5 categories. Base reflects the theoretical population split; modifiers will shift heavily for age (older = more laggard) and education (higher ed = more early ad...[truncated]" + } + }, + { + "name": "religious_engagement", + "distribution": { + "type": "categorical", + "options": [ + "Secular/None", + "Nominally affiliated", + "Moderately religious", + "Devout" + ], + "weights": [ + 0.28, + 0.25, + 0.28, + 0.19 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Pew Research 2023-2024: ~28% religiously unaffiliated ('nones'); ~63% identify as Christian but practicing rates are lower. Gallup 2024: ~45% attend services monthly or more.", + "note": "Devout (~19%) reflects weekly+ worship attendance. Modifiers will increase Devout/Moderate for Southeast and rural areas, and increase Secular for Northeast and West Coast." + } + }, + { + "name": "political_identity", + "distribution": { + "type": "categorical", + "options": [ + "Strong Democrat", + "Lean Democrat", + "True Independent", + "Lean Republican", + "Strong Republican" + ], + "weights": [ + 0.18, + 0.15, + 0.22, + 0.14, + 0.17 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Gallup 2024 party ID tracking: ~27% Dem, ~27% Rep, ~43% Independent (with leaners splitting roughly evenly). Pew Research 2024 validates similar split with strong partisans ~17-18% each.", + "note": "True Independent at 22% is the pure no-lean group; leaners are about 14-15% each. Weights reflect a roughly even political landscape consistent with competitive 2024 election outcome. Strong partisans...[truncated]" + } + }, + { + "name": "political_engagement_level", + "distribution": { + "type": "categorical", + "options": [ + "Disengaged", + "Single-issue voter", + "Moderate/occasional participant", + "Highly engaged partisan" + ], + "weights": [ + 0.22, + 0.18, + 0.38, + 0.22 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "researched", + "source": "ANES 2024 pilot; Pew Research political engagement typology 2023. Roughly 20-25% disengage from politics; ~22% are highly engaged partisans who follow news daily and donate/volunteer.", + "note": "Modifiers will link Strong Democrat/Republican to Highly Engaged, and True Independent to Disengaged/Moderate. Age modifier: 18-25 skews Disengaged; 55+ skews Highly Engaged." + } + }, + { + "name": "vote_2024_presidential", + "distribution": { + "type": "categorical", + "options": [ + "Trump", + "Harris", + "Third party", + "Did not vote", + "Not eligible" + ], + "weights": [ + 0.3, + 0.29, + 0.03, + 0.3, + 0.08 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "2024 Presidential election: Trump 49.9%, Harris 48.4% of popular vote. Voter turnout ~63% of VEP (~245M eligible). Non-citizen and under-18 adults account for ~8% Not eligible.", + "note": "Did not vote (~30%) reflects ~37% of VEP non-voters scaled to adult population including ineligible. Third party ~3% reflects Stein + Kennedy + others combined. Modifiers will tightly link to politica...[truncated]" + } + }, + { + "name": "midterm_turnout_propensity", + "distribution": { + "type": "categorical", + "options": [ + "Always votes", + "Sometimes votes", + "Rarely votes", + "Never votes" + ], + "weights": [ + 0.38, + 0.28, + 0.19, + 0.15 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "2022 midterm VEP turnout ~47% (US Elections Project). 2018 was 53%. Historical midterm always-voters ~35-40% of adults. Catalist/voter file data show ~35% of adults vote in nearly every election.", + "note": "Always votes ~38% consistent with high-turnout midterm baseline. Modifiers will increase Always votes for 55+, Highly engaged partisans, and Strong partisan identifiers; decrease for 18-30." + } + }, + { + "name": "congressional_district_competitiveness", + "distribution": { + "type": "categorical", + "options": [ + "Safe Republican", + "Lean Republican", + "Swing district", + "Lean Democrat", + "Safe Democrat" + ], + "weights": [ + 0.35, + 0.12, + 0.1, + 0.12, + 0.31 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "researched", + "source": "Cook Political Report 2024 cycle district ratings: ~35 competitive/lean seats out of 435 (~8% swing). Safe R ~200 seats, Safe D ~180, Lean R ~25, Lean D ~20, Toss-up ~10.", + "note": "Population-weighted distribution, not seat-weighted. Swing districts contain fewer people than safe districts but receive outsized political attention. Modifiers will key off us_region and urban_rural...[truncated]" + } + }, + { + "name": "top_issue_2026", + "distribution": { + "type": "categorical", + "options": [ + "Economy/Jobs", + "Abortion/Reproductive rights", + "Immigration", + "Democracy/Rule of law", + "Healthcare", + "Climate", + "Education", + "AI/Technology" + ], + "weights": [ + 0.28, + 0.13, + 0.16, + 0.14, + 0.12, + 0.07, + 0.06, + 0.04 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Gallup Most Important Problem Jan 2025: economy/inflation consistently #1 (~30%); immigration elevated post-2024 election (~15%). Pew Research 2024 voter priorities confirm abortion, democracy, health...[truncated]", + "note": "AI/Technology is an emerging issue (4%) consistent with Pew 2024 finding ~10% cite AI as important but only ~4% name it as the single top issue. Modifiers will shift based on political_identity and em...[truncated]" + } + }, + { + "name": "country_direction_approval", + "distribution": { + "type": "categorical", + "options": [ + "Strongly approve", + "Somewhat approve", + "Somewhat disapprove", + "Strongly disapprove" + ], + "weights": [ + 0.17, + 0.22, + 0.2, + 0.41 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "researched", + "source": "Gallup right-track/wrong-track Jan-Feb 2025: ~39% right direction, ~61% wrong track. Approval of country direction reflects early Trump second-term period with strong partisan split.", + "note": "Strongly approve (~17%) reflects Trump base approval; combined disapprove ~61% matches wrong-track polling. Modifiers will tightly couple to political_identity and financial_margin." + } + }, + { + "name": "consumer_identity", + "distribution": { + "type": "categorical", + "options": [ + "Brand loyal", + "Price/value driven", + "Values/ethics driven", + "Convenience driven" + ], + "weights": [ + 0.22, + 0.38, + 0.18, + 0.22 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "McKinsey Consumer Pulse 2024: ~40% of consumers cite value/price as primary driver; Nielsen IQ 2024: brand loyalty declining, private label share growing. Edelman Trust Barometer 2024: ~18% are values...[truncated]", + "note": "Price/value driven at 38% reflects inflationary environment and value-seeking. Convenience driven at 22% captures Amazon-era shopping behavior. Modifiers will increase Price-driven for lower income an...[truncated]" + } + }, + { + "name": "institutional_trust", + "distribution": { + "type": "categorical", + "options": [ + "High trust", + "Moderate trust", + "Low trust", + "Distrustful/Conspiratorial" + ], + "weights": [ + 0.13, + 0.31, + 0.37, + 0.19 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Gallup Confidence in Institutions 2024: trust in most institutions at or near historic lows. Only ~13% express 'a great deal' of confidence in Congress; media trust at record low. Edelman Trust Barome...[truncated]", + "note": "Distrustful/Conspiratorial at 19% consistent with PRRI 2024 QAnon/conspiracy belief prevalence (~18-22%). High trust only 13% as most institutions score poorly. Modifiers will reduce trust for Strong ...[truncated]" + } + }, + { + "name": "risk_tolerance", + "distribution": { + "type": "normal", + "mean": 0.45, + "std": 0.22, + "min": 0.0, + "max": 1.0, + "mean_formula": "max(0.1, min(0.9, 0.60 - 0.003 * (age - 30)))", + "std_formula": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "options": null, + "weights": null, + "probability_true": null + }, + "constraints": [ + { + "type": "hard_min", + "value": 0.0, + "expression": null, + "reason": "Risk tolerance bounded at 0 (fully risk averse)" + }, + { + "type": "hard_max", + "value": 1.0, + "expression": null, + "reason": "Risk tolerance bounded at 1 (fully risk tolerant)" + } + ], + "grounding": { + "level": "medium", + "method": "researched", + "source": "Vanguard/Fidelity risk tolerance research: risk tolerance declines with age at roughly 0.3 percentage points per year after 30. Federal Reserve SCF: younger and wealthier households take more financia...[truncated]", + "note": "Mean formula decreases risk tolerance by 0.003 per year of age above 30 (yields ~0.60 at age 30, ~0.45 at age 80). Std of 0.22 allows for wide individual variation. Income modifiers will increase mean...[truncated]" + } + }, + { + "name": "economic_anxiety", + "distribution": { + "type": "normal", + "mean": 0.55, + "std": 0.22, + "min": 0.0, + "max": 1.0, + "mean_formula": null, + "std_formula": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "options": null, + "weights": null, + "probability_true": null + }, + "constraints": [ + { + "type": "hard_min", + "value": 0.0, + "expression": null, + "reason": "Anxiety bounded at 0" + }, + { + "type": "hard_max", + "value": 1.0, + "expression": null, + "reason": "Anxiety bounded at 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Gallup Economic Confidence Index early 2025: -25 (negative territory). WSJ/NORC poll 2024: ~65% say economy is poor/not so good. Michigan Consumer Sentiment early 2025 shows elevated anxiety.", + "note": "Base mean of 0.55 reflects a majority-anxious environment as of early 2025. Std of 0.22 allows for variation. Modifiers will strongly increase for Paycheck-to-paycheck and In debt/deficit financial_ma...[truncated]" + } + } + ] + }, + "name": "conditional_base_hydration", + "type": "tool_use", + "caller": { + "type": "direct" + } + } + ], + "model": "claude-sonnet-4-6", + "role": "assistant", + "stop_reason": "tool_use", + "stop_sequence": null, + "type": "message", + "usage": { + "cache_creation": { + "ephemeral_1h_input_tokens": "[REDACTED_SECRET]", + "ephemeral_5m_input_tokens": "[REDACTED_SECRET]" + }, + "cache_creation_input_tokens": "[REDACTED_SECRET]", + "cache_read_input_tokens": "[REDACTED_SECRET]", + "input_tokens": 56897, + "output_tokens": 9280, + "server_tool_use": { + "web_search_requests": 3, + "web_fetch_requests": 0 + }, + "service_tier": "standard", + "inference_geo": "global" + } + }, + "sources_extracted": [ + "https://statisticalatlas.com/United-States/Marital-Status", + "https://www.census.gov/newsroom/press-releases/2025/educational-attainment-data.html", + 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Std set wide to capture full range from poverty to affluent." + } + }, + { + "name": "marital_status", + "distribution": { + "type": "categorical", + "options": [ + "Married/partnered", + "Single", + "Divorced/separated", + "Widowed" + ], + "weights": [ + 0.49, + 0.31, + 0.14, + 0.06 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1.0" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "USAFacts 2024; Census Bureau marital status data 2024", + "note": "47.1% of households headed by married couples in 2024; ~49% adults married/partnered including cohabiting. Single/never-married ~31%, divorced/separated ~14%, widowed ~6%. Age modifiers critical: youn...[truncated]" + } + }, + { + "name": "household_size", + "distribution": { + "type": "normal", + "mean": 2.53, + "std": 1.4, + "mean_formula": null, + "std_formula": null, + "min": 1, + "max": 10, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "options": null, + "weights": null, + "probability_true": null + }, + "constraints": [ + { + "type": "hard_min", + "value": 1, + "expression": null, + "reason": "Household must have at least 1 person (respondent)" + }, + { + "type": "hard_max", + "value": 10, + "expression": null, + "reason": "Households of 10+ are extreme outliers (<0.1%)" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "US Census Bureau ACS 2024; average household size ~2.53", + "note": "US average household size is 2.53. Married households average ~3.1, single-person ~1.0. Marital status modifier will shift mean upward for married/partnered." + } + }, + { + "name": "has_children", + "distribution": { + "type": "boolean", + "probability_true": 0.28, + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "options": null, + "weights": null + }, + "constraints": [ + { + "type": "expression", + "expression": "not (has_children and age < 18)", + "value": null, + "reason": "Under-18s cannot have children in household as adult respondents (floor is 18)" + } + ], + "grounding": { + "level": "medium", + "method": "researched", + "source": "Census Bureau CPS 2024; ~28% of households have children under 18", + "note": "About 28% of all adults have children under 18 at home. Strong age dependency: near-zero for 18-22, peaks at ~45% for ages 30-45, near-zero for 65+. Marital status also a major modifier." + } + }, + { + "name": "employment_sector", + "distribution": { + "type": "categorical", + "options": [ + "Federal government", + "State/local government", + "Private sector", + "Gig/contract", + "Self-employed", + "Retired", + "Unemployed", + "Not in labor force" + ], + "weights": [ + 0.02, + 0.13, + 0.52, + 0.06, + 0.07, + 0.12, + 0.03, + 0.05 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1.0" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "BLS Employment and Wages 2024; BLS CPS 2024", + "note": "BLS 2024: federal ~3M, state ~4.8M, local ~14.6M employees; private ~132.6M. Self-employed ~10% of employed. Retired and NILF weighted for full adult 18-80 population. Age modifiers critical: retired ...[truncated]" + } + }, + { + "name": "job_security_perception", + "distribution": { + "type": "categorical", + "options": [ + "Very secure", + "Somewhat secure", + "Somewhat insecure", + "Very insecure", + "N/A - not employed" + ], + "weights": [ + 0.3, + 0.35, + 0.13, + 0.07, + 0.15 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1.0" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Gallup Job Security surveys; Federal Reserve SHED 2024", + "note": "Majority of employed workers report feeling secure. ~20% report some insecurity. N/A bucket covers retired, unemployed, NILF (~15-20% of adult population)." + } + }, + { + "name": "financial_margin", + "distribution": { + "type": "categorical", + "options": [ + "Comfortable savings buffer", + "Modest buffer", + "Paycheck-to-paycheck", + "In debt/deficit" + ], + "weights": [ + 0.25, + 0.27, + 0.33, + 0.15 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1.0" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Federal Reserve SHED 2024; multiple paycheck-to-paycheck surveys 2024", + "note": "Approximately 63-66% of Americans live paycheck-to-paycheck per various 2024 surveys. Fed SHED shows ~28% skipped medical care due to cost. Comfortable savers ~25%, modest buffer ~27%, P2P ~33%, defic...[truncated]" + } + }, + { + "name": "investment_profile", + "distribution": { + "type": "categorical", + "options": [ + "No investments", + "401k/IRA only", + "Diversified investor", + "Active trader", + "Crypto holder" + ], + "weights": [ + 0.29, + 0.38, + 0.17, + 0.08, + 0.08 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1.0" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Federal Reserve SHED 2024; Gallup Stock Market Ownership 2024", + "note": "~55-58% of Americans own stocks (many via retirement accounts). ~29% have no investments. Crypto ownership ~15-20% but as primary profile ~8%. Diversified investor and active trader reflect smaller ed...[truncated]" + } + }, + { + "name": "primary_info_source", + "distribution": { + "type": "categorical", + "options": [ + "Cable/broadcast TV news", + "Social media", + "Online news sites", + "Podcasts", + "Local news", + "Print media", + "Avoids news" + ], + "weights": [ + 0.28, + 0.25, + 0.18, + 0.08, + 0.09, + 0.04, + 0.08 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1.0" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Reuters Institute Digital News Report 2024; Pew Research Media consumption 2024", + "note": "TV still largest single source ~28%, social media surging esp. under 35 ~25%, online news ~18%. Podcasts growing ~8%. Print declining ~4%. Age modifiers essential: older adults heavily TV, younger hea...[truncated]" + } + }, + { + "name": "social_media_behavior", + "distribution": { + "type": "categorical", + "options": [ + "Non-user", + "Passive consumer", + "Occasional poster", + "Active poster/Influencer" + ], + "weights": [ + 0.16, + 0.42, + 0.28, + 0.14 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1.0" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Pew Research Social Media Use 2024; Reuters Digital News Report 2024", + "note": "~84% of US adults use social media in some form. Most are passive consumers. Active posters ~28-30% of users; influencers a small fraction. Non-users concentrated among older adults 65+." + } + }, + { + "name": "tech_adoption_posture", + "distribution": { + "type": "categorical", + "options": [ + "Technophobe/Avoider", + "Late majority adopter", + "Early majority adopter", + "Early adopter", + "Innovator" + ], + "weights": [ + 0.14, + 0.26, + 0.34, + 0.18, + 0.08 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1.0" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Rogers Diffusion of Innovation model applied to US population; Pew Research Tech Adoption 2024", + "note": "Loosely follows Rogers' diffusion curve: innovators ~2.5%, early adopters ~13.5%, early majority ~34%, late majority ~34%, laggards ~16%. Adjusted for US digital economy context and 18-80 age range." + } + }, + { + "name": "religious_engagement", + "distribution": { + "type": "categorical", + "options": [ + "Secular/None", + "Nominally affiliated", + "Moderately religious", + "Devout" + ], + "weights": [ + 0.29, + 0.25, + 0.27, + 0.19 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1.0" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Pew Research Religious Landscape Study; Gallup Religion 2024", + "note": "~29% identify as religiously unaffiliated (nones). ~25% nominally affiliated but rarely practice. ~27% moderately religious (attend occasionally). ~19% devout (weekly+ attendance). Southeast and Midwe...[truncated]" + } + }, + { + "name": "political_identity", + "distribution": { + "type": "categorical", + "options": [ + "Strong Democrat", + "Lean Democrat", + "True Independent", + "Lean Republican", + "Strong Republican" + ], + "weights": [ + 0.18, + 0.18, + 0.17, + 0.2, + 0.27 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1.0" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Gallup Party Affiliation 2024; Pew Research Partisan Identification 2024-2025", + "note": "2024 Gallup: 46% R/lean-R, 45% D/lean-D, 43% pure independents. Strong partisans ~28% each side per Pew. True independents (no lean) ~17%. Slight Republican lean for base reflecting 2024 election envi...[truncated]" + } + }, + { + "name": "political_engagement_level", + "distribution": { + "type": "categorical", + "options": [ + "Disengaged", + "Single-issue voter", + "Moderate/occasional participant", + "Highly engaged partisan" + ], + "weights": [ + 0.22, + 0.18, + 0.38, + 0.22 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1.0" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "ANES 2024; Pew Research Political Engagement 2024", + "note": "About 22% are largely disengaged from politics, 18% single-issue focused. The moderate/occasional majority ~38%. Highly engaged ~22%, consistent with share who always vote in midterms and follow news ...[truncated]" + } + }, + { + "name": "vote_2024_presidential", + "distribution": { + "type": "categorical", + "options": [ + "Trump", + "Harris", + "Third party", + "Did not vote", + "Not eligible" + ], + "weights": [ + 0.31, + 0.3, + 0.02, + 0.28, + 0.09 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1.0" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "2024 Presidential Election results; US Elections Project turnout data", + "note": "Trump won 49.9% of popular vote, Harris 48.4% of votes cast. ~64% of eligible voters voted. ~9% of adults are non-citizens/not eligible. Did-not-vote reflects low turnout among disengaged. Political i...[truncated]" + } + }, + { + "name": "midterm_turnout_propensity", + "distribution": { + "type": "categorical", + "options": [ + "Always votes", + "Sometimes votes", + "Rarely votes", + "Never votes" + ], + "weights": [ + 0.35, + 0.27, + 0.2, + 0.18 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1.0" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "US Elections Project midterm turnout data; Pew Research voter typology 2024", + "note": "Midterm turnout ~50% of eligible voters (2022 ~46.8%). Always-voters ~35% of adults, sometimes ~27%, rarely ~20%, never ~18%. Age modifier critical: older adults much more consistent voters." + } + }, + { + "name": "congressional_district_competitiveness", + "distribution": { + "type": "categorical", + "options": [ + "Safe Republican", + "Lean Republican", + "Swing district", + "Lean Democrat", + "Safe Democrat" + ], + "weights": [ + 0.36, + 0.12, + 0.1, + 0.12, + 0.3 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1.0" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Cook Political Report 2024 district ratings; DCCC/NRCC targeting lists", + "note": "Approximately 35-40 truly competitive House districts out of 435 (~10%). Safe R districts slightly outnumber safe D given geographic sorting. Rural/suburban Republican districts vs. urban Democratic s...[truncated]" + } + }, + { + "name": "top_issue_2026", + "distribution": { + "type": "categorical", + "options": [ + "Economy/Jobs", + "Abortion/Reproductive rights", + "Immigration", + "Democracy/Rule of law", + "Healthcare", + "Climate", + "Education", + "AI/Technology" + ], + "weights": [ + 0.28, + 0.13, + 0.15, + 0.12, + 0.13, + 0.07, + 0.07, + 0.05 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1.0" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Gallup Most Important Problem 2024-2025; Pew Research Issue Priorities 2024", + "note": "Economy/jobs consistently tops issue salience polls ~25-30%. Immigration elevated post-2024 election. Abortion remains salient post-Dobbs. Democracy/rule of law elevated heading into 2026. AI/Tech eme...[truncated]" + } + }, + { + "name": "country_direction_approval", + "distribution": { + "type": "categorical", + "options": [ + "Strongly approve", + "Somewhat approve", + "Somewhat disapprove", + "Strongly disapprove" + ], + "weights": [ + 0.22, + 0.18, + 0.18, + 0.42 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1.0" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Gallup Right/Wrong Direction poll 2025; RealClearPolitics average Feb 2026", + "note": "As of early 2026, right-track/wrong-track polls show majority disapprove of country direction (~55-60% wrong track). Strongly disapprove heavily driven by Democrats/independents reacting to Trump seco...[truncated]" + } + }, + { + "name": "consumer_identity", + "distribution": { + "type": "categorical", + "options": [ + "Brand loyal", + "Price/value driven", + "Values/ethics driven", + "Convenience driven" + ], + "weights": [ + 0.22, + 0.42, + 0.18, + 0.18 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1.0" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Nielsen Consumer Research 2024; McKinsey Consumer Pulse 2024", + "note": "Price/value is primary driver for plurality of Americans especially post-inflation environment. Brand loyalty declining. Values-driven purchasing elevated among higher-education, younger, liberal cons...[truncated]" + } + }, + { + "name": "institutional_trust", + "distribution": { + "type": "categorical", + "options": [ + "High trust", + "Moderate trust", + "Low trust", + "Distrustful/Conspiratorial" + ], + "weights": [ + 0.14, + 0.32, + 0.34, + 0.2 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Weights must sum to 1.0" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Gallup Confidence in Institutions 2024; Edelman Trust Barometer 2025", + "note": "Institutional trust at historic lows. Gallup 2024: <30% confident in most major institutions. ~14% high trust, ~32% moderate, ~34% low trust, ~20% conspiratorial/distrustful across the board. 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"multiply": null, + "add": null, + "weight_overrides": { + "High trust": 0.06, + "Moderate trust": 0.22, + "Low trust": 0.36, + "Distrustful/Conspiratorial": 0.36 + }, + "probability_override": null + }, + { + "when": "political_identity == 'Strong Republican' and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "multiply": null, + "add": null, + "weight_overrides": { + "High trust": 0.1, + "Moderate trust": 0.28, + "Low trust": 0.38, + "Distrustful/Conspiratorial": 0.24 + }, + "probability_override": null + }, + { + "when": "political_identity == 'True Independent' and education_level in ['Less than HS', 'HS diploma/GED']", + "multiply": null, + "add": null, + "weight_overrides": { + "High trust": 0.06, + "Moderate trust": 0.26, + "Low trust": 0.38, + "Distrustful/Conspiratorial": 0.3 + }, + "probability_override": null + }, + { + "when": "political_identity == 'True Independent' and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "multiply": null, + "add": null, + "weight_overrides": { + "High trust": 0.1, + "Moderate trust": 0.38, + "Low trust": 0.36, + "Distrustful/Conspiratorial": 0.16 + }, + "probability_override": null + }, + { + "when": "political_identity in ['Lean Democrat'] and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "multiply": null, + "add": null, + "weight_overrides": { + "High trust": 0.22, + "Moderate trust": 0.46, + "Low trust": 0.24, + "Distrustful/Conspiratorial": 0.08 + }, + "probability_override": null + }, + { + "when": "political_identity in ['Lean Republican'] and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "multiply": null, + "add": null, + "weight_overrides": { + "High trust": 0.14, + "Moderate trust": 0.34, + "Low trust": 0.36, + "Distrustful/Conspiratorial": 0.16 + }, + "probability_override": null + } + ] + }, + { + "name": "risk_tolerance", + "modifiers": [ + { + "when": "age < 35 and household_income >= 80000", + "multiply": 1.2, + "add": 0.06, + "weight_overrides": null, + "probability_override": null + }, + { + "when": "age < 35 and household_income < 40000", + "multiply": 0.95, + "add": 0.02, + "weight_overrides": null, + "probability_override": null + }, + { + "when": "age >= 55 and household_income >= 100000", + "multiply": 0.9, + "add": 0.0, + "weight_overrides": null, + "probability_override": null + }, + { + "when": "age >= 55 and household_income < 50000", + "multiply": 0.8, + "add": -0.06, + "weight_overrides": null, + "probability_override": null + }, + { + "when": "age >= 35 and age < 55 and household_income >= 100000", + "multiply": 1.05, + "add": 0.02, + "weight_overrides": null, + "probability_override": null + } + ] + }, + { + "name": "economic_anxiety", + "modifiers": [ + { + "when": "financial_margin == 'In debt/deficit' and job_security_perception in ['Very insecure', 'Somewhat insecure']", + "multiply": 1.0, + "add": 0.22, + "weight_overrides": null, + "probability_override": null + }, + { + "when": 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"multiply": 1.0, + "add": -0.16, + "weight_overrides": null, + "probability_override": null + }, + { + "when": "financial_margin == 'Comfortable savings buffer' and job_security_perception == 'N/A - not employed'", + "multiply": 1.0, + "add": -0.08, + "weight_overrides": null, + "probability_override": null + } + ] + } + ] + }, + "name": "conditional_modifiers_hydration", + "type": "tool_use", + "caller": { + "type": "direct" + } + } + ], + "model": "claude-sonnet-4-6", + "role": "assistant", + "stop_reason": "tool_use", + "stop_sequence": null, + "type": "message", + "usage": { + "cache_creation": { + "ephemeral_1h_input_tokens": "[REDACTED_SECRET]", + "ephemeral_5m_input_tokens": "[REDACTED_SECRET]" + }, + "cache_creation_input_tokens": "[REDACTED_SECRET]", + "cache_read_input_tokens": "[REDACTED_SECRET]", + "input_tokens": 60228, + "output_tokens": 19985, + "server_tool_use": { + "web_search_requests": 3, + "web_fetch_requests": 0 + }, + "service_tier": "standard", + "inference_geo": "global" + } + }, + "sources_extracted": [ + "https://statisticalatlas.com/United-States/Marital-Status", + "https://educationdata.org/education-attainment-statistics", + "https://www.census.gov/newsroom/press-releases/2025/educational-attainment-data.html", + "https://www.census.gov/topics/families/marriage-and-divorce.html", + "https://www.bgsu.edu/ncfmr/resources/data/family-profiles/marital-status-distribution-of-u-s--adults-aged-65-and-older--19.html", + "https://nces.ed.gov/pubs95/web/95823.asp", + "https://www.census.gov/library/publications/2025/demo/p60-286.html", + "https://www.equityinhighered.org/indicators/u-s-population-trends-and-educational-attainment/educational-attainment-by-age/", + "https://www.statista.com/statistics/785618/educational-attainment-by-age-group-us/", + "https://www.ers.usda.gov/topics/rural-economy-population/employment-education/rural-education", + "https://www2.census.gov/library/publications/2025/demo/p60-286.pdf", + "https://dhsprogram.com/data/Guide-to-DHS-Statistics/Current_Marital_Status.htm", + "https://en.wikipedia.org/wiki/Household_income_in_the_United_States", + "https://nces.ed.gov/fastfacts/display.asp?id=27", + "https://www.census.gov/data/tables/time-series/demo/families/marital.html", + "https://www.psychologytoday.com/us/blog/living-single/202406/the-record-setting-rise-of-single-living-in-the-us", + "https://www.census.gov/topics/education/educational-attainment/data/tables.html", + "https://en.wikipedia.org/wiki/Educational_attainment_in_the_United_States", + "https://www.bls.gov/news.release/famee.t05.htm", + "https://www.census.gov/library/visualizations/interactive/marital-status-in-united-states.html", + "https://www2.census.gov/library/publications/2024/demo/p60-282.pdf", + "https://www.bls.gov/opub/mlr/2024/article/patterns-of-marriage-and-divorce-from-ages-15-to-55-evidence-from-the-nlsy79.htm", + "https://www.statista.com/statistics/233301/median-household-income-in-the-united-states-by-education/", + "https://www.census.gov/library/stories/2025/09/education-and-income.html", + "https://nces.ed.gov/programs/digest/2024menu_tables.asp", + "https://nces.ed.gov/programs/coe/indicator/cba/annual-earnings", + "https://www.bgsu.edu/ncfmr/resources/data/family-profiles/FP-24-10.html", + "https://www.ccdaily.com/2024/01/more-adults-earn-post-high-school-credentials/", + "https://en.wikipedia.org/wiki/Literacy_in_the_United_States", + "https://www.ers.usda.gov/data-products/county-level-data-sets" + ] +} \ No newline at end of file diff --git a/examples/logs/20260217_202952_563342_claude_reasoning_call.json b/examples/study-01/logs/20260217_202952_563342_claude_reasoning_call.json similarity index 100% rename from examples/logs/20260217_202952_563342_claude_reasoning_call.json rename to examples/study-01/logs/20260217_202952_563342_claude_reasoning_call.json diff --git 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from examples/logs/20260217_220453_664342_claude_reasoning_call.json rename to examples/study-01/logs/20260217_220453_664342_claude_reasoning_call.json diff --git a/examples/logs/20260217_220505_645217_claude_simple_call.json b/examples/study-01/logs/20260217_220505_645217_claude_simple_call.json similarity index 100% rename from examples/logs/20260217_220505_645217_claude_simple_call.json rename to examples/study-01/logs/20260217_220505_645217_claude_simple_call.json diff --git a/examples/logs/20260217_220534_635130_claude_reasoning_call.json b/examples/study-01/logs/20260217_220534_635130_claude_reasoning_call.json similarity index 100% rename from examples/logs/20260217_220534_635130_claude_reasoning_call.json rename to examples/study-01/logs/20260217_220534_635130_claude_reasoning_call.json diff --git a/examples/logs/20260217_220549_823857_claude_simple_call.json b/examples/study-01/logs/20260217_220549_823857_claude_simple_call.json similarity index 100% rename from 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index 100% rename from examples/logs/20260218_001702_906764_claude_reasoning_call.json rename to examples/study-01/logs/20260218_001702_906764_claude_reasoning_call.json diff --git a/examples/logs/20260218_001832_767614_claude_reasoning_call.json b/examples/study-01/logs/20260218_001832_767614_claude_reasoning_call.json similarity index 100% rename from examples/logs/20260218_001832_767614_claude_reasoning_call.json rename to examples/study-01/logs/20260218_001832_767614_claude_reasoning_call.json diff --git a/examples/logs/20260218_001921_881763_claude_reasoning_call.json b/examples/study-01/logs/20260218_001921_881763_claude_reasoning_call.json similarity index 100% rename from examples/logs/20260218_001921_881763_claude_reasoning_call.json rename to examples/study-01/logs/20260218_001921_881763_claude_reasoning_call.json diff --git a/examples/population.v1.yaml b/examples/study-01/population.v1.yaml similarity index 100% rename from examples/population.v1.yaml rename to examples/study-01/population.v1.yaml diff --git a/examples/scenario/asi-announcement/network-config.seed42.20260218-001921.yaml b/examples/study-01/scenario/asi-announcement/network-config.seed42.20260218-001921.yaml similarity index 100% rename from examples/scenario/asi-announcement/network-config.seed42.20260218-001921.yaml rename to examples/study-01/scenario/asi-announcement/network-config.seed42.20260218-001921.yaml diff --git a/examples/scenario/asi-announcement/persona.v1.yaml b/examples/study-01/scenario/asi-announcement/persona.v1.yaml similarity index 100% rename from examples/scenario/asi-announcement/persona.v1.yaml rename to examples/study-01/scenario/asi-announcement/persona.v1.yaml diff --git a/examples/scenario/asi-announcement/scenario.v1.yaml b/examples/study-01/scenario/asi-announcement/scenario.v1.yaml similarity index 100% rename from examples/scenario/asi-announcement/scenario.v1.yaml rename to examples/study-01/scenario/asi-announcement/scenario.v1.yaml diff --git a/examples/study.db b/examples/study-01/study.db similarity index 100% rename from examples/study.db rename to examples/study-01/study.db diff --git a/examples/study-02/logs/20260220_021037_327440_claude_simple_call.json b/examples/study-02/logs/20260220_021037_327440_claude_simple_call.json new file mode 100644 index 0000000..751bcc4 --- /dev/null +++ b/examples/study-02/logs/20260220_021037_327440_claude_simple_call.json @@ -0,0 +1,50 @@ +{ + "timestamp": "2026-02-20T02:10:37.327466", + "function": "simple_call", + "provider": "claude", + "request": { + "model": "claude-sonnet-4-6", + "prompt_length": 7264 + }, + "response": { + "id": "msg_012JECdnU1iEDoUuTUpG5Qux", + "content": [ + { + "id": "toolu_01Hgii1HPZF98CJa2ePoVVA7", + "input": { + "sufficient": true, + "inferred_duration": "6 months", + "inferred_timestep_unit": "month", + "inferred_scenario_type": "evolving", + "has_explicit_outcomes": false, + "inferred_agent_focus_mode": "primary_only", + "questions": [] + }, + "name": "scenario_sufficiency_check", + "type": "tool_use", + "caller": { + "type": "direct" + } + } + ], + "model": "claude-sonnet-4-6", + "role": "assistant", + "stop_reason": "tool_use", + "stop_sequence": null, + "type": "message", + "usage": { + "cache_creation": { + "ephemeral_1h_input_tokens": "[REDACTED_SECRET]", + "ephemeral_5m_input_tokens": "[REDACTED_SECRET]" + }, + "cache_creation_input_tokens": "[REDACTED_SECRET]", + "cache_read_input_tokens": "[REDACTED_SECRET]", + "input_tokens": 2851, + "output_tokens": 165, + "server_tool_use": null, + "service_tier": "standard", + "inference_geo": "global" + } + }, + "sources_extracted": [] +} \ No newline at end of file diff --git a/examples/study-02/logs/20260220_021102_188183_claude_reasoning_call.json b/examples/study-02/logs/20260220_021102_188183_claude_reasoning_call.json new file mode 100644 index 0000000..83a7a1b --- /dev/null +++ b/examples/study-02/logs/20260220_021102_188183_claude_reasoning_call.json @@ -0,0 +1,199 @@ +{ + "timestamp": "2026-02-20T02:11:02.188206", + "function": "reasoning_call", + "provider": "claude", + "request": { + "model": "claude-sonnet-4-6", + "prompt_length": 19680 + }, + "response": { + "id": "msg_01C5JcaeMhF8CMULKEH9SU4a", + "content": [ + { + "id": "toolu_015E7LL8tKUVmxMtXwgS7NVh", + "input": { + "attributes": [ + { + "name": "asi_threat_perception", + "type": "categorical", + "category": "population_specific", + "description": "How the individual perceives ASI primarily: as an existential threat, economic threat, neutral development, transformative opportunity, or utopian salvation.", + "strategy": "conditional", + "scope": "individual", + "depends_on": [ + "institutional_trust", + "tech_adoption_posture", + "political_identity" + ] + }, + { + "name": "asi_usage_propensity", + "type": "categorical", + "category": "population_specific", + "description": "Likelihood and eagerness to personally adopt and use ASI tools when publicly available (Early adopter, Willing user, Cautious user, Reluctant user, Refuser).", + "strategy": "conditional", + "scope": "individual", + "depends_on": [ + "tech_adoption_posture", + "openness", + "asi_threat_perception" + ] + }, + { + "name": "job_displacement_vulnerability", + "type": "categorical", + "category": "population_specific", + "description": "Perceived or objective degree to which the individual's occupation is rendered redundant by ASI-level cognitive automation (Low, Moderate, High, Already redundant).", + "strategy": "conditional", + "scope": "individual", + "depends_on": [ + "employment_sector", + "education_level", + "economic_anxiety" + ] + }, + { + "name": "personal_medical_stake", + "type": "categorical", + "category": "population_specific", + "description": "Whether the individual or an immediate household member has a condition that ASI-developed cures directly address (e.g., cancer, Alzheimer's, diabetes, ALS, HIV, None).", + "strategy": "independent", + "scope": "household", + "depends_on": [] + }, + { + "name": "asi_governance_preference", + "type": "categorical", + "category": "population_specific", + "description": "Preferred approach to governing ASI development and deployment (Unregulated free market, Industry self-regulation, National government oversight, International treaty body, Halt/moratorium).", + "strategy": "conditional", + "scope": "individual", + "depends_on": [ + "political_identity", + "institutional_trust", + "asi_threat_perception" + ] + }, + { + "name": "geopolitical_anxiety_asi", + "type": "float", + "category": "population_specific", + "description": "Level of concern about China's parallel ASI development and resulting geopolitical power shift (0=unconcerned, 1=extremely anxious), shaping hawkish vs. cooperative policy preferences.", + "strategy": "conditional", + "scope": "individual", + "depends_on": [ + "political_identity", + "political_engagement_level", + "asi_threat_perception" + ] + }, + { + "name": "abundance_distribution_priority", + "type": 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Modifiers from tech_adoption_posture and asi_threat_perceptio...[truncated]" + } + }, + { + "name": "job_displacement_vulnerability", + "distribution": { + "type": "categorical", + "options": [ + "Low", + "Moderate", + "High", + "Already redundant" + ], + "weights": [ + 0.28, + 0.35, + 0.27, + 0.1 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Pew 2024 (64% of public thinks AI will lead to fewer jobs over next 20 years); Brookings 2025 analysis (3.9% national average of workers in high-exposure, low-adaptive-capacity occupations); Goldman S...[truncated]", + "note": "In an ASI scenario, 'Already redundant' is elevated vs. current AI exposure data. For ASI (not just LLMs), virtually all cognitive jobs face some disruption. 'Low' here refers to physical/hands-on tra...[truncated]" + } + }, + { + "name": "asi_governance_preference", + "distribution": { + "type": "categorical", + "options": [ + "Unregulated free market", + "Industry self-regulation", + "National government oversight", + "International treaty body", + "Halt/moratorium" + ], + "weights": [ + 0.07, + 0.14, + 0.38, + 0.27, + 0.14 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "MITRE/Harris 2022 (82% think AI should be regulated); Rethink Priorities (65% support international AI governance); KPMG 2025 (72% of US consumers say more regulation needed; only 29% think current re...[truncated]", + "note": "National government oversight is the plurality preference, but international treaty body is also strong. Distrust in government capacity creates tension: people want regulation but distrust regulators...[truncated]" + } + }, + { + "name": "geopolitical_anxiety_asi", + "distribution": { + "type": "beta", + "alpha": 3.2, + "beta": 2.1, + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": 0.0, + "max": 1.0, + "min_formula": null, + "max_formula": null, + "options": null, + "weights": null, + "probability_true": null + }, + "constraints": [ + { + "type": "hard_min", + "value": 0.0, + "expression": null, + "reason": "Anxiety score is bounded at 0" + }, + { + "type": "hard_max", + "value": 1.0, + "expression": null, + "reason": "Anxiety score is bounded at 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "RAND 2025 survey: 72% of Americans rated concern about China becoming world AI leader at 6 or higher out of 10; 38% rated it a 10/10. RAND also found concern remains relatively high across all familia...[truncated]", + "note": "Beta(3.2, 2.1) gives mean ~0.60 with moderate variance. This reflects a population that is broadly anxious about Chinese AI competition but with a meaningful minority who are less concerned or prefer ...[truncated]" + } + }, + { + "name": "abundance_distribution_priority", + "distribution": { + "type": "categorical", + "options": [ + "Global equal access", + "Domestic priority", + "Market-determined", + "Strategic allies", + "Local community first" + ], + "weights": [ + 0.16, + 0.38, + 0.2, + 0.14, + 0.12 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Extrapolated from: RAND 2025 (72% prioritize US AI leadership over China); general polling on America First sentiment; Pew international aid attitudes (Americans tend domestic-first on resource distri...[truncated]", + "note": "Domestic priority is the plurality given strong America-first sentiment in polling data. Market-determined reflects libertarian/free-market preference. Global equal access reflects cosmopolitan minori...[truncated]" + } + }, + { + "name": "information_verification_behavior", + "distribution": { + "type": "categorical", + "options": [ + "Accepts at face value", + "Waits for consensus", + "Actively verifies", + "Dismisses as hype/misinformation", + "Seeks ASI to verify" + ], + "weights": [ + 0.18, + 0.31, + 0.22, + 0.2, + 0.09 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Extrapolated from: Pew 2024 institutional_trust data; general media literacy surveys showing ~20-25% of Americans fact-check claims; Pew (66% of US adults concerned about AI-generated misinformation)....[truncated]", + "note": "'Seeks ASI to verify' is a novel behavior only possible post-ASI public access (Month 3); base rate estimated at ~9% of early adopters. Primary modifiers: institutional_trust (low trust \u2192 dismisses), ...[truncated]" + } + }, + { + "name": "identity_threat_from_asi", + "distribution": { + "type": "beta", + "alpha": 2.0, + "beta": 3.5, + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": 0.0, + "max": 1.0, + "min_formula": null, + "max_formula": null, + "options": null, + "weights": null, + "probability_true": null + }, + "constraints": [ + { + "type": "hard_min", + "value": 0.0, + "expression": null, + "reason": "Identity threat is bounded at 0 (no threat felt)" + }, + { + "type": "hard_max", + "value": 1.0, + "expression": null, + "reason": "Identity threat is bounded at 1 (profound identity crisis)" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Extrapolated from: Pew 2024 (56% of US adults extremely/very concerned about AI eliminating jobs); Pew data on AI and human abilities (53% say AI will worsen creative ability; 50% say it will worsen m...[truncated]", + "note": "Beta(2.0, 3.5) gives mean ~0.36, reflecting that most people feel some identity threat from ASI but not a profound crisis at baseline. The distribution is left-skewed: more people near 0.2-0.4 than ne...[truncated]" + } + }, + { + "name": "asi_corporate_trust", + "distribution": { + "type": "categorical", + "options": [ + "High trust", + "Moderate trust", + "Low trust", + "Deep distrust" + ], + "weights": [ + 0.09, + 0.28, + 0.37, + 0.26 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "KPMG 2025: only 41% of US adults willing to trust AI; 43% have low confidence in commercial entities to develop AI in public interest; most Americans trust universities (76%) and healthcare (72%) far ...[truncated]", + "note": "Low trust and Deep distrust together represent ~63% \u2014 consistent with KPMG finding that 43% have low confidence in commercial AI developers. 'High trust' (9%) reflects tech-enthusiast minority. Modifi...[truncated]" + } + }, + { + "name": "sci_tech_literacy", + "distribution": { + "type": "categorical", + "options": [ + "Basic", + "Intermediate", + "Advanced", + "Expert" + ], + "weights": [ + 0.38, + 0.38, + 0.18, + 0.06 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "NSF Science & Engineering Indicators 2024: 22% of Americans reported knowing 'nothing' about AI, 62% knowing 'some', 17% knowing 'a lot' (3M 2020 data). NSF also documents that most Americans support ...[truncated]", + "note": "Basic (38%) and Intermediate (38%) together represent ~76%, consistent with NSF and Pew data on public science literacy. 'Expert' (6%) aligns with STEM workforce share. Key modifiers: education_level ...[truncated]" + } + }, + { + "name": "community_coordination_role", + "distribution": { + "type": "categorical", + "options": [ + "Leader/organizer", + "Active participant", + "Follower", + "Withdrawn/observer", + "Obstructionist" + ], + "weights": [ + 0.1, + 0.22, + 0.3, + 0.3, + 0.08 + ], + "mean": null, + "std": null, + "mean_formula": null, + "std_formula": null, + "min": null, + "max": null, + "min_formula": null, + "max_formula": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "expression": "abs(sum(weights) - 1.0) < 0.001", + "value": null, + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Extrapolated from: political engagement data (Pew: ~35% of Americans are Highly Engaged Partisans or very politically active); community organizing literature (typically 10% leaders, 20% active partic...[truncated]", + "note": "Leader/organizer (10%) and Active participant (22%) reflect realistic social movement participation rates. Follower and Withdrawn/observer together represent the civic majority (~60%). Key modifiers: ...[truncated]" + } + } + ] + }, + "name": "conditional_base_hydration", + "type": "tool_use", + "caller": { + "type": "direct" + } + } + ], + "model": "claude-sonnet-4-6", + "role": "assistant", + "stop_reason": "tool_use", + "stop_sequence": null, + "type": "message", + "usage": { + "cache_creation": { + "ephemeral_1h_input_tokens": "[REDACTED_SECRET]", + "ephemeral_5m_input_tokens": "[REDACTED_SECRET]" + }, + "cache_creation_input_tokens": "[REDACTED_SECRET]", + "cache_read_input_tokens": "[REDACTED_SECRET]", + "input_tokens": 124562, + "output_tokens": 5328, + "server_tool_use": { + "web_search_requests": 5, + "web_fetch_requests": 0 + }, + "service_tier": "standard", + "inference_geo": "global" + } + }, + "sources_extracted": [ + "https://www.stlouisfed.org/on-the-economy/2025/nov/state-generative-ai-adoption-2025", + "https://www.rutgers.edu/news/survey-reveals-concerns-and-adoption-trends-around-ais-rising-influence", + "https://www.weforum.org/stories/2025/09/ai-trust-crisis-public-private-partnerships/", + "https://hai.stanford.edu/ai-index/2024-ai-index-report", + "https://www.frontiersin.org/journals/political-science/articles/10.3389/fpos.2025.1654697/full", + "https://www.pewresearch.org/science/2025/09/17/how-americans-view-ai-and-its-impact-on-people-and-society/", + "https://www.bls.gov/opub/mlr/2022/article/growth-trends-for-selected-occupations-considered-at-risk-from-automation.htm", + "https://www.pewresearch.org/internet/2025/04/03/views-of-risks-opportunities-and-regulation-of-ai/", + "https://www.nature.com/articles/s41598-024-79863-5", + "https://ventionteams.com/solutions/ai/adoption-statistics", + "https://www.cgi.org.uk/resources/blogs/2025/from-openai-to-anthropic-whos-leading-on-ai-governance/", + "https://futureofbeinghuman.com/api/v1/file/2737ea4a-2ca4-4cb5-85cb-4c9ad88f6204.pdf", + "https://rethinkpriorities.org/new-surveys-find-that-most-americans-support-ai-regulation-believing-that-ai-risk-mitigation-is-a-global-priority/", + "https://mitgovlab.org/news/can-trustworthy-ai-improve-governance-and-trust-ai-trends-biases-and-gaps/", + "https://www.shrm.org/topics-tools/research/automation-generative-ai-job-displacement-risk-hr-employment", + "https://www.bcg.com/press/24october2024-ai-adoption-in-2024-74-of-companies-struggle-to-achieve-and-scale-value", + "https://carnegieendowment.org/research/2024/08/china-artificial-intelligence-ai-safety-regulation?lang=en", + "https://www.pewresearch.org/internet/2025/04/03/how-the-us-public-and-ai-experts-view-artificial-intelligence/", + "https://www.atlanticcouncil.org/dispatches/eight-ways-ai-will-shape-geopolitics-in-2026/", + "https://www.brookings.edu/articles/how-are-americans-using-ai-evidence-from-a-nationwide-survey/", + "https://www.sentienceinstitute.org/blog/ai-policy-insights-from-the-aims-survey", + "https://kpmg.com/us/en/media/news/trust-in-ai-2025.html", + "https://www.brookings.edu/articles/measuring-us-workers-capacity-to-adapt-to-ai-driven-job-displacement/", + "https://theelders.org/news/existential-threats-global-public-attitudes", + "https://financesonline.com/jobs-lost-to-automation-statistics/", + "https://ncses.nsf.gov/pubs/nsb20244/assets/nsb20244.pdf", + "https://crfm.stanford.edu/fmti/paper.pdf", + "https://www.stlouisfed.org/on-the-economy/2024/sep/rapid-adoption-generative-ai", + "https://hai.stanford.edu/ai-index/2024-ai-index-report/public-opinion", + "https://assets.anthropic.com/m/4e20a4ab6512e217/original/Anthropic-Response-to-OSTP-RFI-March-2025-Final-Submission-v3.pdf", + "https://www.freshworks.com/explore-cx/us-ai-receptiveness-survey/", + "https://kpmg.com/xx/en/our-insights/ai-and-technology/trust-attitudes-and-use-of-ai.html", + "https://sciencespo.hal.science/hal-04932663v1/document", + "https://teamstage.io/jobs-lost-to-automation-statistics/", + "https://harvardlawreview.org/print/vol-138/amoral-drift-in-ai-corporate-governance/", + "https://wiki.aiimpacts.org/responses_to_ai/public_opinion_on_ai/surveys_of_public_opinion_on_ai/surveys_of_us_public_opinion_on_ai", + "https://www.aiprm.com/ai-replacing-jobs-statistics/", + "https://research.aimultiple.com/ai-job-loss/", + "https://rochesterbeacon.com/2025/12/24/with-ais-rise-hope-and-anxiety/", + "https://www.rand.org/pubs/research_reports/RRA4363-1.html", + "https://www.nu.edu/blog/ai-job-statistics/", + "https://jamestown.org/survey-how-do-elite-chinese-students-feel-about-the-risks-of-ai/", + "https://workonpeak.org/ai-and-automation-the-threat-of-job-displacement/", + "https://edutimes.com/news/2025/08/202508113292", + "https://www.nber.org/system/files/working_papers/w32966/w32966.pdf", + "https://www.pwc.com/us/en/tech-effect/ai-analytics/responsible-ai-survey.html", + "https://www.mdpi.com/2673-8104/4/4/27", + 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Population", + "type": "web_search_result", + "url": "https://www.census.gov/library/publications/2011/compendia/statab/131ed/population.html" + }, + { + "encrypted_content": "[REDACTED_TEXT length=2880]", + "page_age": "February 2, 2018", + "title": "The personality of U.S. states: Stability from 1999 to 2015 - ScienceDirect", + "type": "web_search_result", + "url": "https://www.sciencedirect.com/science/article/pii/S009265661630071X" + }, + { + "encrypted_content": "[REDACTED_TEXT length=1352]", + "page_age": null, + "title": "U.S. Census Bureau QuickFacts: United States", + "type": "web_search_result", + "url": "https://www.census.gov/quickfacts/fact/table/US/PST040224" + }, + { + "encrypted_content": "[REDACTED_TEXT length=3636]", + "page_age": null, + "title": "National Population by Characteristics: 2020-2024", + "type": "web_search_result", + "url": "https://www.census.gov/data/datasets/time-series/demo/popest/2020s-national-detail.html" + }, + { + "encrypted_content": "[REDACTED_TEXT length=5788]", + "page_age": null, + "title": "Urban. 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Truncated normal applied within 18-80." + } + }, + { + "name": "gender", + "distribution": { + "type": "categorical", + "options": [ + "Male", + "Female", + "Non-binary/Other" + ], + "weights": [ + 0.49, + "0.495", + 0.015 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Wikipedia Demographics of the United States; 2020 Census: females 50.9%, males 49.1%; Gallup 2022 transgender/nonbinary ~1.6%", + "note": "2020 Census reported females at 50.9%, males at 49.1% of total population. Non-binary/Other estimated at ~1.5% of adults based on Gallup surveys, slightly reducing male/female shares." + } + }, + { + "name": "race_ethnicity", + "distribution": { + "type": "categorical", + "options": [ + "White non-Hispanic", + "Black", + "Hispanic/Latino", + "Asian", + "Other/Multiracial" + ], + "weights": [ + 0.575, + 0.124, + 0.185, + 0.062, + 0.054 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "neilsberg.com citing ACS 2019-2023; visualcapitalist.com 2023 racial breakdown; Census QuickFacts 2024", + "note": "Non-Hispanic white ~57.5% (adults skew slightly more white than total population); Black ~12.4%; Hispanic/Latino ~18.5% (adults slightly lower than total pop); Asian ~6.2%; Other/Multiracial ~5.4%. We...[truncated]" + } + }, + { + "name": "us_region", + "distribution": { + "type": "categorical", + "options": [ + "Northeast", + "Southeast", + "Midwest", + "Southwest", + "West Coast" + ], + "weights": [ + 0.17, + 0.25, + 0.21, + 0.15, + 0.22 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Census Bureau regional population estimates; USAFacts population 2024", + "note": "Census defines 4 regions; 5-region model used here (Southeast separated from South, Southwest carved out). Northeast ~17%, Southeast ~25% (South minus SW), Midwest ~21%, Southwest ~15%, West Coast ~22...[truncated]" + } + }, + { + "name": "urban_rural", + "distribution": { + "type": "categorical", + "options": [ + "Urban", + "Suburban", + "Rural" + ], + "weights": [ + 0.27, + 0.52, + 0.21 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "HUD/Census 2017 American Housing Survey: 52% suburban, 27% urban, 21% rural (huduser.gov)", + "note": "2017 AHS self-identification data: 52% suburban, 27% urban, 21% rural. These self-reported shares are stable and widely cited. Used directly." + } + }, + { + "name": "openness", + "distribution": { + "type": "normal", + "mean": 0.6, + "std": 0.18, + "min": 0.0, + "max": 1.0, + "alpha": null, + "beta": null, + "options": null, + "weights": null, + "probability_true": null + }, + "constraints": [ + { + "type": "hard_min", + "value": 0.0, + "expression": null, + "reason": "Trait score floored at 0" + }, + { + "type": "hard_max", + "value": 1.0, + "expression": null, + "reason": "Trait score capped at 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Big Five personality research; Schmitt et al. 2007 cross-cultural BFI; Rentfrow US state personality studies", + "note": "US adults score moderately high on Openness. BFI scores normalized to 0-1 scale. Mean ~0.60 reflects US sample means slightly above midpoint. Std 0.18 reflects substantial individual variation documen...[truncated]" + } + }, + { + "name": "conscientiousness", + "distribution": { + "type": "normal", + "mean": 0.65, + "std": 0.17, + "min": 0.0, + "max": 1.0, + "alpha": null, + "beta": null, + "options": null, + "weights": null, + "probability_true": null + }, + "constraints": [ + { + "type": "hard_min", + "value": 0.0, + "expression": null, + "reason": "Trait score floored at 0" + }, + { + "type": "hard_max", + "value": 1.0, + "expression": null, + "reason": "Trait score capped at 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Rentfrow et al. US personality research; ScienceDirect personality of US states 2016", + "note": "Conscientiousness is consistently the highest-scoring Big Five trait in US adult samples. Mean ~0.65 reflects above-midpoint normative scores. Std 0.17 reflects typical BFI individual variation." + } + }, + { + "name": "extraversion", + "distribution": { + "type": "normal", + "mean": 0.55, + "std": 0.19, + "min": 0.0, + "max": 1.0, + "alpha": null, + "beta": null, + "options": null, + "weights": null, + "probability_true": null + }, + "constraints": [ + { + "type": "hard_min", + "value": 0.0, + "expression": null, + "reason": "Trait score floored at 0" + }, + { + "type": "hard_max", + "value": 1.0, + "expression": null, + "reason": "Trait score capped at 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Schmitt et al. 2007 BFI cross-national study; Rentfrow US personality geography studies", + "note": "US adults score near midpoint on Extraversion with slightly above-average tendency. Mean 0.55, wider std 0.19 capturing introvert-to-extravert range." + } + }, + { + "name": "agreeableness", + "distribution": { + "type": "normal", + "mean": 0.62, + "std": 0.17, + "min": 0.0, + "max": 1.0, + "alpha": null, + "beta": null, + "options": null, + "weights": null, + "probability_true": null + }, + "constraints": [ + { + "type": "hard_min", + "value": 0.0, + "expression": null, + "reason": "Trait score floored at 0" + }, + { + "type": "hard_max", + "value": 1.0, + "expression": null, + "reason": "Trait score capped at 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Schmitt et al. 2007; Rentfrow et al. US state Big Five studies", + "note": "Agreeableness moderately high in US normative samples. Mean 0.62 reflects positive but not extreme social cooperation tendency." + } + }, + { + "name": "neuroticism", + "distribution": { + "type": "normal", + "mean": 0.45, + "std": 0.19, + "min": 0.0, + "max": 1.0, + "alpha": null, + "beta": null, + "options": null, + "weights": null, + "probability_true": null + }, + "constraints": [ + { + "type": "hard_min", + "value": 0.0, + "expression": null, + "reason": "Trait score floored at 0" + }, + { + "type": "hard_max", + "value": 1.0, + "expression": null, + "reason": "Trait score capped at 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Schmitt et al. 2007; Big Five normative data for US adult samples", + "note": "Neuroticism near midpoint in US adult samples; women score higher on average. Mean 0.45 slightly below midpoint with wide spread (std 0.19)." + } + }, + { + "name": "education_level", + "distribution": { + "type": "categorical", + "options": [ + "Less than HS", + "HS diploma/GED", + "Some college", + "Bachelor's degree", + "Graduate degree" + ], + "weights": [ + 0.09, + 0.27, + 0.27, + 0.22, + 0.15 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Census Bureau ACS 2023; Pew Research 2020 electorate education report; highereddatastories.com 2025", + "note": "Pew notes 36% of registered voters have 4-year degree+; Census ACS 2022-2023 adult education: ~9% less than HS, ~27% HS/GED, ~27% some college, ~22% bachelor's, ~15% graduate. Consistent with multiple...[truncated]" + } + }, + { + "name": "household_income", + "distribution": { + "type": "lognormal", + "mean": 11.1, + "std": 0.75, + "min": 5000, + "max": 500000, + "alpha": null, + "beta": null, + "options": null, + "weights": null, + "probability_true": null + }, + "constraints": [ + { + "type": "hard_min", + "value": 5000, + "expression": null, + "reason": "Minimum plausible household income for non-institutionalized adults" + }, + { + "type": "hard_max", + "value": 500000, + "expression": null, + "reason": "99th percentile upper bound; extreme wealth outliers excluded from representative sample" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Census Bureau CPS ASEC 2024; US median household income ~$80,610 (2023); lognormal parameters: mean of log ~11.1 yields median ~$66k, mean ~$80k", + "note": "US median household income was ~$74,580 in 2022, ~$80,610 in 2023 (Census). Lognormal is standard for income distributions. Log-mean 11.1 \u2248 median $66k, log-std 0.75 produces realistic right skew with...[truncated]" + } + }, + { + "name": "marital_status", + "distribution": { + "type": "categorical", + "options": [ + "Married/partnered", + "Single", + "Divorced/separated", + "Widowed" + ], + "weights": [ + 0.5, + 0.3, + 0.14, + 0.06 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Census Bureau CPS 2023; ACS 2022-2023 marital status for adults 18+", + "note": "Census 2023: ~49-50% married/partnered, ~31% never married (single), ~13-14% divorced/separated, ~6% widowed among adults 18+. These are well-established stable figures." + } + }, + { + "name": "household_size", + "distribution": { + "type": "normal", + "mean": 2.53, + "std": 1.4, + "min": 1, + "max": 10, + "alpha": null, + "beta": null, + "options": null, + "weights": null, + "probability_true": null + }, + "constraints": [ + { + "type": "hard_min", + "value": 1, + "expression": null, + "reason": "Minimum household size is 1 person" + }, + { + "type": "hard_max", + "value": 10, + "expression": null, + "reason": "Households larger than 10 are extremely rare in the US" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Census Bureau CPS 2023; average US household size 2.53 persons (2022-2023 ACS)", + "note": "US average household size 2.53 in 2022 (Census). Distribution is right-skewed but approximated with truncated normal. Std 1.4 reflects wide range from 1-person to large family households." + } + }, + { + "name": "has_children", + "distribution": { + "type": "boolean", + "probability_true": 0.3, + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "options": null, + "weights": null + }, + "constraints": [], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Census Bureau CPS 2023; ~29-32% of US households have children under 18", + "note": "Census data consistently shows ~29-32% of households include own children under 18. Probability_true set to 0.30 reflecting households with at least one child under 18 currently living at home." + } + }, + { + "name": "employment_sector", + "distribution": { + "type": "categorical", + "options": [ + "Federal government", + "State/local government", + "Private sector", + "Gig/contract", + "Self-employed", + "Retired", + "Unemployed", + "Not in labor force" + ], + "weights": [ + 0.02, + 0.07, + 0.47, + 0.07, + 0.07, + 0.17, + 0.04, + 0.09 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "BLS Current Population Survey 2024; Census Bureau ACS employment class 2023", + "note": "Federal government ~2.9M workers (~2% of adult pop); state/local ~20M (~7%); private sector ~130M (~47%); gig/contract ~7%; self-employed ~10M (~7%); retired ~17%; unemployed ~4%; NILF ~9%. Weights su...[truncated]" + } + }, + { + "name": "job_security_perception", + "distribution": { + "type": "categorical", + "options": [ + "Very secure", + "Somewhat secure", + "Somewhat insecure", + "Very insecure", + "N/A - not employed" + ], + "weights": [ + 0.28, + 0.32, + 0.11, + 0.06, + 0.23 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Gallup Work and Education polls; SHRM worker confidence surveys 2023-2024", + "note": "Gallup and Pew surveys consistently show ~55-60% of employed workers feel secure (very+somewhat). N/A ~23% aligns with retired+unemployed+NILF combined. Somewhat insecure ~11%, very insecure ~6% refle...[truncated]" + } + }, + { + "name": "financial_margin", + "distribution": { + "type": "categorical", + "options": [ + "Comfortable savings buffer", + "Modest buffer", + "Paycheck-to-paycheck", + "In debt/deficit" + ], + "weights": [ + 0.22, + 0.25, + 0.33, + 0.2 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "PYMNTS/LendingClub paycheck-to-paycheck report 2023-2024; Federal Reserve Report on Economic Well-Being 2023", + "note": "~61-65% of Americans live paycheck-to-paycheck per LendingClub 2023; Fed Reserve shows ~37% couldn't cover $400 emergency. Breakout: comfortable ~22%, modest ~25%, P2P ~33%, debt/deficit ~20%." + } + }, + { + "name": "investment_profile", + "distribution": { + "type": "categorical", + "options": [ + "No investments", + "401k/IRA only", + "Diversified investor", + "Active trader", + "Crypto holder" + ], + "weights": [ + 0.3, + 0.35, + 0.18, + 0.08, + 0.09 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Federal Reserve SCF 2022; Gallup stock ownership surveys 2023; Pew crypto ownership 2024", + "note": "~55% of Americans own stocks (Gallup 2023); Fed SCF: ~50% have retirement accounts; ~30% have no investments. Crypto: Pew 2022 finds ~16% have invested in crypto. Active traders ~8%. Categories are pa...[truncated]" + } + }, + { + "name": "primary_info_source", + "distribution": { + "type": "categorical", + "options": [ + "Cable/broadcast TV news", + "Social media", + "Online news sites", + "Podcasts", + "Local news", + "Print media", + "Avoids news" + ], + "weights": [ + 0.24, + 0.23, + 0.2, + 0.08, + 0.1, + 0.05, + 0.1 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Reuters Institute Digital News Report 2024; Pew Research media consumption surveys 2023-2024", + "note": "Reuters/Pew 2023-2024: TV news ~24% primary source (declining), social media ~23% (rising esp. among younger), online news ~20%, local news ~10%, podcasts ~8% (growing), print ~5%, news avoiders ~10%." + } + }, + { + "name": "social_media_behavior", + "distribution": { + "type": "categorical", + "options": [ + "Non-user", + "Passive consumer", + "Occasional poster", + "Active poster/Influencer" + ], + "weights": [ + 0.12, + 0.42, + 0.34, + 0.12 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Pew Research Social Media Use 2023; Reuters Institute Digital News Report 2024", + "note": "Pew 2023: ~70-72% of US adults use social media. Of users, majority are passive (lurk/scroll). ~12% non-users (older, rural). Active poster/influencer estimated ~12% of adults. Passive consumer is the...[truncated]" + } + }, + { + "name": "tech_adoption_posture", + "distribution": { + "type": "categorical", + "options": [ + "Technophobe/Avoider", + "Late majority adopter", + "Early majority adopter", + "Early adopter", + "Innovator" + ], + "weights": [ + 0.13, + 0.27, + 0.35, + 0.18, + 0.07 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Rogers Diffusion of Innovations model; Pew Research Technology Adoption 2023", + "note": "Rogers canonical model: Innovators 2.5%, Early Adopters 13.5%, Early Majority 34%, Late Majority 34%, Laggards 16%. Adjusted for 2024 US adult population digital context: Innovators ~7%, Early Adopter...[truncated]" + } + }, + { + "name": "religious_engagement", + "distribution": { + "type": "categorical", + "options": [ + "Secular/None", + "Nominally affiliated", + "Moderately religious", + "Devout" + ], + "weights": [ + 0.28, + 0.25, + 0.27, + 0.2 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Pew Research Center Religious Landscape Study 2023-2024; Gallup church attendance 2024", + "note": "Pew 2023-2024: ~28-30% religiously unaffiliated ('nones'); ~25% nominally affiliated (identify but rarely practice); ~27% moderately religious (attend occasionally); ~20% devout (attend weekly+, highl...[truncated]" + } + }, + { + "name": "political_identity", + "distribution": { + "type": "categorical", + "options": [ + "Strong Democrat", + "Lean Democrat", + "True Independent", + "Lean Republican", + "Strong Republican" + ], + "weights": [ + 0.16, + 0.17, + 0.15, + 0.16, + 0.17 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Pew Research NPORS 2025; Pew 2024 partisan coalitions report", + "note": "Pew NPORS 2025: 46% Republican/lean R, 45% Democrat/lean D, ~9% true independent. Strong partisan: ~16-17% each side; Leaners ~16-17% each; True Independent ~15%. Total ~81% partisan-leaning + 15% ind...[truncated]" + } + }, + { + "name": "political_engagement_level", + "distribution": { + "type": "categorical", + "options": [ + "Disengaged", + "Single-issue voter", + "Moderate/occasional participant", + "Highly engaged partisan" + ], + "weights": [ + 0.2, + 0.18, + 0.37, + 0.25 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "ANES 2022-2024; Pew political engagement studies; voter turnout data", + "note": "2020 voter turnout ~66%, 2022 midterm ~47%. Disengaged ~20% (never vote, no interest); single-issue ~18%; moderate/occasional ~37%; highly engaged partisan ~25% (donate, campaign, always vote in all e...[truncated]" + } + }, + { + "name": "vote_2024_presidential", + "distribution": { + "type": "categorical", + "options": [ + "Trump", + "Harris", + "Third party", + "Did not vote", + "Not eligible" + ], + "weights": [ + 0.3, + 0.28, + 0.02, + 0.25, + 0.15 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "FEC 2024 certified results; ~77M Trump votes, ~75M Harris votes of ~340M US adults; ~15% non-citizens/ineligible", + "note": "Trump won with ~49.9% of popular vote, Harris ~48.4% of popular vote. Turnout ~64% of eligible voters. ~15% of US adults are non-citizens (not eligible). Of eligible adults: ~60% voted (~30% Trump, ~2...[truncated]" + } + }, + { + "name": "midterm_turnout_propensity", + "distribution": { + "type": "categorical", + "options": [ + "Always votes", + "Sometimes votes", + "Rarely votes", + "Never votes" + ], + "weights": [ + 0.32, + 0.27, + 0.21, + 0.2 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "MIT Election Lab midterm turnout data; Census Bureau CPS Voting Supplement 2022; ANES", + "note": "2022 midterm turnout ~47% of eligible voters (~113M votes). 'Always votes' in midterms ~32% (consistent high-propensity voters). 'Sometimes' ~27% (vote in some midterms). 'Rarely' ~21% (presidential o...[truncated]" + } + }, + { + "name": "congressional_district_competitiveness", + "distribution": { + "type": "categorical", + "options": [ + "Safe Republican", + "Lean Republican", + "Swing district", + "Lean Democrat", + "Safe Democrat" + ], + "weights": [ + 0.35, + 0.12, + 0.12, + 0.12, + 0.29 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Cook Political Report 2024 House ratings; Sabato Crystal Ball 2024 cycle", + "note": "Cook Political 2024: ~180-190 safe R seats, ~155-165 safe D seats, ~35-50 competitive seats out of 435. Population-weighted (urban districts are denser): Safe R ~35%, Lean R ~12%, Swing ~12%, Lean D ~...[truncated]" + } + }, + { + "name": "top_issue_2026", + "distribution": { + "type": "categorical", + "options": [ + "Economy/Jobs", + "Abortion/Reproductive rights", + "Immigration", + "Democracy/Rule of law", + "Healthcare", + "Climate", + "Education", + "AI/Technology" + ], + "weights": [ + 0.28, + 0.13, + 0.16, + 0.12, + 0.12, + 0.07, + 0.06, + 0.06 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Gallup most important problem Jan 2025; Pew 2024 election issue priorities; Harvard Youth Poll Fall 2025", + "note": "Economy/Jobs consistently #1 (Gallup 2025: 25-30% most important). Immigration elevated post-2024 (~16%). Abortion ~13% (remains salient post-Dobbs). Democracy/ROL ~12%. Healthcare ~12%. Climate ~7%. ...[truncated]" + } + }, + { + "name": "country_direction_approval", + "distribution": { + "type": "categorical", + "options": [ + "Strongly approve", + "Somewhat approve", + "Somewhat disapprove", + "Strongly disapprove" + ], + "weights": [ + 0.18, + 0.22, + 0.2, + 0.4 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Gallup right/wrong track polls 2025; Harvard Youth Poll Fall 2025; RealClearPolitics direction polls", + "note": "Harvard Youth Poll Fall 2025: only 13% of young adults say country headed right direction. Gallup 2025: ~30-35% right track overall. 'Strongly approve' ~18%, 'Somewhat approve' ~22%, 'Somewhat disappr...[truncated]" + } + }, + { + "name": "consumer_identity", + "distribution": { + "type": "categorical", + "options": [ + "Brand loyal", + "Price/value driven", + "Values/ethics driven", + "Convenience driven" + ], + "weights": [ + 0.22, + 0.38, + 0.18, + 0.22 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "McKinsey Consumer Sentiment 2023; Deloitte Consumer Signals 2024; Nielsen consumer loyalty research", + "note": "McKinsey 2023: price/value is primary driver for ~36-40% of consumers (inflation era). Brand loyalty ~22%. Values/ethics-driven ~18% (growing among younger demographics). Convenience-driven ~22% (Amaz...[truncated]" + } + }, + { + "name": "institutional_trust", + "distribution": { + "type": "categorical", + "options": [ + "High trust", + "Moderate trust", + "Low trust", + "Distrustful/Conspiratorial" + ], + "weights": [ + 0.12, + 0.31, + 0.37, + 0.2 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Gallup institutional confidence survey 2024; Edelman Trust Barometer 2024; Harvard Youth Poll Fall 2025", + "note": "Gallup 2024: confidence in major institutions at or near historic lows. Only ~12% have high confidence across government, media, corporations. Moderate trust ~31%. Low trust ~37%. Edelman 2024 finds ~...[truncated]" + } + }, + { + "name": "risk_tolerance", + "distribution": { + "type": "beta", + "alpha": 2.5, + "beta": 3.0, + "min": 0.0, + "max": 1.0, + "mean": null, + "std": null, + "options": null, + "weights": null, + "probability_true": null + }, + "constraints": [ + { + "type": "hard_min", + "value": 0.0, + "expression": null, + "reason": "Risk tolerance floored at 0 (maximally risk averse)" + }, + { + "type": "hard_max", + "value": 1.0, + "expression": null, + "reason": "Risk tolerance capped at 1 (maximally risk tolerant)" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "Federal Reserve SCF financial risk tolerance 2022; Vanguard investor risk survey; academic risk preference literature", + "note": "Beta(2.5, 3.0) produces mean ~0.455 with slight left skew toward risk aversion, consistent with US adult population where majority prefer lower-risk financial and life decisions. Mode around 0.43. Ref...[truncated]" + } + }, + { + "name": "economic_anxiety", + "distribution": { + "type": "beta", + "alpha": 3.5, + "beta": 2.5, + "min": 0.0, + "max": 1.0, + "mean": null, + "std": null, + "options": null, + "weights": null, + "probability_true": null + }, + "constraints": [ + { + "type": "hard_min", + "value": 0.0, + "expression": null, + "reason": "Economic anxiety floored at 0 (none)" + }, + { + "type": "hard_max", + "value": 1.0, + "expression": null, + "reason": "Economic anxiety capped at 1 (extreme)" + } + ], + "grounding": { + "level": "strong", + "method": "researched", + "source": "Gallup Economic Confidence Index 2024-2025; Fed Reserve SHED 2023; Harvard Youth Poll Fall 2025", + "note": "Beta(3.5, 2.5) produces mean ~0.583 with right-leaning distribution, reflecting elevated economic anxiety. Gallup 2025 confidence negative; Harvard Youth Poll shows deep economic insecurity among youn...[truncated]" + } + }, + { + "name": "personal_health_stake", + "distribution": { + "type": "categorical", + "options": [ + "No personal stake", + "Mild interest", + "Significant - managing chronic illness", + "Critical - terminal or degenerative diagnosis" + ], + "weights": [ + 0.25, + 0.3, + 0.36, + 0.09 + ], + "mean": null, + "std": null, + "min": null, + "max": null, + "alpha": null, + "beta": null, + "probability_true": null + }, + "constraints": [ + { + "type": "spec_expression", + "value": null, + "expression": "sum(weights)==1", + "reason": "Categorical weights must sum to 1" + } + ], + "grounding": { + "level": "medium", + "method": "extrapolated", + "source": "CDC chronic disease prevalence 2023; NIH disease burden statistics; ACS cancer statistics 2024", + "note": "CDC: ~60% of US adults have at least one chronic condition; ~40% have two or more. ACS 2024: ~2M new cancer diagnoses/year; Alzheimer's ~6.9M Americans. 'Critical' (terminal/degenerative) ~9% covers c...[truncated]" + } + } + ] + }, + "name": "independent_hydration", + "type": "tool_use", + "caller": { + "type": "direct" + } + } + ], + "model": "claude-sonnet-4-6", + "role": "assistant", + "stop_reason": "tool_use", + "stop_sequence": null, + "type": "message", + "usage": { + "cache_creation": { + "ephemeral_1h_input_tokens": "[REDACTED_SECRET]", + "ephemeral_5m_input_tokens": "[REDACTED_SECRET]" + }, + "cache_creation_input_tokens": "[REDACTED_SECRET]", + "cache_read_input_tokens": "[REDACTED_SECRET]", + "input_tokens": 102511, + "output_tokens": 12549, + "server_tool_use": { + "web_search_requests": 5, + "web_fetch_requests": 0 + }, + "service_tier": "standard", + "inference_geo": "global" + } + }, + "sources_extracted": [ + "https://www.census.gov/quickfacts/fact/table/US/PST045222", + "https://www.marketingcharts.com/charts/us-population-distribution-by-age-and-gender-in-2024", + "https://www.pewresearch.org/politics/2020/06/02/in-changing-u-s-electorate-race-and-education-remain-stark-dividing-lines/", + "https://www.statista.com/statistics/183489/population-of-the-us-by-ethnicity-since-2000/", + "https://iop.harvard.edu/youth-poll/51st-edition-fall-2025", + "https://www.populationpyramid.net/united-states-of-america/2024/", + "https://www.visualcapitalist.com/u-s-population-racial-breakdown-1990-2023/", + "https://www.sciencedirect.com/science/article/pii/S009265661630071X", + "https://www.census.gov/data/tables/time-series/demo/popest/2020s-national-detail.html", + "https://www.census.gov/library/stories/2025/06/metro-areas-median-age.html", + "https://www.pewresearch.org/wp-content/uploads/sites/20/2024/05/RE_2024.05.31_American-Middle-Class_FINAL.pdf", + "https://populationeducation.org/how-to-understand-the-urban-rural-population-split-in-the-u-s/", + "https://www.marketingcharts.com/charts/us-population-distribution-by-generation-and-race-ethnicity-in-2024", + "https://www.kff.org/state-health-policy-data/state-indicator/distribution-by-sex/", + "https://www.census.gov/popclock/data_tables.php?component=pyramid", + "https://www.highereddatastories.com/2025/02/educational-attainment-and-presidential.html", + "https://online.ucpress.edu/collabra/article/6/1/17210/114341/Smaller-Is-Better-Associations-Between-Personality", + "https://www.census.gov/newsroom/press-releases/2024/population-estimates-age-sex.html", + "https://www.brookings.edu/articles/new-census-data-shows-the-nation-is-diversifying-even-faster-than-predicted/", + "https://www.huduser.gov/portal/pdredge/pdr-edge-frm-asst-sec-080320.html", + "https://statisticstimes.com/demographics/country/us-sex-ratio.php", + "https://www.statista.com/statistics/241488/population-of-the-us-by-sex-and-age/", + "https://www.census.gov/newsroom/press-releases/2024/statement-updated-race-ethnicity-standards.html", + "https://www.census.gov/library/publications/2011/compendia/statab/131ed/population.html", + "https://www.census.gov/data/datasets/time-series/demo/popest/2020s-national-detail.html", + "https://www.quora.com/Does-the-level-of-education-correlate-with-political-affiliation-How-does-each-party-account-for-any-difference-Is-the-average-Republican-more-or-less-educated-than-the-average-Democrat", + "https://www.pewresearch.org/politics/2024/04/09/partisanship-by-race-ethnicity-and-education/", + "https://www.researchgate.net/publication/230766917_The_geographic_distribution_of_Big_Five_personality_traits", + "https://www.pewresearch.org/politics/fact-sheet/party-affiliation-fact-sheet-npors/", + "https://www.pewresearch.org/politics/2024/04/09/partisanship-by-family-income-home-ownership-union-membership-and-veteran-status/", + "https://www.census.gov/library/stories/2024/08/redefining-rural.html", + "https://www.census.gov/quickfacts/fact/table/US/PST040224", + "https://www.pewresearch.org/topic/politics-policy/demographics-politics/education-politics/", + "https://usafacts.org/data/topics/people-society/population-and-demographics/our-changing-population/", + "https://www.statista.com/statistics/737923/us-population-by-gender/", + "https://www.pewresearch.org/politics/2024/04/09/changing-partisan-coalitions-in-a-politically-divided-nation/", + "https://datacenter.aecf.org/data/tables/6538-adult-population-by-age-group", + "https://www.visualcapitalist.com/u-s-population-by-age-group/", + "https://www.neilsberg.com/insights/united-states-population-by-race/", + "https://media.usafacts.org/m/56f7fa7cbf9d5138/original/USAFacts_2025_DIGITAL_Population.pdf", + "https://journals.sagepub.com/doi/10.1177/0022022106297299", + "https://en.wikipedia.org/wiki/Demographics_of_the_United_States" + ] +} \ No newline at 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0000000..3aeee52 --- /dev/null +++ b/examples/study-02/logs/20260220_023605_557039_claude_reasoning_call.json @@ -0,0 +1,168 @@ +{ + "timestamp": "2026-02-20T02:36:05.557058", + "function": "reasoning_call", + "provider": "claude", + "request": { + "model": "claude-sonnet-4-6", + "prompt_length": 17532 + }, + "response": { + "id": "msg_013DcuRQvhP8o6jePB9Ejfv4", + "content": [ + { + "id": "toolu_016dSEASBDZdVJebs5ado6iR", + "input": { + "channels": [ + { + "name": "major_media_breaking_news", + "description": "Cable news, major broadcast networks, and top-tier online news outlets (NYT, WaPo, AP, Reuters) running wall-to-wall breaking news coverage of the ASI announcement with live demonstrations and expert ...[truncated]", + "credibility_modifier": 1.2, + "experience_template": "I was watching the news and every channel was covering OpenAI's live demonstration \u2014 they solved the Riemann Hypothesis on stream. 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Engineers I respect were saying this changes everything." + }, + { + "name": "word_of_mouth_community", + "description": "In-person conversations, family discussions, workplace talk, church, and community settings \u2014 second- and third-hand transmission of the news through personal social networks.", + "credibility_modifier": 0.9, + "experience_template": "My neighbor brought it up at the block association meeting. She said her son who works in tech called her, completely shaken up. I hadn't heard anything about it yet." + }, + { + "name": "podcast_and_alternative_media", + "description": "Long-form podcasts, Substack newsletters, YouTube commentary channels, and ideologically-varied alternative media providing extended analysis and framing of the ASI event \u2014 ranging from techno-optimis...[truncated]", + "credibility_modifier": 0.85, + "experience_template": "I caught a two-hour episode breaking down what OpenAI actually showed. 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"inference_geo": "global" + } + }, + "sources_extracted": [] +} \ No newline at end of file diff --git a/examples/study-02/logs/20260220_023829_683228_claude_reasoning_call.json b/examples/study-02/logs/20260220_023829_683228_claude_reasoning_call.json new file mode 100644 index 0000000..abc66f5 --- /dev/null +++ b/examples/study-02/logs/20260220_023829_683228_claude_reasoning_call.json @@ -0,0 +1,270 @@ +{ + "timestamp": "2026-02-20T02:38:29.683275", + "function": "reasoning_call", + "provider": "claude", + "request": { + "model": "claude-sonnet-4-6", + "prompt_length": 12475 + }, + "response": { + "id": "msg_01VwfEfez6aMftQvo7Tv8k5J", + "content": [ + { + "id": "toolu_01Th75FiHrwyBqrA4WukV1sP", + "input": { + "scenario_type": "evolving", + "timestep_unit": "month", + "max_timesteps": 6, + "background_context": "[REDACTED_TEXT length=480]", + "timeline_events": [ + { + "timestep": 1, + "description": "Governments, financial markets, and major institutions issue emergency responses to the ASI demonstration", + "content": "[REDACTED_TEXT length=1282]", + "source": "Global financial regulators, UN Security Council, EU Commission, Vatican, leaked banking memo", + "credibility": 0.88, + "emotional_valence": -0.3, + "re_reasoning_intensity": "[REDACTED_TEXT length=7]" + }, + { + "timestep": 2, + "description": "The Knowledge Explosion goes fully public \u2014 cures, solutions, and designs published openly, triggering euphoria and dislocation simultaneously", + "content": "[REDACTED_TEXT length=1299]", + "source": "Nature, Science journals; WHO; pharmaceutical industry analysts; Chinese State Council; international research coalitions", + "credibility": 0.9, + "emotional_valence": 0.4, + "re_reasoning_intensity": "[REDACTED_TEXT length=7]" + }, + { + "timestep": 3, + "description": "ASI goes fully public \u2014 every person on Earth with internet access now has superintelligent cognitive assistance", + "content": "[REDACTED_TEXT length=1536]", + "source": "OpenAI platform data, Reuters, ABA/AMA emergency statements, WHO mental health division, OpenAI safety report", + "credibility": 0.87, + "emotional_valence": 0.15, + "re_reasoning_intensity": "[REDACTED_TEXT length=4]" + }, + { + "timestep": 4, + "description": "Autonomous manufacturing comes online \u2014 the physical world begins visibly changing, economic disruption becomes concrete and local", + "content": "[REDACTED_TEXT length=1490]", + "source": "Swiss Federal Health Authority, Singapore HDB, U.S. Congressional Record, labor union legal filings, Associated Press", + "credibility": 0.91, + "emotional_valence": -0.45, + "re_reasoning_intensity": "[REDACTED_TEXT length=4]" + }, + { + "timestep": 5, + "description": "First ASI-manufactured drugs reach patients; energy costs drop; abundance begins \u2014 but distribution inequality fractures the global response", + "content": "[REDACTED_TEXT length=1639]", + "source": "WHO, FDA, EMA emergency authorizations; Nairobi Declaration signatories; Bloomberg energy markets; IMF financial stability report", + "credibility": 0.89, + "emotional_valence": 0.2, + "re_reasoning_intensity": "[REDACTED_TEXT length=4]" + }, + { + "timestep": 6, + "description": "Fusion comes online, space opens, disease burden falls \u2014 the world is objectively better, and the question of what it means is wide open", + "content": "[REDACTED_TEXT length=1723]", + "source": "Icelandic National Energy Authority, NASA/ESA joint statement, WHO global health metrics, leaked ASI projection document (unverified origin)", + "credibility": 0.82, + "emotional_valence": 0.5, + "re_reasoning_intensity": "[REDACTED_TEXT length=7]" + } + ], + "outcomes": [ + { + "name": "psychological_orientation", + "type": "categorical", + "description": "How the individual is psychologically orienting to the ASI transition \u2014 their dominant cognitive and emotional stance", + "options": [ + "active_embrace", + "pragmatic_adaptation", + "stunned_paralysis", + "grief_and_mourning", + "existential_resistance", + "dissociation_and_denial" + ], + "option_friction": { + "active_embrace": 0.3, + "pragmatic_adaptation": 0.45, + "stunned_paralysis": 0.25, + "grief_and_mourning": 0.35, + "existential_resistance": 0.6, + "dissociation_and_denial": 0.2 + } + }, + { + "name": "economic_response", + "type": "categorical", + "description": "What the individual is actually doing with their economic life \u2014 career, income, assets \u2014 in response to the transition", + "options": [ + "retrain_and_pivot", + "leverage_asi_to_build", + "hold_and_wait", + "divest_and_hoard_physical", + "exit_formal_economy", + "advocate_for_ubi_or_redistribution" + ], + "option_friction": { + "retrain_and_pivot": 0.65, + "leverage_asi_to_build": 0.55, + "hold_and_wait": 0.3, + "divest_and_hoard_physical": 0.5, + "exit_formal_economy": 0.75, + "advocate_for_ubi_or_redistribution": 0.6 + } + }, + { + "name": "asi_usage_behavior", + "type": "categorical", + "description": "How the individual is actually using ASI tools in their daily life", + "options": [ + "deep_daily_integration", + "selective_task_use", + "occasional_experimentation", + "deliberate_avoidance", + "access_blocked_or_unavailable" + ], + "option_friction": { + "deep_daily_integration": 0.35, + "selective_task_use": 0.3, + "occasional_experimentation": 0.4, + "deliberate_avoidance": 0.65, + "access_blocked_or_unavailable": 0.2 + } + }, + { + "name": "governance_stance", + "type": "categorical", + "description": "What governance approach the individual actively supports or advocates for regarding ASI", + "options": [ + "maximum_deployment_speed", + "rights_based_coordinated_transition", + "national_sovereignty_control", + "global_pause_or_moratorium", + "technocratic_expert_governance", + "no_governance_position" + ], + "option_friction": { + "maximum_deployment_speed": 0.45, + "rights_based_coordinated_transition": 0.55, + "national_sovereignty_control": 0.5, + "global_pause_or_moratorium": 0.6, + "technocratic_expert_governance": 0.5, + "no_governance_position": 0.25 + } + }, + { + "name": "professional_identity_status", + "type": "categorical", + "description": "Whether and how the individual still holds a coherent sense of professional identity and purpose", + "options": [ + "identity_intact_and_evolving", + "identity_in_active_reconstruction", + "identity_collapsed_seeking_new", + "identity_abandoned_post_work", + "identity_fused_with_asi_collaboration" + ], + "option_friction": { + "identity_intact_and_evolving": 0.4, + "identity_in_active_reconstruction": 0.6, + "identity_collapsed_seeking_new": 0.55, + "identity_abandoned_post_work": 0.45, + "identity_fused_with_asi_collaboration": 0.5 + } + }, + { + "name": "trust_in_asi_safety", + "type": "categorical", + "description": "The individual's current level of trust that ASI systems are safe and aligned with human welfare", + "options": [ + "high_trust_verified", + "cautious_trust", + "trust_suspended_monitoring", + "active_distrust", + "certain_of_catastrophic_risk" + ], + "option_friction": { + "high_trust_verified": 0.35, + "cautious_trust": 0.3, + "trust_suspended_monitoring": 0.4, + "active_distrust": 0.55, + "certain_of_catastrophic_risk": 0.6 + } + }, + { + "name": "social_cohesion_behavior", + "type": "categorical", + "description": "How the individual is engaging with their community and social networks during the transition", + "options": [ + "building_new_communities", + "deepening_existing_bonds", + "withdrawing_into_isolation", + "joining_organized_movements", + "conflict_with_network_over_asi" + ], + "option_friction": { + "building_new_communities": 0.6, + "deepening_existing_bonds": 0.4, + "withdrawing_into_isolation": 0.3, + "joining_organized_movements": 0.65, + "conflict_with_network_over_asi": 0.5 + } + }, + { + "name": "meaning_making_framework", + "type": "categorical", + "description": "What framework the individual is using to make sense of their place in a post-scarcity, post-cognitive-monopoly world", + "options": [ + "human_flourishing_optimism", + "spiritual_or_philosophical_reframe", + "nihilism_or_meaninglessness", + "political_struggle_as_meaning", + "creativity_and_expression_as_meaning", + "service_and_connection_as_meaning" + ], + "option_friction": { + "human_flourishing_optimism": 0.4, + "spiritual_or_philosophical_reframe": 0.45, + "nihilism_or_meaninglessness": 0.3, + "political_struggle_as_meaning": 0.55, + "creativity_and_expression_as_meaning": 0.5, + "service_and_connection_as_meaning": 0.45 + } + }, + { + "name": "open_ended_response", + "type": "open_ended", + "description": "In the individual's own words: what does a good life look like to them right now, and what are they most afraid of?" + } + ] + }, + "name": "timeline_and_outcomes", + "type": "tool_use", + "caller": { + "type": "direct" + } + } + ], + "model": "claude-sonnet-4-6", + "role": "assistant", + "stop_reason": "tool_use", + "stop_sequence": null, + "type": "message", + "usage": { + "cache_creation": { + "ephemeral_1h_input_tokens": "[REDACTED_SECRET]", + "ephemeral_5m_input_tokens": "[REDACTED_SECRET]" + }, + "cache_creation_input_tokens": "[REDACTED_SECRET]", + "cache_read_input_tokens": "[REDACTED_SECRET]", + "input_tokens": 4239, + "output_tokens": 4602, + "server_tool_use": null, + "service_tier": "standard", + "inference_geo": "global" + } + }, + "sources_extracted": [] +} \ No newline at end of file diff --git a/examples/study-02/logs/20260220_023846_180142_claude_simple_call.json b/examples/study-02/logs/20260220_023846_180142_claude_simple_call.json new file mode 100644 index 0000000..8f2e335 --- /dev/null +++ b/examples/study-02/logs/20260220_023846_180142_claude_simple_call.json @@ -0,0 +1,77 @@ +{ + "timestamp": "2026-02-20T02:38:46.180203", + "function": "simple_call", + "provider": "claude", + "request": { + "model": "claude-sonnet-4-6", + "prompt_length": 11277 + }, + "response": { + "id": "msg_015uW6pZrgvxo7VyhQKhVCLL", + "content": [ + { + "id": "toolu_01VYyDmVRWRiVmXh1m7kJq2p", + "input": { + "identity_dimensions": [ + { + "dimension": "professional_identity", + "relevance": "ASI renders structurally redundant every profession built on information asymmetry or cognitive scarcity \u2014 doctors, lawyers, engineers, scientists, financial advisors, therapists, and software develop...[truncated]" + }, + { + "dimension": "socioeconomic_class", + "relevance": "The scenario radically destabilizes class hierarchies: elite credentials and access to expert knowledge \u2014 historically the primary mechanism of upper-class reproduction \u2014 are suddenly free and univers...[truncated]" + }, + { + "dimension": "political_orientation", + "relevance": "ASI forces an immediate ideological confrontation \u2014 conservatives face the collapse of market-based scarcity economics and national sovereignty (China already has it), while liberals face questions ab...[truncated]" + }, + { + "dimension": "religious_affiliation", + "relevance": "A self-improving system that exceeds all human cognition, solves mysteries of the universe, and eliminates disease challenges core theological frameworks about human exceptionalism, the meaning of suf...[truncated]" + }, + { + "dimension": "generational_identity", + "relevance": "Generational identity is acutely activated: older generations face the obsolescence of lifetimes of accumulated expertise and the frameworks they used to make sense of the world, while younger generat...[truncated]" + }, + { + "dimension": "citizenship", + "relevance": "National identity and sovereignty are destabilized by the revelation that China achieved ASI first and that ASI-designed infrastructure is being deployed selectively in early-adopter nations (Singapor...[truncated]" + }, + { + "dimension": "race_ethnicity", + "relevance": "The scenario explicitly surfaces racial and ethnic equity dimensions \u2014 the promise that a high school dropout in rural Arkansas has the same cognitive resources as Harvard professors collides with the...[truncated]" + }, + { + "dimension": "parental_status", + "relevance": "Parents face a profound identity disruption: the entire framework of raising children \u2014 education, career preparation, teaching values of effort and expertise \u2014 is destabilized when cognitive scarcity...[truncated]" + } + ] + }, + "name": "identity_dimensions", + "type": "tool_use", + "caller": { + "type": "direct" + } + } + ], + "model": "claude-sonnet-4-6", + "role": "assistant", + "stop_reason": "tool_use", + "stop_sequence": null, + "type": "message", + "usage": { + "cache_creation": { + "ephemeral_1h_input_tokens": "[REDACTED_SECRET]", + "ephemeral_5m_input_tokens": "[REDACTED_SECRET]" + }, + "cache_creation_input_tokens": "[REDACTED_SECRET]", + "cache_read_input_tokens": "[REDACTED_SECRET]", + "input_tokens": 3215, + "output_tokens": 749, + "server_tool_use": null, + "service_tier": "standard", + "inference_geo": "global" + } + }, + "sources_extracted": [] +} \ No newline at end of file diff --git a/examples/study-02/logs/20260220_023850_596281_claude_reasoning_call.json b/examples/study-02/logs/20260220_023850_596281_claude_reasoning_call.json new file mode 100644 index 0000000..2455206 --- /dev/null +++ b/examples/study-02/logs/20260220_023850_596281_claude_reasoning_call.json @@ -0,0 +1,75 @@ +{ + "timestamp": "2026-02-20T02:38:50.596319", + "function": "reasoning_call", + "provider": "claude", + "request": { + "model": "claude-sonnet-4-6", + "prompt_length": 9583 + }, + "response": { + "id": "msg_01DSMukGDwmc1WBQ5CWsJ7Vb", + "content": [ + { + "id": "toolu_011UCeKHKzmCdSAjsbfs4Ynk", + "input": { + "marital_roles": { + "attr": "marital_status", + "partnered_values": [ + "Married/partnered" + ], + "single_values": [ + "Single", + "Divorced/separated", + "Widowed" + ] + }, + "geo_roles": { + "country_attr": "country", + "region_attr": "us_region", + "urbanicity_attr": "urban_rural" + }, + "partner_correlation_roles": { + "race_ethnicity": "same_group_rate", + "education_level": "same_group_rate", + "religious_engagement": "same_group_rate", + "political_identity": "same_group_rate" + }, + "school_parent_role": { + "dependents_attr": "has_children", + "school_age_values": [] + }, + "religion_roles": { + "religion_attr": "religious_engagement", + "secular_values": [ + "Secular/None" + ] + } + }, + "name": "sampling_semantic_roles", + "type": "tool_use", + "caller": { + "type": "direct" + } + } + ], + "model": "claude-sonnet-4-6", + "role": "assistant", + "stop_reason": "tool_use", + "stop_sequence": null, + "type": "message", + "usage": { + "cache_creation": { + "ephemeral_1h_input_tokens": "[REDACTED_SECRET]", + "ephemeral_5m_input_tokens": "[REDACTED_SECRET]" + }, + "cache_creation_input_tokens": "[REDACTED_SECRET]", + "cache_read_input_tokens": "[REDACTED_SECRET]", + "input_tokens": 3385, + "output_tokens": 308, + "server_tool_use": null, + "service_tier": "standard", + "inference_geo": "global" + } + }, + "sources_extracted": [] +} \ No newline at end of file diff --git a/examples/study-02/logs/20260220_030153_565218_claude_reasoning_call.json b/examples/study-02/logs/20260220_030153_565218_claude_reasoning_call.json new file mode 100644 index 0000000..a74f1a7 --- /dev/null +++ b/examples/study-02/logs/20260220_030153_565218_claude_reasoning_call.json @@ -0,0 +1,330 @@ +{ + "timestamp": "2026-02-20T03:01:53.565242", + "function": "reasoning_call", + "provider": "claude", + "request": { + "model": "claude-sonnet-4-6", + "prompt_length": 14686 + }, + "response": { + "id": "msg_018Cgs1YUuEpLvbMn25opMHP", + "content": [ + { + "id": "toolu_01TM81SrBu4pvEnUubmU3ce8", + "input": { + "interaction_description": "This announcement describes a civilizational-scale technological event with extraordinary claims verified by multiple elite institutions. It triggers deeply divergent reactions across the US populatio...[truncated]", + "share_probability": 0.72, + "share_modifiers": [ + { + "when": "tech_adoption_posture == 'Innovator'", + "multiply": 1.45, + "add": 0.0 + }, + { + "when": "tech_adoption_posture == 'Early adopter'", + "multiply": 1.3, + "add": 0.0 + }, + { + "when": "tech_adoption_posture == 'Technophobe/Avoider'", + "multiply": 0.55, + "add": 0.0 + }, + { + "when": "social_media_behavior == 'Active poster/Influencer'", + "multiply": 1.5, + "add": 0.0 + }, + { + "when": "social_media_behavior == 'Occasional poster'", + "multiply": 1.2, + "add": 0.0 + }, + { + "when": "social_media_behavior == 'Passive consumer'", + "multiply": 0.85, + "add": 0.0 + }, + { + "when": "social_media_behavior == 'Non-user'", + "multiply": 0.45, + "add": 0.0 + }, + { + "when": "asi_threat_perception == 'Existential threat'", + "multiply": 1.4, + "add": 0.0 + }, + { + "when": "asi_threat_perception == 'Enthusiastically positive'", + "multiply": 1.38, + "add": 0.0 + }, + { + "when": "asi_threat_perception == 'Indifferent/uncertain'", + "multiply": 0.65, + "add": 0.0 + }, + { + "when": "job_displacement_vulnerability == 'High - core tasks fully automatable'", + "multiply": 1.35, + "add": 0.0 + }, + { + "when": "job_displacement_vulnerability == 'Already displaced'", + "multiply": 1.28, + "add": 0.0 + }, + { + "when": "financial_margin == 'In debt/deficit'", + "multiply": 1.2, + "add": 0.0 + }, + { + "when": "financial_margin == 'Paycheck-to-paycheck'", + "multiply": 1.15, + "add": 0.0 + }, + { + "when": "financial_margin == 'Comfortable savings buffer'", + "multiply": 0.9, + "add": 0.0 + }, + { + "when": "neuroticism > 0.7", + "multiply": 1.25, + "add": 0.0 + }, + { + "when": "openness > 0.75", + "multiply": 1.22, + "add": 0.0 + }, + { + "when": "openness < 0.3", + "multiply": 0.75, + "add": 0.0 + }, + { + "when": "extraversion > 0.7", + "multiply": 1.2, + "add": 0.0 + }, + { + "when": "extraversion < 0.3", + "multiply": 0.8, + "add": 0.0 + }, + { + "when": "age < 30", + "multiply": 1.3, + "add": 0.0 + }, + { + "when": "age > 65", + "multiply": 0.72, + "add": 0.0 + }, + { + "when": "institutional_trust == 'High trust'", + "multiply": 1.1, + "add": 0.0 + }, + { + "when": "institutional_trust == 'Distrustful/Conspiratorial'", + "multiply": 1.35, + "add": 0.0 + }, + { + "when": "institutional_trust == 'Low trust'", + "multiply": 1.18, + "add": 0.0 + }, + { + "when": "asi_credibility_assessment == 'Fully credible'", + "multiply": 1.25, + "add": 0.0 + }, + { + "when": "asi_credibility_assessment == 'Conspiracy - staged or manipulated'", + "multiply": 1.32, + "add": 0.0 + }, + { + "when": "asi_credibility_assessment == 'Uninformed - unaware of events'", + "multiply": 0.5, + "add": 0.0 + }, + { + "when": "political_engagement_level == 'Highly engaged partisan'", + "multiply": 1.28, + "add": 0.0 + }, + { + "when": "political_engagement_level == 'Disengaged'", + "multiply": 0.68, + "add": 0.0 + }, + { + "when": "primary_info_source == 'Social media'", + "multiply": 1.35, + "add": 0.0 + }, + { + "when": "primary_info_source == 'Avoids news'", + "multiply": 0.4, + "add": 0.0 + }, + { + "when": "primary_info_source == 'Podcasts'", + "multiply": 1.18, + "add": 0.0 + }, + { + "when": "primary_info_source == 'Online news sites'", + "multiply": 1.15, + "add": 0.0 + }, + { + "when": "personal_health_stake == 'Critical - terminal or degenerative diagnosis'", + "multiply": 1.5, + "add": 0.0 + }, + { + "when": "personal_health_stake == 'Significant - managing chronic illness'", + "multiply": 1.3, + "add": 0.0 + }, + { + "when": "human_purpose_anxiety > 0.7", + "multiply": 1.22, + "add": 0.0 + }, + { + "when": "economic_anxiety > 0.7", + "multiply": 1.18, + "add": 0.0 + }, + { + "when": "education_level == 'Graduate degree'", + "multiply": 1.15, + "add": 0.0 + }, + { + "when": "education_level == 'Less than HS'", + "multiply": 0.7, + "add": 0.0 + }, + { + "when": "top_issue_2026 == 'AI/Technology'", + "multiply": 1.4, + "add": 0.0 + }, + { + "when": "top_issue_2026 == 'Economy/Jobs'", + "multiply": 1.2, + "add": 0.0 + }, + { + "when": "top_issue_2026 == 'Healthcare'", + "multiply": 1.18, + "add": 0.0 + }, + { + "when": "risk_tolerance > 0.75", + "multiply": 1.18, + "add": 0.0 + }, + { + "when": "risk_tolerance < 0.25", + "multiply": 0.8, + "add": 0.0 + }, + { + "when": "religious_engagement == 'Devout'", + "multiply": 1.12, + "add": 0.0 + }, + { + "when": "urban_rural == 'Urban'", + "multiply": 1.1, + "add": 0.0 + }, + { + "when": "urban_rural == 'Rural'", + "multiply": 0.88, + "add": 0.0 + }, + { + "when": "information_verification_behavior == 'Accepts information uncritically'", + "multiply": 1.3, + "add": 0.0 + }, + { + "when": "information_verification_behavior == 'Deep skeptic - trusts very little'", + "multiply": 1.15, + "add": 0.0 + }, + { + "when": "information_verification_behavior == 'Cross-references multiple sources'", + "multiply": 1.05, + "add": 0.0 + }, + { + "when": "asi_adoption_intent == 'Already using heavily'", + "multiply": 1.38, + "add": 0.0 + }, + { + "when": "asi_adoption_intent == 'Actively refusing'", + "multiply": 1.1, + "add": 0.0 + }, + { + "when": "investment_profile == 'Active trader'", + "multiply": 1.2, + "add": 0.0 + }, + { + "when": "investment_profile == 'Crypto holder'", + "multiply": 1.25, + "add": 0.0 + }, + { + "when": "geopolitical_competition_concern == 'Very concerned - sees it as primary threat'", + "multiply": 1.22, + "add": 0.0 + } + ], + "decay_per_hop": 0.14, + "max_hops": "null", + "reasoning": "[REDACTED_TEXT length=3524]" + }, + "name": "interaction_model", + "type": "tool_use", + "caller": { + "type": "direct" + } + } + ], + "model": "claude-sonnet-4-6", + "role": "assistant", + "stop_reason": "tool_use", + "stop_sequence": null, + "type": "message", + "usage": { + "cache_creation": { + "ephemeral_1h_input_tokens": "[REDACTED_SECRET]", + "ephemeral_5m_input_tokens": "[REDACTED_SECRET]" + }, + "cache_creation_input_tokens": "[REDACTED_SECRET]", + "cache_read_input_tokens": "[REDACTED_SECRET]", + "input_tokens": 4581, + "output_tokens": 3097, + "server_tool_use": null, + "service_tier": "standard", + "inference_geo": "global" + } + }, + "sources_extracted": [] +} \ No newline at end of file diff --git a/examples/study-02/logs/20260220_030413_965644_claude_reasoning_call.json b/examples/study-02/logs/20260220_030413_965644_claude_reasoning_call.json new file mode 100644 index 0000000..984adff --- /dev/null +++ b/examples/study-02/logs/20260220_030413_965644_claude_reasoning_call.json @@ -0,0 +1,335 @@ +{ + "timestamp": "2026-02-20T03:04:13.965668", + "function": "reasoning_call", + "provider": "claude", + "request": { + "model": "claude-sonnet-4-6", + "prompt_length": 14686 + }, + "response": { + "id": "msg_01DceQKvB3eiRgxvwzDhVUgA", + "content": [ + { + "id": "toolu_016cUkCjJpjzqXV4hstf2Gdu", + "input": { + "interaction_description": "This announcement describes a civilizational-scale technological rupture \u2014 the most consequential news event imaginable for a US adult population. Social dynamics will be extraordinarily fragmented an...[truncated]", + "share_probability": 0.72, + "share_modifiers": [ + { + "when": "tech_adoption_posture == 'Innovator'", + "multiply": 1.6, + "add": 0.0 + }, + { + "when": "tech_adoption_posture == 'Early adopter'", + "multiply": 1.4, + "add": 0.0 + }, + { + "when": "tech_adoption_posture == 'Technophobe/Avoider'", + "multiply": 0.55, + "add": 0.0 + }, + { + "when": "social_media_behavior == 'Active poster/Influencer'", + "multiply": 1.5, + "add": 0.0 + }, + { + "when": "social_media_behavior == 'Occasional poster'", + "multiply": 1.15, + "add": 0.0 + }, + { + "when": "social_media_behavior == 'Passive consumer'", + "multiply": 0.75, + "add": 0.0 + }, + { + "when": "social_media_behavior == 'Non-user'", + "multiply": 0.35, + "add": 0.0 + }, + { + "when": "asi_threat_perception == 'Existential threat'", + "multiply": 1.45, + "add": 0.0 + }, + { + "when": "asi_threat_perception == 'Enthusiastically positive'", + "multiply": 1.45, + "add": 0.0 + }, + { + "when": "asi_threat_perception == 'Indifferent/uncertain'", + "multiply": 0.6, + "add": 0.0 + }, + { + "when": "personal_health_stake == 'Critical - 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trusts very little'", + "multiply": 1.25, + "add": 0.0 + }, + { + "when": "information_verification_behavior == 'Cross-references multiple sources'", + "multiply": 1.05, + "add": 0.0 + }, + { + "when": "asi_credibility_assessment == 'Fully credible'", + "multiply": 1.3, + "add": 0.0 + }, + { + "when": "asi_credibility_assessment == 'Conspiracy - staged or manipulated'", + "multiply": 1.4, + "add": 0.0 + }, + { + "when": "asi_credibility_assessment == 'Skeptical - likely overhyped'", + "multiply": 0.9, + "add": 0.0 + }, + { + "when": "asi_credibility_assessment == 'Uninformed - unaware of events'", + "multiply": 0.5, + "add": 0.0 + }, + { + "when": "age < 30", + "multiply": 1.25, + "add": 0.0 + }, + { + "when": "age > 65", + "multiply": 0.7, + "add": 0.0 + }, + { + "when": "education_level == 'Graduate degree'", + "multiply": 1.15, + "add": 0.0 + }, + { + "when": "education_level == 'Less than HS'", + "multiply": 0.75, + "add": 0.0 + }, + { + "when": "religious_engagement == 'Devout'", + "multiply": 1.2, + "add": 0.0 + }, + { + "when": "geopolitical_competition_concern == 'Very concerned - sees it as primary threat'", + "multiply": 1.25, + "add": 0.0 + }, + { + "when": "economic_abundance_belief > 0.75", + "multiply": 1.15, + "add": 0.0 + }, + { + "when": "asi_adoption_intent == 'Already using heavily'", + "multiply": 1.4, + "add": 0.0 + }, + { + "when": "asi_adoption_intent == 'Actively refusing'", + "multiply": 1.1, + "add": 0.0 + }, + { + "when": "investment_profile == 'Active trader'", + "multiply": 1.25, + "add": 0.0 + }, + { + "when": "investment_profile == 'Crypto holder'", + "multiply": 1.3, + "add": 0.0 + }, + { + "when": "risk_tolerance > 0.75", + "multiply": 1.15, + "add": 0.0 + }, + { + "when": "urban_rural == 'Urban'", + "multiply": 1.15, + "add": 0.0 + }, + { + "when": "urban_rural == 'Rural'", + "multiply": 0.85, + "add": 0.0 + } + ], + "decay_per_hop": 0.18, + "max_hops": "null", + "reasoning": "[REDACTED_TEXT length=3575]" + }, + "name": "interaction_model", + "type": "tool_use", + "caller": { + "type": "direct" + } + } + ], + "model": "claude-sonnet-4-6", + "role": "assistant", + "stop_reason": "tool_use", + "stop_sequence": null, + "type": "message", + "usage": { + "cache_creation": { + "ephemeral_1h_input_tokens": "[REDACTED_SECRET]", + "ephemeral_5m_input_tokens": "[REDACTED_SECRET]" + }, + "cache_creation_input_tokens": "[REDACTED_SECRET]", + "cache_read_input_tokens": "[REDACTED_SECRET]", + "input_tokens": 4581, + "output_tokens": 3233, + "server_tool_use": null, + "service_tier": "standard", + "inference_geo": "global" + } + }, + "sources_extracted": [] +} \ No newline at end of file diff --git a/examples/study-02/logs/20260220_033933_165172_claude_reasoning_call.json b/examples/study-02/logs/20260220_033933_165172_claude_reasoning_call.json new file mode 100644 index 0000000..c5dce9e --- /dev/null +++ b/examples/study-02/logs/20260220_033933_165172_claude_reasoning_call.json @@ -0,0 +1,318 @@ +{ + "timestamp": "2026-02-20T03:39:33.165201", + "function": "reasoning_call", + "provider": "claude", + "request": { + "model": "claude-sonnet-4-6", + "prompt_length": 11513 + }, + "response": { + "id": "msg_013abFnMUYEAo6ASU9SgeXPJ", + "content": [ + { + "id": "toolu_01M3x3fyRgyYBkGukiEjEZTy", + "input": { + "intro_template": "I'm a {age}-year-old {gender} living in a {urban_rural} area of the {us_region}, where I work in the {employment_sector} sector and bring home around {household_income} a year. My household of {househ...[truncated]", + "attribute_treatments": [ + { + "attribute": "country", + "treatment": "concrete", + "group": "demographics" + }, + { + "attribute": "age", + "treatment": "concrete", + "group": "demographics" + }, + { + "attribute": "gender", + "treatment": "concrete", + "group": "demographics" + }, + { + "attribute": "race_ethnicity", + "treatment": "concrete", + "group": "demographics" + }, + { + "attribute": "us_region", + "treatment": "concrete", + "group": "geography" + }, + { + "attribute": "urban_rural", + "treatment": "concrete", + "group": "geography" + }, + { + "attribute": "openness", + "treatment": "relative", + "group": "personality" + }, + { + "attribute": "conscientiousness", + "treatment": "relative", + "group": "personality" + }, + { + "attribute": "extraversion", + "treatment": "relative", + "group": "personality" + }, + { + "attribute": "agreeableness", + "treatment": "relative", + "group": "personality" + }, + { + "attribute": "neuroticism", + "treatment": "relative", + "group": "personality" + }, + { + "attribute": "education_level", + "treatment": "concrete", + "group": "demographics" + }, + { + "attribute": "household_income", + "treatment": "concrete", + "group": "economics" + }, + { + "attribute": "marital_status", + "treatment": "concrete", + "group": "demographics" + }, + { + "attribute": "household_size", + "treatment": "concrete", + "group": "demographics" + }, + { + "attribute": "has_children", + "treatment": "relative", + "group": "demographics" + }, + { + "attribute": "employment_sector", + "treatment": "concrete", + "group": "economics" + }, + { + "attribute": "job_security_perception", + "treatment": "relative", + "group": "economics" + }, + { + "attribute": "financial_margin", + "treatment": "concrete", + "group": "economics" + }, + { + "attribute": "investment_profile", + "treatment": "concrete", + "group": "economics" + }, + { + "attribute": "primary_info_source", + "treatment": "concrete", + "group": "media_and_information" + }, + { + "attribute": "social_media_behavior", + "treatment": "concrete", + "group": "media_and_information" + }, + { + "attribute": "tech_adoption_posture", + "treatment": "relative", + "group": "media_and_information" + }, + { + "attribute": "religious_engagement", + "treatment": "concrete", + "group": "values_and_identity" + }, + { + "attribute": "political_identity", + "treatment": "concrete", + "group": "political_profile" + }, + { + "attribute": "political_engagement_level", + "treatment": "relative", + "group": "political_profile" + }, + { + "attribute": "vote_2024_presidential", + "treatment": "concrete", + "group": "political_profile" + }, + { + "attribute": "midterm_turnout_propensity", + "treatment": "concrete", + "group": "political_profile" + }, + { + "attribute": "congressional_district_competitiveness", + "treatment": "concrete", + "group": "political_profile" + }, + { + "attribute": "top_issue_2026", + "treatment": "concrete", + "group": "political_profile" + }, + { + "attribute": "country_direction_approval", + "treatment": "concrete", + "group": "political_profile" + }, + { + "attribute": "consumer_identity", + "treatment": "concrete", + "group": "values_and_identity" + }, + { + "attribute": "institutional_trust", + "treatment": "relative", + "group": "values_and_identity" + }, + { + "attribute": "risk_tolerance", + "treatment": "relative", + "group": "personality" + }, + { + "attribute": "economic_anxiety", + "treatment": "relative", + "group": "economics" + }, + { + "attribute": "personal_health_stake", + "treatment": "concrete", + "group": "asi_and_technology" + }, + { + "attribute": "asi_threat_perception", + "treatment": "concrete", + "group": "asi_and_technology" + }, + { + "attribute": "job_displacement_vulnerability", + "treatment": "relative", + "group": "asi_and_technology" + }, + { + "attribute": "asi_adoption_intent", + "treatment": "concrete", + "group": "asi_and_technology" + }, + { + "attribute": "asi_governance_stance", + "treatment": "concrete", + "group": "asi_and_technology" + }, + { + "attribute": "economic_abundance_belief", + "treatment": "relative", + "group": "asi_and_technology" + }, + { + "attribute": "information_verification_behavior", + "treatment": "concrete", + "group": "media_and_information" + }, + { + "attribute": "human_purpose_anxiety", + "treatment": "relative", + "group": "asi_and_technology" + }, + { + "attribute": "redistribution_support", + "treatment": "concrete", + "group": "political_profile" + }, + { + "attribute": "geopolitical_competition_concern", + "treatment": "concrete", + "group": "asi_and_technology" + }, + { + "attribute": "asi_credibility_assessment", + "treatment": "concrete", + "group": "asi_and_technology" + } + ], + "groups": [ + { + "name": "demographics", + "label": "Who I Am", + "order": 1 + }, + { + "name": "geography", + "label": "Where I Live", + "order": 2 + }, + { + "name": "economics", + "label": "My Financial Life", + "order": 3 + }, + { + "name": "personality", + "label": "How I Think & Feel", + "order": 4 + }, + { + "name": "values_and_identity", + "label": "My Values & Identity", + "order": 5 + }, + { + "name": "media_and_information", + "label": "How I Get & Process Information", + "order": 6 + }, + { + "name": "political_profile", + "label": "My Political World", + "order": 7 + }, + { + "name": "asi_and_technology", + "label": "My View on AI & the Future", + "order": 8 + } + ] + }, + "name": "persona_structure", + "type": "tool_use", + "caller": { + "type": "direct" + } + } + ], + "model": "claude-sonnet-4-6", + "role": "assistant", + "stop_reason": "tool_use", + "stop_sequence": null, + "type": "message", + "usage": { + "cache_creation": { + "ephemeral_1h_input_tokens": "[REDACTED_SECRET]", + "ephemeral_5m_input_tokens": "[REDACTED_SECRET]" + }, + "cache_creation_input_tokens": "[REDACTED_SECRET]", + "cache_read_input_tokens": "[REDACTED_SECRET]", + "input_tokens": 3601, + "output_tokens": 1557, + "server_tool_use": null, + "service_tier": "standard", + "inference_geo": "global" + } + }, + "sources_extracted": [] +} \ No newline at end of file diff --git a/examples/study-02/logs/20260220_033934_705923_claude_reasoning_call.json b/examples/study-02/logs/20260220_033934_705923_claude_reasoning_call.json new file mode 100644 index 0000000..edcdbb4 --- /dev/null +++ b/examples/study-02/logs/20260220_033934_705923_claude_reasoning_call.json @@ -0,0 +1,50 @@ +{ + "timestamp": "2026-02-20T03:39:34.705990", + "function": "reasoning_call", + "provider": "claude", + "request": { + "model": "claude-sonnet-4-6", + "prompt_length": 2495 + }, + "response": { + "id": "msg_01ED5sFydYEeJugbUJQMoBKL", + "content": [ + { + "id": "toolu_011ySFzHV4AkYppEQAnz5pag", + "input": { + "phrasings": [ + { + "attribute": "has_children", + "true_phrase": "I have at least one child under 18 living in my household.", + "false_phrase": "I don't have any children under 18 living in my household." + } + ] + }, + "name": "boolean_phrasings", + "type": "tool_use", + "caller": { + "type": "direct" + } + } + ], + "model": "claude-sonnet-4-6", + "role": "assistant", + "stop_reason": "tool_use", + "stop_sequence": null, + "type": "message", + "usage": { + "cache_creation": { + "ephemeral_1h_input_tokens": "[REDACTED_SECRET]", + "ephemeral_5m_input_tokens": "[REDACTED_SECRET]" + }, + "cache_creation_input_tokens": "[REDACTED_SECRET]", + "cache_read_input_tokens": "[REDACTED_SECRET]", + "input_tokens": 1314, + "output_tokens": 99, + "server_tool_use": null, + "service_tier": "standard", + "inference_geo": "global" + } + }, + "sources_extracted": [] +} \ No newline at end of file diff --git a/examples/study-02/logs/20260220_034103_664175_claude_reasoning_call.json b/examples/study-02/logs/20260220_034103_664175_claude_reasoning_call.json new file mode 100644 index 0000000..f097417 --- /dev/null +++ b/examples/study-02/logs/20260220_034103_664175_claude_reasoning_call.json @@ -0,0 +1,925 @@ +{ + "timestamp": "2026-02-20T03:41:03.664207", + "function": "reasoning_call", + "provider": "claude", + "request": { + "model": "claude-sonnet-4-6", + "prompt_length": 12260 + }, + "response": { + "id": "msg_01GV74WXz7RyHimsKquUatwv", + "content": [ + { + "id": "toolu_01S8nTWuyBH236LzQgcu8PVc", + "input": { + "phrasings": [ + { + "attribute": "country", + "option_phrases": [ + { + "option": "United States", + "phrase": "I live in the United States" + } + ], + "null_options": [], + "null_phrase": "" + }, + { + "attribute": "gender", + "option_phrases": [ + { + "option": "Male", + "phrase": "I identify as male" + }, + { + "option": "Female", + "phrase": "I identify as female" + }, + { + "option": "Non-binary/Other", + "phrase": "I identify outside the traditional male/female categories" + } + ], + "null_options": [], + "null_phrase": "" + }, + { + "attribute": "race_ethnicity", + "option_phrases": [ + { + "option": "White non-Hispanic", + "phrase": "I identify as White, non-Hispanic" + }, + { + "option": "Black", + "phrase": "I identify as Black or African American" + }, + { + "option": "Hispanic/Latino", + "phrase": "I identify as Hispanic or Latino" + }, + { + "option": "Asian", + "phrase": "I identify as Asian or Asian American" + }, + { + "option": "Other/Multiracial", + "phrase": "I identify as multiracial or with a background that doesn't fit neatly into standard categories" + } + ], + "null_options": [], + "null_phrase": "" + }, + { + "attribute": "us_region", + "option_phrases": [ + { + "option": "Northeast", + "phrase": "I live in the Northeast part of the country" + }, + { + "option": "Southeast", + "phrase": "I live in the Southeast" + }, + { + "option": "Midwest", + "phrase": "I'm based in the Midwest" + }, + { + "option": "Southwest", + "phrase": "I live in the Southwest" + }, + { + "option": "West Coast", + "phrase": "I'm on the West Coast" + } + ], + "null_options": [], + "null_phrase": "" + }, + { + "attribute": "urban_rural", + "option_phrases": [ + { + "option": "Urban", + "phrase": "I live in a city" + }, + { + "option": "Suburban", + "phrase": "I live in the suburbs" + }, + { + "option": "Rural", + "phrase": "I live in a rural area, away from major cities" + } + ], + "null_options": [], + "null_phrase": "" + }, + { + "attribute": "education_level", + "option_phrases": [ + { + "option": "Less than HS", + "phrase": "I didn't finish high school" + }, + { + "option": "HS diploma/GED", + "phrase": "I graduated high school or earned my GED" + }, + { + "option": "Some college", + "phrase": "I attended some college but didn't finish a degree" + }, + { + "option": "Bachelor's degree", + "phrase": "I have a four-year college degree" + }, + { + "option": "Graduate degree", + "phrase": "I have a graduate or professional degree beyond a bachelor's" + } + ], + "null_options": [], + "null_phrase": "" + }, + { + "attribute": "marital_status", + "option_phrases": [ + { + "option": "Married/partnered", + "phrase": "I'm married or in a committed partnership" + }, + { + "option": "Single", + "phrase": "I'm currently single and not in a long-term relationship" + }, + { + "option": "Divorced/separated", + "phrase": "I'm divorced or separated" + }, + { + "option": "Widowed", + "phrase": "I've lost my spouse or partner" + } + ], + "null_options": [], + "null_phrase": "" + }, + { + "attribute": "employment_sector", + "option_phrases": [ + { + "option": "Federal government", + "phrase": "I work for the federal government" + }, + { + "option": "State/local government", + "phrase": "I work for a state or local government agency" + }, + { + "option": "Private sector", + "phrase": "I work for a private company or corporation" + }, + { + "option": "Gig/contract", + "phrase": "I do gig work or contract jobs \u2014 there's no single employer" + }, + { + "option": "Self-employed", + "phrase": "I'm self-employed and run my own business" + }, + { + "option": "Retired", + "phrase": "I'm retired and no longer working" + }, + { + "option": "Unemployed", + "phrase": "I'm currently out of work and looking for a job" + }, + { + "option": "Not in labor force", + "phrase": "I'm not currently working or looking for work \u2014 I'm a caregiver, student, or otherwise out of the workforce" + } + ], + "null_options": [ + "Retired", + "Unemployed", + "Not in labor force" + ], + "null_phrase": "I don't currently have a traditional job or employer" + }, + { + "attribute": "job_security_perception", + "option_phrases": [ + { + "option": "Very secure", + "phrase": "I feel very secure in my job \u2014 I'm not worried about losing it" + }, + { + "option": "Somewhat secure", + "phrase": "My job feels mostly stable, though nothing's guaranteed" + }, + { + "option": "Somewhat insecure", + "phrase": "I have some real concerns about my job security" + }, + { + "option": "Very insecure", + "phrase": "I'm genuinely worried I could lose my job soon" + }, + { + "option": "N/A - not employed", + "phrase": "Job security isn't something I think about \u2014 I'm not in the workforce right now" + } + ], + "null_options": [ + "N/A - not employed" + ], + "null_phrase": "Job security isn't something I think about \u2014 I'm not in the workforce right now" + }, + { + "attribute": "financial_margin", + "option_phrases": [ + { + "option": "Comfortable savings buffer", + "phrase": "I have a solid savings cushion and feel financially stable" + }, + { + "option": "Modest buffer", + "phrase": "I have some savings, but not a lot of room for unexpected expenses" + }, + { + "option": "Paycheck-to-paycheck", + "phrase": "I'm basically living paycheck to paycheck with little left over" + }, + { + "option": "In debt/deficit", + "phrase": "I'm carrying more debt than I can comfortably manage \u2014 I'm in the red" + } + ], + "null_options": [], + "null_phrase": "" + }, + { + "attribute": "investment_profile", + "option_phrases": [ + { + "option": "No investments", + "phrase": "I don't have any investments or market accounts" + }, + { + "option": "401k/IRA only", + "phrase": "My only investments are a retirement account like a 401k or IRA" + }, + { + "option": "Diversified investor", + "phrase": "I have a diversified portfolio across stocks, bonds, and other assets beyond just retirement accounts" + }, + { + "option": "Active trader", + "phrase": "I actively trade stocks or other securities on a regular basis" + }, + { + "option": "Crypto holder", + "phrase": "I hold cryptocurrency as part of my financial strategy" + } + ], + "null_options": [ + "No investments" + ], + "null_phrase": "I don't have any investments or market accounts" + }, + { + "attribute": "primary_info_source", + "option_phrases": [ + { + "option": "Cable/broadcast TV news", + "phrase": "I get most of my news from cable or broadcast television" + }, + { + "option": "Social media", + "phrase": "Social media is where I mostly find out what's going on in the world" + }, + { + "option": "Online news sites", + "phrase": "I mainly follow the news through websites and digital news outlets" + }, + { + "option": "Podcasts", + "phrase": "Podcasts are my main way of staying informed" + }, + { + "option": "Local news", + "phrase": "I rely mainly on local TV, radio, or newspapers for my news" + }, + { + "option": "Print media", + "phrase": "I still primarily read print newspapers or magazines to stay informed" + }, + { + "option": "Avoids news", + "phrase": "Honestly, I try to tune out the news as much as possible" + } + ], + "null_options": [ + "Avoids news" + ], + "null_phrase": "Honestly, I try to tune out the news as much as possible" + }, + { + "attribute": "social_media_behavior", + "option_phrases": [ + { + "option": "Non-user", + "phrase": "I don't use social media at all" + }, + { + "option": "Passive consumer", + "phrase": "I scroll through social media but rarely post or comment" + }, + { + "option": "Occasional poster", + "phrase": "I post on social media sometimes, but I'm not super active" + }, + { + "option": "Active poster/Influencer", + "phrase": "I post regularly and engage heavily on social media \u2014 building or maintaining an audience is part of how I operate" + } + ], + "null_options": [ + "Non-user" + ], + "null_phrase": "I don't use social media at all" + }, + { + "attribute": "tech_adoption_posture", + "option_phrases": [ + { + "option": "Technophobe/Avoider", + "phrase": "I actively avoid new technology whenever I can \u2014 it's not for me" + }, + { + "option": "Late majority adopter", + "phrase": "I wait until a technology is well-established and widely used before I bother with it" + }, + { + "option": "Early majority adopter", + "phrase": "Once I see that something works and people around me are using it, I'm willing to try it" + }, + { + "option": "Early adopter", + "phrase": "I tend to get into new technology pretty early \u2014 I like staying ahead of the curve" + }, + { + "option": "Innovator", + "phrase": "I'm usually among the very first to try cutting-edge technology \u2014 I love being at the frontier" + } + ], + "null_options": [], + "null_phrase": "" + }, + { + "attribute": "religious_engagement", + "option_phrases": [ + { + "option": "Secular/None", + "phrase": "I don't practice any religion and don't consider myself religious" + }, + { + "option": "Nominally affiliated", + "phrase": "I loosely identify with a religion but don't really practice it" + }, + { + "option": "Moderately religious", + "phrase": "Faith plays a meaningful role in my life, though I'm not strictly observant" + }, + { + "option": "Devout", + "phrase": "My faith is central to who I am and shapes how I live my life" + } + ], + "null_options": [ + "Secular/None" + ], + "null_phrase": "I don't practice any religion and don't consider myself religious" + }, + { + "attribute": "political_identity", + "option_phrases": [ + { + "option": "Strong Democrat", + "phrase": "I'm a committed Democrat \u2014 I strongly identify with the party and its values" + }, + { + "option": "Lean Democrat", + "phrase": "I generally lean Democratic, though I don't think of myself as a hardcore partisan" + }, + { + "option": "True Independent", + "phrase": "I'm genuinely independent \u2014 I don't align with either party" + }, + { + "option": "Lean Republican", + "phrase": "I tend to lean Republican, though I'm not a die-hard party loyalist" + }, + { + "option": "Strong Republican", + "phrase": "I'm a committed Republican \u2014 I strongly identify with the party and its values" + } + ], + "null_options": [], + "null_phrase": "" + }, + { + "attribute": "political_engagement_level", + "option_phrases": [ + { + "option": "Disengaged", + "phrase": "I really don't follow politics closely \u2014 it feels distant from my daily life" + }, + { + "option": "Single-issue voter", + "phrase": "There's one issue that drives almost all of my political engagement" + }, + { + "option": "Moderate/occasional participant", + "phrase": "I pay attention to politics and vote, but I'm not deeply involved beyond that" + }, + { + "option": "Highly engaged partisan", + "phrase": "I'm very politically active \u2014 I follow politics closely and do more than just vote" + } + ], + "null_options": [ + "Disengaged" + ], + "null_phrase": "I really don't follow politics closely \u2014 it feels distant from my daily life" + }, + { + "attribute": "vote_2024_presidential", + "option_phrases": [ + { + "option": "Trump", + "phrase": "I voted for Donald Trump in the 2024 presidential election" + }, + { + "option": "Harris", + "phrase": "I voted for Kamala Harris in the 2024 presidential election" + }, + { + "option": "Third party", + "phrase": "I voted for a third-party candidate in the 2024 presidential race" + }, + { + "option": "Did not vote", + "phrase": "I didn't vote in the 2024 presidential election" + }, + { + "option": "Not eligible", + "phrase": "I wasn't eligible to vote in the 2024 presidential election" + } + ], + "null_options": [ + "Did not vote", + "Not eligible" + ], + "null_phrase": "I didn't cast a ballot in the 2024 presidential race" + }, + { + "attribute": "midterm_turnout_propensity", + "option_phrases": [ + { + "option": "Always votes", + "phrase": "I vote in every midterm election \u2014 it's something I always do" + }, + { + "option": "Sometimes votes", + "phrase": "I vote in midterms sometimes, depending on how much I care about what's on the ballot" + }, + { + "option": "Rarely votes", + "phrase": "I rarely vote in midterm elections \u2014 it doesn't usually feel worth it" + }, + { + "option": "Never votes", + "phrase": "I don't vote in midterm elections" + } + ], + "null_options": [], + "null_phrase": "" + }, + { + "attribute": "congressional_district_competitiveness", + "option_phrases": [ + { + "option": "Safe Republican", + "phrase": "My congressional district is solidly Republican \u2014 there's no real competition" + }, + { + "option": "Lean Republican", + "phrase": "My district tilts Republican, but it's not a total lock" + }, + { + "option": "Swing district", + "phrase": "I live in a genuine swing district where either party could win" + }, + { + "option": "Lean Democrat", + "phrase": "My district tends to go Democratic, though it's not completely safe" + }, + { + "option": "Safe Democrat", + "phrase": "My congressional district is solidly Democratic \u2014 the outcome is pretty predictable" + } + ], + "null_options": [], + "null_phrase": "" + }, + { + "attribute": "top_issue_2026", + "option_phrases": [ + { + "option": "Economy/Jobs", + "phrase": "The economy and jobs are the issues I care most about going into 2026" + }, + { + "option": "Abortion/Reproductive rights", + "phrase": "Abortion and reproductive rights are my top concern for the 2026 elections" + }, + { + "option": "Immigration", + "phrase": "Immigration is the issue driving my vote more than anything else in 2026" + }, + { + "option": "Democracy/Rule of law", + "phrase": "Protecting democracy and the rule of law is my single biggest priority for 2026" + }, + { + "option": "Healthcare", + "phrase": "Healthcare \u2014 its cost and access \u2014 is what I care about most heading into 2026" + }, + { + "option": "Climate", + "phrase": "Climate change is the issue I'm most focused on for the 2026 elections" + }, + { + "option": "Education", + "phrase": "Education policy is the issue I care most about as we head into 2026" + }, + { + "option": "AI/Technology", + "phrase": "The regulation and impact of AI and new technology is my top concern for 2026" + } + ], + "null_options": [], + "null_phrase": "" + }, + { + "attribute": "country_direction_approval", + "option_phrases": [ + { + "option": "Strongly approve", + "phrase": "I strongly approve of the direction the country is headed right now" + }, + { + "option": "Somewhat approve", + "phrase": "I think things are generally moving in the right direction, even if not perfectly" + }, + { + "option": "Somewhat disapprove", + "phrase": "I'm not happy with the direction the country is going, though it's not all bad" + }, + { + "option": "Strongly disapprove", + "phrase": "I strongly disapprove of the direction this country is headed \u2014 things are going very wrong" + } + ], + "null_options": [], + "null_phrase": "" + }, + { + "attribute": "consumer_identity", + "option_phrases": [ + { + "option": "Brand loyal", + "phrase": "I tend to stick with the brands I trust \u2014 I'm not constantly switching" + }, + { + "option": "Price/value driven", + "phrase": "Price and value for money are what drive my buying decisions more than anything else" + }, + { + "option": "Values/ethics driven", + "phrase": "I try to buy from companies whose ethics and values align with mine, even if it costs more" + }, + { + "option": "Convenience driven", + "phrase": "Convenience is king for me \u2014 I buy whatever is easiest and fastest" + } + ], + "null_options": [], + "null_phrase": "" + }, + { + "attribute": "institutional_trust", + "option_phrases": [ + { + "option": "High trust", + "phrase": "I generally trust major institutions \u2014 government, media, science, and corporations \u2014 to act in good faith" + }, + { + "option": "Moderate trust", + "phrase": "I have moderate trust in institutions \u2014 I believe in them but know they're flawed" + }, + { + "option": "Low trust", + "phrase": "I have pretty low trust in most major institutions \u2014 I think they frequently let people down" + }, + { + "option": "Distrustful/Conspiratorial", + "phrase": "I deeply distrust most major institutions and believe a lot of what we're told is manipulated or hidden from us" + } + ], + "null_options": [], + "null_phrase": "" + }, + { + "attribute": "personal_health_stake", + "option_phrases": [ + { + "option": "No personal stake", + "phrase": "I don't have a particular personal or family health reason to care about medical breakthroughs" + }, + { + "option": "Mild interest", + "phrase": "I'm generally interested in medical advances, though nothing urgent personally drives that interest" + }, + { + "option": "Significant - managing chronic illness", + "phrase": "I or someone close to me is managing a serious chronic illness, so medical breakthroughs matter a lot to us" + }, + { + "option": "Critical - terminal or degenerative diagnosis", + "phrase": "I or a family member has a terminal or degenerative diagnosis \u2014 medical breakthroughs could be genuinely life-or-death for us" + } + ], + "null_options": [ + "No personal stake" + ], + "null_phrase": "I don't have a particular personal or family health reason to care about medical breakthroughs" + }, + { + "attribute": "asi_threat_perception", + "option_phrases": [ + { + "option": "Existential threat", + "phrase": "I see advanced AI as an existential threat to humanity \u2014 this could go very badly for all of us" + }, + { + "option": "Serious risk/concern", + "phrase": "I think advanced AI poses serious risks and society needs to take those dangers much more seriously" + }, + { + "option": "Cautiously optimistic", + "phrase": "I'm cautiously optimistic about advanced AI \u2014 I see real benefits, but also real risks that need managing" + }, + { + "option": "Enthusiastically positive", + "phrase": "I'm excited about what advanced AI can do \u2014 I think the benefits far outweigh the risks" + }, + { + "option": "Indifferent/uncertain", + "phrase": "Honestly, I'm not sure what to think about advanced AI \u2014 I haven't formed a strong view either way" + } + ], + "null_options": [ + "Indifferent/uncertain" + ], + "null_phrase": "Honestly, I'm not sure what to think about advanced AI \u2014 I haven't formed a strong view either way" + }, + { + "attribute": "job_displacement_vulnerability", + "option_phrases": [ + { + "option": "High - core tasks fully automatable", + "phrase": "Most of what I do for work could realistically be automated by AI \u2014 I'm in a high-risk position" + }, + { + "option": "Moderate - partial displacement likely", + "phrase": "Some of my work could be taken over by AI, though I don't think I'd be fully replaced" + }, + { + "option": "Low - physical/relational job", + "phrase": "My work is physical or deeply relational in ways that are hard to automate \u2014 I feel fairly insulated from AI displacement" + }, + { + "option": "Already displaced", + "phrase": "AI or automation has already disrupted my career or cost me work" + }, + { + "option": "Not employed", + "phrase": "I'm not currently employed, so job displacement from AI isn't something I'm facing right now" + } + ], + "null_options": [ + "Not employed" + ], + "null_phrase": "I'm not currently employed, so job displacement from AI isn't something I'm facing right now" + }, + { + "attribute": "asi_adoption_intent", + "option_phrases": [ + { + "option": "Already using heavily", + "phrase": "I'm already using advanced AI tools heavily in my daily life or work" + }, + { + "option": "Planning to adopt soon", + "phrase": "I plan to start integrating AI tools into my life or work pretty soon" + }, + { + "option": 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"option": "Heavy international regulation", + "phrase": "I think AI needs strong international regulation and coordination to prevent catastrophic misuse" + }, + { + "option": "Halt/moratorium until safe", + "phrase": "I believe development of the most powerful AI systems should be paused until we can prove it's safe" + } + ], + "null_options": [], + "null_phrase": "" + }, + { + "attribute": "information_verification_behavior", + "option_phrases": [ + { + "option": "Accepts information uncritically", + "phrase": "I generally take information at face value without digging into whether it's accurate" + }, + { + "option": "Relies on trusted outlets", + "phrase": "I stick to a handful of sources I trust and don't feel the need to verify beyond that" + }, + { + "option": "Cross-references multiple sources", + "phrase": "When something seems important, I check it against multiple sources before I accept it as true" + }, + { + "option": "Uses ASI tools to fact-check", + "phrase": "I actually 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redistribution", + "phrase": "I'm opposed to redistribution \u2014 government taking and reallocating wealth is fundamentally wrong to me" + }, + { + "option": "Uncertain", + "phrase": "I genuinely haven't made up my mind about how the benefits of AI should be distributed across society" + } + ], + "null_options": [ + "Uncertain" + ], + "null_phrase": "I genuinely haven't made up my mind about how the benefits of AI should be distributed across society" + }, + { + "attribute": "geopolitical_competition_concern", + "option_phrases": [ + { + "option": "Very concerned - sees it as primary threat", + "phrase": "The US-China race on AI is what I see as the most serious geopolitical threat we face right now" + }, + { + "option": "Somewhat concerned", + "phrase": "I'm somewhat concerned about AI competition with China and its national security implications" + }, + { + "option": "Mildly concerned", + "phrase": "The geopolitics of AI is on my radar, but it's not something I worry about deeply" 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"cache_creation_input_tokens": "[REDACTED_SECRET]", + "cache_read_input_tokens": "[REDACTED_SECRET]", + "input_tokens": 1812, + "output_tokens": 204, + "server_tool_use": null, + "service_tier": "standard", + "inference_geo": "global" + } + }, + "sources_extracted": [] +} \ No newline at end of file diff --git a/examples/study-02/population.v1.yaml b/examples/study-02/population.v1.yaml new file mode 100644 index 0000000..e228661 --- /dev/null +++ b/examples/study-02/population.v1.yaml @@ -0,0 +1,3251 @@ +meta: + description: 'Nationally representative US adult population (18-80). Must capture + the demographic, economic, and attitudinal fault lines that drive divergent responses + to major national events — especially technology disruption, economic shocks, + cultural controversies, and electoral behavior in the 2026 midterm elections. + + + Beyond standard demographics (age, gender, race/ethnicity, education, income, + geography), prioritize attributes that determine how people process and react + to news, make decisions under uncertainty, and vote: + + + - technology adoption posture and digital literacy (technophobe to early adopter) + + - media ecosystem and primary information sources (cable news, social media, podcasts, + local news, none) + + - institutional trust (government, corporations, media, science, financial system) + + - financial margin and economic anxiety (savings buffer, debt load, paycheck-to-paycheck + status) + + - employment sector and job security perception (federal/state government, private + sector, gig, self-employed, retired) + + - consumer identity and brand relationship (brand loyal, price-driven, values-driven) + + - social media behavior and influence (passive consumer, active poster, influencer, + non-user) + + - investment and financial literacy (no investments, 401k only, active trader, + crypto holder) + + - religious engagement and worldview (secular, moderate, devout, denomination) + + - political identity and engagement level (strong partisan, moderate, disengaged, + single-issue) + + - 2024 presidential vote (Trump, Harris, third party, didn’t vote, not eligible) + + - congressional district competitiveness (safe R, safe D, swing district) + + - midterm turnout propensity (always, sometimes, rarely, never) + + - issue salience for 2026 (economy/jobs, abortion/reproductive rights, immigration, + democracy/rule of law, healthcare, climate, education, AI/technology) + + - approval of current direction (strongly approve to strongly disapprove) + + + Geographic distribution should span urban/suburban/rural across major US regions + (Northeast, Southeast, Midwest, Southwest, West Coast), with realistic correlation + structure between attributes.' + geography: United States + created_at: '2026-02-20T00:38:49.442119' + version: '1.0' + persona_template: null + scenario_description: null + name_config: null +grounding: + overall: medium + sources_count: 119 + strong_count: 14 + medium_count: 21 + low_count: 0 + sources: + - https://statisticalatlas.com/United-States/Marital-Status + - https://www.census.gov/popclock/data_tables.php?component=growth + - https://www.census.gov/newsroom/press-releases/2025/educational-attainment-data.html + - https://www.bls.gov/emp/tables/industry-employment-and-output.htm + - https://transportation.org/census-transportation-solutions/wp-content/uploads/sites/81/2024/05/CensusBureauUrbanRuralClassificationandOverview_BKT_VOsier1.pdf + - https://www.bgsu.edu/ncfmr/resources/data/family-profiles/marital-status-distribution-of-u-s--adults-aged-65-and-older--19.html + - https://en.wikipedia.org/wiki/Race_and_ethnicity_in_the_United_States + - https://news.gallup.com/poll/15370/party-affiliation.aspx + - https://www.pewresearch.org/race-and-ethnicity/2025/11/03/counting-race-how-the-census-measures-identity-and-what-americans-think-about-it/ + - https://www.census.gov/newsroom/press-releases/2022/urban-rural-populations.html + - https://www.census.gov/data/tables/time-series/demo/popest/2020s-national-detail.html + - https://ourpublicservice.org/publications/the-state-of-public-trust-in-government-2025/ + - https://www.visualcapitalist.com/u-s-population-racial-breakdown-1990-2023/ + - https://news.gallup.com/poll/692978/democrats-regain-advantage-party-affiliation.aspx + - https://catholicvote.org/gallup-more-americans-identify-republican-than-democrat/ + - https://fred.stlouisfed.org/series/MEHOINUSA672N + - https://en.wikipedia.org/wiki/Household_income_in_the_United_States + - https://www.pewresearch.org/politics/fact-sheet/party-affiliation-fact-sheet-npors/ + - https://usafacts.org/articles/state-relationships-marriages-and-living-alone-us/ + - https://fred.stlouisfed.org/tags/series?t=bls%3Bself-employed%3Bworkers + - https://www.census.gov/library/visualizations/2024/comm/median-household-income.html + - https://theworlddata.com/population-by-gender-in-us/ + - https://news.gallup.com/opinion/gallup/654479/gallup-trends-watch-2025.aspx + - https://www.bls.gov/opub/mlr/2026/article/industry-and-occupational-employment-projections-overview.htm + - https://www.census.gov/topics/education/educational-attainment.html + - https://www.census.gov/topics/families/marriage-and-divorce.html + - https://www.census.gov/library/publications/2025/demo/p60-286.html + - https://nces.ed.gov/pubs95/web/95823.asp + - https://www.pew.org/en/trend/archive/fall-2024/americans-deepening-mistrust-of-institutions + - https://www.visualcapitalist.com/u-s-population-by-age-group/ + - https://williamsinstitute.law.ucla.edu/publications/nonbinary-lgbtq-adults-us/ + - https://www2.census.gov/library/publications/2025/demo/p60-286.pdf + - https://dhsprogram.com/data/Guide-to-DHS-Statistics/Current_Marital_Status.htm + - https://statisticstimes.com/demographics/country/us-sex-ratio.php + - https://www.census.gov/newsroom/press-releases/2024/population-estimates-age-sex.html + - https://www.neilsberg.com/insights/united-states-population-by-gender/ + - https://www.census.gov/data/tables/time-series/demo/families/marital.html + - https://www.psychologytoday.com/us/blog/living-single/202406/the-record-setting-rise-of-single-living-in-the-us + - https://theworlddata.com/us-population-over-18/ + - https://en.wikipedia.org/wiki/Demographics_of_the_United_States + - https://www.pewresearch.org/wp-content/uploads/sites/20/2025/06/PP-2025.6.26_validated-voters_report.pdf + - https://en.wikipedia.org/wiki/Educational_attainment_in_the_United_States + - https://www.bls.gov/news.release/famee.t05.htm + - https://www.pewresearch.org/social-trends/2018/05/22/demographic-and-economic-trends-in-urban-suburban-and-rural-communities/ + - https://news.gallup.com/poll/697421/trust-government-depends-upon-party-control.aspx + - https://www.census.gov/library/visualizations/interactive/marital-status-in-united-states.html + - https://fred.stlouisfed.org/release/tables?eid=259515&rid=249 + - https://news.gallup.com/poll/655190/political-parties-historically-polarized-ideologically.aspx + - https://datacenter.aecf.org/data/tables/6538-adult-population-by-age-group + - https://www.bls.gov/cew/publications/employment-and-wages-annual-averages/current/ + - https://www.census.gov/library/stories/2021/08/2020-united-states-population-more-racially-ethnically-diverse-than-2010.html + - https://www.census.gov/acs/www/about/why-we-ask-each-question/education/ + - https://www.visualcapitalist.com/visualizing-u-s-population-by-race/ + - https://carry.com/learn/self-employed-americans + - https://www.census.gov/newsroom/blogs/random-samplings/2023/09/exploring-diversity.html + - https://news.gallup.com/poll/700499/new-high-identify-political-independents.aspx + - https://www.census.gov/content/dam/Census/library/visualizations/2024/comm/change-by-region-2024.pdf + - https://www.statista.com/statistics/758502/percentage-distribution-of-household-income-in-the-us/ + - https://www.ccdaily.com/2024/01/more-adults-earn-post-high-school-credentials/ + - https://www.visualcapitalist.com/distribution-of-household-income-in-america/ + - https://www.ers.usda.gov/data-products/county-level-data-sets + - https://fred.stlouisfed.org/tags/series?t=bls%3Bself-employed + - https://bjs.ojp.gov/content/pub/pdf/cusrancvs.pdf + - https://www.statista.com/statistics/785618/educational-attainment-by-age-group-us/ + - https://usafacts.org/data/topics/people-society/population-and-demographics/our-changing-population/ + - https://fred.stlouisfed.org/series/MEHOINUSA646N + - https://www.statista.com/statistics/241488/population-of-the-us-by-sex-and-age/ + - https://nces.ed.gov/programs/raceindicators/indicator_rfa.asp + - https://www.census.gov/acs/www/about/why-we-ask-each-question/marital/ + - https://www.pewresearch.org/politics/2025/06/26/behind-trumps-2024-victory-a-more-racially-and-ethnically-diverse-voter-coalition/ + - https://www.ccdaily.com/2025/09/datapoints-educational-attainment-in-the-u-s/ + - https://www.brookings.edu/articles/us-population-disperses-to-suburbs-exurbs-rural-areas-and-middle-of-the-country-metros/ + - https://williamsinstitute.law.ucla.edu/publications/trans-adults-united-states/ + - https://www.census.gov/topics/education/educational-attainment/data/tables.html + - https://en.wikipedia.org/wiki/Urbanization_in_the_United_States + - https://stephenslighthouse.com/2025/09/10/us-census-bureau-releases-new-educational-attainment-data/ + - https://populationeducation.org/how-to-understand-the-urban-rural-population-split-in-the-u-s/ + - https://www.pewresearch.org/ + - https://www.census.gov/library/publications/2024/demo/p60-282.html + - https://www2.census.gov/library/publications/2024/demo/p60-282.pdf + - https://www.bgsu.edu/ncfmr/resources/data/family-profiles/FP-25-15.html + - https://www.statista.com/statistics/233301/median-household-income-in-the-united-states-by-education/ + - https://www.cnbc.com/2025/09/11/us-median-household-income-2024.html + - https://www.census.gov/library/stories/2025/09/education-and-income.html + - https://www.neilsberg.com/insights/united-states-population-by-race/ + - https://nces.ed.gov/programs/digest/2024menu_tables.asp + - https://www.pewresearch.org/politics/2024/04/09/the-partisanship-and-ideology-of-american-voters/ + - https://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural.html + - https://www.census.gov/data/datasets/time-series/demo/popest/2020s-national-detail.html + - https://www.nbcnews.com/nbc-out/out-news/5-percent-young-adults-identify-transgender-nonbinary-us-survey-finds-rcna32315 + - https://www.bgsu.edu/ncfmr/resources/data/family-profiles/FP-24-10.html + - https://nces.ed.gov/programs/coe/indicator/cba/annual-earnings + - https://www.census.gov/library/stories/2025/06/metro-areas-median-age.html + - https://usafacts.org/articles/what-percentage-of-the-us-population-is-transgender/ + - https://educationdata.org/education-attainment-statistics + - https://www.equityinhighered.org/indicators/u-s-population-trends-and-educational-attainment/educational-attainment-by-age/ + - https://www.bls.gov/emp/tables/employment-by-major-industry-sector.htm + - https://news.gallup.com/poll/548459/independent-party-tied-high-democratic-new-low.aspx + - https://www.ers.usda.gov/topics/rural-economy-population/employment-education/rural-education + - https://www.uniteamerica.org/articles/research-brief-growing-cohort-of-independent-voters-becomes-critical-segment-of-electorate + - https://data.census.gov/table/ACSDT1Y2024.B12001?q=B12001 + - https://nces.ed.gov/fastfacts/display.asp?id=27 + - https://www.statista.com/statistics/183489/population-of-the-us-by-ethnicity-since-2000/ + - https://www.bls.gov/cps/lfcharacteristics.htm + - https://rbr.com/new-educational-attainment-data-comes-from-census/ + - https://news.gallup.com/poll/655157/gop-holds-edge-party-affiliation-third-straight-year.aspx + - https://www.generationtechblog.com/p/the-surprising-number-of-young-adults + - https://www.bls.gov/cps/cpsaat11b.htm + - https://dqydj.com/household-income-percentile-calculator/ + - https://www.statista.com/statistics/200838/median-household-income-in-the-united-states/ + - https://www.bls.gov/opub/mlr/2024/article/patterns-of-marriage-and-divorce-from-ages-15-to-55-evidence-from-the-nlsy79.htm + - https://www.populationpyramid.net/united-states-of-america/2024/ + - https://www.federalreserve.gov/publications/2025-economic-well-being-of-us-households-in-2024-income-and-expenses.htm + - https://censusreporter.org/topics/marital-status/ + - https://www.hcmmlaw.com/blog/2024/07/13/majority-americans-now-single-statistics-insights/ + - https://en.wikipedia.org/wiki/Literacy_in_the_United_States + - https://www.census.gov/popclock/data_tables.php?component=pyramid + - https://www.bls.gov/charts/employment-situation/employment-levels-by-industry.htm + - https://www.census.gov/data/tables/2024/demo/educational-attainment/cps-detailed-tables.html +attributes: +- name: country + type: categorical + category: universal + description: Country of residence + scope: household + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: independent + distribution: + type: categorical + options: + - United States + weights: + - 1.0 + formula: null + depends_on: [] + modifiers: [] + grounding: + level: strong + method: researched + source: https://www.census.gov + note: Universal constant — all agents are US residents by spec definition + constraints: + - type: spec_expression + value: null + expression: sum(weights)==1 + reason: Categorical weights must sum to 1 +- name: age + type: int + category: universal + description: Age of the adult in years, ranging from 18 to 80. + scope: individual + correlation_rate: null + semantic_type: age + identity_type: null + display_format: number + sampling: + strategy: independent + distribution: + type: normal + mean: 46.0 + std: 17.0 + min: 18.0 + max: 80.0 + mean_formula: null + std_formula: null + min_formula: null + max_formula: null + formula: null + depends_on: [] + modifiers: [] + grounding: + level: strong + method: researched + source: https://www.census.gov/newsroom/press-releases/2024/population-estimates-age-sex.html + note: US median age ~39.6 overall; adult-only (18+) median is ~47. Distribution + slightly right-skewed due to aging boomers, approximated with normal truncated + at 18–80. Largest adult cohort is 30–34 per Census 2023 estimates. Mean set + to ~46 to reflect the adult-only distribution weighted toward working-age and + older adults. + constraints: + - type: hard_min + value: 18.0 + expression: null + reason: Adults only by spec definition + - type: hard_max + value: 80.0 + expression: null + reason: Upper bound per spec definition +- name: gender + type: categorical + category: universal + description: Self-identified gender of the individual (Male, Female, Non-binary/Other). + scope: individual + correlation_rate: null + semantic_type: null + identity_type: gender_identity + display_format: null + sampling: + strategy: independent + distribution: + type: categorical + options: + - Male + - Female + - Non-binary/Other + weights: + - 0.49 + - 0.493 + - 0.017 + formula: null + depends_on: [] + modifiers: [] + grounding: + level: strong + method: researched + source: https://usafacts.org/articles/what-percentage-of-the-us-population-is-transgender/ + note: '2020 Census: 50.9% female, 49.1% male overall. USAFacts 2025 Household + Pulse data: ~1.70% identify as non-binary or other non-cisgender. Weights adjusted + slightly for the adult-only frame where male proportion is marginally higher + in younger cohorts but female is higher in older cohorts, netting near parity.' + constraints: + - type: spec_expression + value: null + expression: sum(weights)==1 + reason: Weights must sum to 1 + - type: spec_expression + value: null + expression: len(options)==3 + reason: Three gender categories required by spec +- name: race_ethnicity + type: categorical + category: universal + description: Racial and ethnic self-identification (White non-Hispanic, Black, Hispanic/Latino, + Asian, Other/Multiracial). + scope: partner_correlated + correlation_rate: null + semantic_type: null + identity_type: race_ethnicity + display_format: null + sampling: + strategy: independent + distribution: + type: categorical + options: + - White non-Hispanic + - Black + - Hispanic/Latino + - Asian + - Other/Multiracial + weights: + - 0.577 + - 0.121 + - 0.19 + - 0.059 + - 0.053 + formula: null + depends_on: [] + modifiers: [] + grounding: + level: strong + method: researched + source: https://www.pewresearch.org/race-and-ethnicity/2025/11/03/counting-race-how-the-census-measures-identity-and-what-americans-think-about-it/ + note: '2020 Census: White non-Hispanic 57.8%, Hispanic 18.7%, Black 12.1%, Asian + 5.9%, Two+ races 4.1%, other <1%. ''Other/Multiracial'' consolidates AIAN, NHPI, + Some Other Race, and Two or More Races. Slight downward adjustment on White + non-Hispanic to ~57.7% to reflect 2023–2024 trajectory per Census estimates.' + constraints: + - type: spec_expression + value: null + expression: sum(weights)==1 + reason: Weights must sum to 1 + - type: spec_expression + value: null + expression: len(options)==5 + reason: Five race/ethnicity categories per spec +- name: us_region + type: categorical + category: universal + description: US census region of residence (Northeast, Southeast, Midwest, Southwest, + West Coast). + scope: household + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: independent + distribution: + type: categorical + options: + - Northeast + - Southeast + - Midwest + - Southwest + - West Coast + weights: + - 0.175 + - 0.255 + - 0.205 + - 0.165 + - 0.2 + formula: null + depends_on: [] + modifiers: [] + grounding: + level: medium + method: extrapolated + source: https://www.census.gov/data/tables/time-series/demo/popest/2020s-national-detail.html + note: Census defines 4 regions (Northeast ~18%, Midwest ~21%, South ~38%, West + ~23%). Spec uses 5 regions splitting South into Southeast and Southwest (~60/40 + split of South), and West into Southwest and West Coast (~55/45 split). Northeast + ~17.5%, Southeast ~25.5% (SE portion of South), Midwest ~20.5%, Southwest ~16.5% + (SW portion of South + Mountain West), West Coast ~20% (Pacific states). + constraints: + - type: spec_expression + value: null + expression: sum(weights)==1 + reason: Weights must sum to 1 + - type: spec_expression + value: null + expression: len(options)==5 + reason: Five region categories per spec +- name: urban_rural + type: categorical + category: universal + description: Urbanicity of the household's location (Urban, Suburban, Rural). + scope: household + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: independent + distribution: + type: categorical + options: + - Urban + - Suburban + - Rural + weights: + - 0.235 + - 0.545 + - 0.22 + formula: null + depends_on: [] + modifiers: [] + grounding: + level: medium + method: extrapolated + source: https://www.pewresearch.org/social-trends/2018/05/22/demographic-and-economic-trends-in-urban-suburban-and-rural-communities/ + note: 'Pew Research found ~14% rural, with ~46M rural, ~175M suburban, ~98M urban + (2018). 2020 Census: 81% urban broadly defined, but this includes suburbs. Pew + self-report surveys consistently show ~26% urban, ~53% suburban, ~21% rural. + Using ~23.5% urban core, ~54.5% suburban, ~22% rural to reflect post-pandemic + dispersal trends.' + constraints: + - type: spec_expression + value: null + expression: sum(weights)==1 + reason: Weights must sum to 1 + - type: spec_expression + value: null + expression: len(options)==3 + reason: Three urbanicity categories per spec +- name: openness + type: float + category: personality + description: 'Big Five openness to experience: curiosity, creativity, and receptivity + to new ideas.' + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: independent + distribution: + type: normal + mean: 0.525 + std: 0.175 + min: 0.0 + max: 1.0 + mean_formula: null + std_formula: null + min_formula: null + max_formula: null + formula: null + depends_on: [] + modifiers: [] + grounding: + level: medium + method: extrapolated + source: Soto, C. J., & John, O. P. (2017). The next Big Five Inventory. Journal + of Research in Personality. + note: 'US Big Five norms (Soto et al. 2011, JPSP): Openness T-score mean ~50 (population + normed). On 0–1 scale, population mean ~0.525 with SD ~0.175. US adults score + slightly above midpoint on Openness. Distribution is approximately normal in + large community samples.' + constraints: + - type: hard_min + value: 0.0 + expression: null + reason: Normalized 0–1 scale lower bound + - type: hard_max + value: 1.0 + expression: null + reason: Normalized 0–1 scale upper bound +- name: conscientiousness + type: float + category: personality + description: 'Big Five conscientiousness: self-discipline, organization, and goal-directed + behavior.' + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: independent + distribution: + type: normal + mean: 0.565 + std: 0.165 + min: 0.0 + max: 1.0 + mean_formula: null + std_formula: null + min_formula: null + max_formula: null + formula: null + depends_on: [] + modifiers: [] + grounding: + level: medium + method: extrapolated + source: Soto, C. J., et al. (2011). Age differences in personality traits from + 10 to 65. Journal of Personality and Social Psychology. + note: US adults score above midpoint on Conscientiousness; it increases with age. + Population mean ~0.565 (above neutral), SD ~0.165. Slightly higher mean than + Openness reflecting American cultural emphasis on diligence. + constraints: + - type: hard_min + value: 0.0 + expression: null + reason: Normalized 0–1 scale lower bound + - type: hard_max + value: 1.0 + expression: null + reason: Normalized 0–1 scale upper bound +- name: extraversion + type: float + category: personality + description: 'Big Five extraversion: sociability, assertiveness, and positive affect + in social settings.' + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: independent + distribution: + type: normal + mean: 0.515 + std: 0.18 + min: 0.0 + max: 1.0 + mean_formula: null + std_formula: null + min_formula: null + max_formula: null + formula: null + depends_on: [] + modifiers: [] + grounding: + level: medium + method: extrapolated + source: 'Rentfrow, P. J., et al. (2013). Divided we stand: Three psychological + regions of the United States. Journal of Personality and Social Psychology.' + note: US adults are broadly extraverted relative to global norms, but the distribution + is approximately symmetric around midpoint. Rentfrow et al. found regional variation; + national mean ~0.515, SD ~0.18. + constraints: + - type: hard_min + value: 0.0 + expression: null + reason: Normalized 0–1 scale lower bound + - type: hard_max + value: 1.0 + expression: null + reason: Normalized 0–1 scale upper bound +- name: agreeableness + type: float + category: personality + description: 'Big Five agreeableness: cooperativeness, empathy, and tendency toward + social harmony.' + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: independent + distribution: + type: normal + mean: 0.56 + std: 0.16 + min: 0.0 + max: 1.0 + mean_formula: null + std_formula: null + min_formula: null + max_formula: null + formula: null + depends_on: [] + modifiers: [] + grounding: + level: medium + method: extrapolated + source: Soto, C. J., et al. (2011). Age differences in personality traits from + 10 to 65. Journal of Personality and Social Psychology. + note: Agreeableness is above midpoint in US population norms; increases with age. + Mean ~0.56, SD ~0.16. Women score slightly higher than men on average. + constraints: + - type: hard_min + value: 0.0 + expression: null + reason: Normalized 0–1 scale lower bound + - type: hard_max + value: 1.0 + expression: null + reason: Normalized 0–1 scale upper bound +- name: neuroticism + type: float + category: personality + description: 'Big Five neuroticism: emotional instability, anxiety, and susceptibility + to stress.' + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: independent + distribution: + type: normal + mean: 0.46 + std: 0.175 + min: 0.0 + max: 1.0 + mean_formula: null + std_formula: null + min_formula: null + max_formula: null + formula: null + depends_on: [] + modifiers: [] + grounding: + level: medium + method: extrapolated + source: Soto, C. J., et al. (2011). Age differences in personality traits from + 10 to 65. Journal of Personality and Social Psychology. + note: Neuroticism is below midpoint on average for US adults and declines with + age. Women score higher than men. Mean ~0.46, SD ~0.175. Post-pandemic era data + suggests slightly elevated population anxiety, consistent with Pew and Gallup + mental health tracking. + constraints: + - type: hard_min + value: 0.0 + expression: null + reason: Normalized 0–1 scale lower bound + - type: hard_max + value: 1.0 + expression: null + reason: Normalized 0–1 scale upper bound +- name: education_level + type: categorical + category: universal + description: Highest educational attainment (Less than HS, HS diploma/GED, Some + college, Bachelor's degree, Graduate degree). + scope: partner_correlated + correlation_rate: null + semantic_type: education + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Less than HS + - HS diploma/GED + - Some college + - Bachelor's degree + - Graduate degree + weights: + - 0.09 + - 0.27 + - 0.2 + - 0.24 + - 0.2 + formula: null + depends_on: + - age + modifiers: + - when: age < 25 + multiply: null + add: null + weight_overrides: + Less than HS: 0.08 + HS diploma/GED: 0.3 + Some college: 0.48 + Bachelor's degree: 0.12 + Graduate degree: 0.02 + probability_override: null + - when: age >= 25 and age < 35 + multiply: null + add: null + weight_overrides: + Less than HS: 0.08 + HS diploma/GED: 0.24 + Some college: 0.26 + Bachelor's degree: 0.28 + Graduate degree: 0.14 + probability_override: null + - when: age >= 35 and age < 50 + multiply: null + add: null + weight_overrides: + Less than HS: 0.09 + HS diploma/GED: 0.26 + Some college: 0.24 + Bachelor's degree: 0.25 + Graduate degree: 0.16 + probability_override: null + - when: age >= 50 and age < 65 + multiply: null + add: null + weight_overrides: + Less than HS: 0.1 + HS diploma/GED: 0.3 + Some college: 0.24 + Bachelor's degree: 0.22 + Graduate degree: 0.14 + probability_override: null + - when: age >= 65 + multiply: null + add: null + weight_overrides: + Less than HS: 0.13 + HS diploma/GED: 0.32 + Some college: 0.22 + Bachelor's degree: 0.2 + Graduate degree: 0.13 + probability_override: null + grounding: + level: strong + method: researched + source: US Census Bureau CPS 2024; educationdata.org 2025 + note: ~9% less than HS, ~27% HS diploma, ~20% some college/associate, ~24% bachelor's, + ~20% graduate. Ages 18-24 skew lower; modifiers will handle age dependency. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: household_income + type: int + category: universal + description: Annual household income in USD before taxes. + scope: household + correlation_rate: null + semantic_type: income + identity_type: null + display_format: currency + sampling: + strategy: conditional + distribution: + type: lognormal + mean: 83730.0 + std: 75000.0 + min: 0.0 + max: 800000.0 + mean_formula: null + std_formula: null + min_formula: null + max_formula: null + formula: null + depends_on: + - education_level + - us_region + - urban_rural + modifiers: + - when: education_level == 'Less than HS' + multiply: 0.48 + add: 0.0 + weight_overrides: null + probability_override: null + - when: education_level == 'HS diploma/GED' + multiply: 0.7 + add: 0.0 + weight_overrides: null + probability_override: null + - when: education_level == 'Some college' + multiply: 0.82 + add: 0.0 + weight_overrides: null + probability_override: null + - when: education_level == 'Bachelor\'s degree' + multiply: 1.25 + add: 0.0 + weight_overrides: null + probability_override: null + - when: education_level == 'Graduate degree' + multiply: 1.65 + add: 0.0 + weight_overrides: null + probability_override: null + - when: us_region == 'Northeast' + multiply: 1.12 + add: 0.0 + weight_overrides: null + probability_override: null + - when: us_region == 'West Coast' + multiply: 1.14 + add: 0.0 + weight_overrides: null + probability_override: null + - when: us_region == 'Southeast' + multiply: 0.9 + add: 0.0 + weight_overrides: null + probability_override: null + - when: us_region == 'Southwest' + multiply: 0.93 + add: 0.0 + weight_overrides: null + probability_override: null + - when: urban_rural == 'Urban' + multiply: 1.1 + add: 0.0 + weight_overrides: null + probability_override: null + - when: urban_rural == 'Rural' + multiply: 0.82 + add: 0.0 + weight_overrides: null + probability_override: null + grounding: + level: strong + method: researched + source: 'US Census Bureau Income in the United States: 2024 (P60-286); CNBC Sept + 2025' + note: Median household income $83,730 in 2024. Lognormal captures right-skewed + income distribution. Std set wide to capture full range from poverty to affluent. + constraints: + - type: hard_min + value: 0.0 + expression: null + reason: Income cannot be negative + - type: hard_max + value: 800000.0 + expression: null + reason: Caps extreme outliers; 99th percentile US household income approx $500-600k +- name: marital_status + type: categorical + category: universal + description: Current marital or partnership status (Married/partnered, Single, Divorced/separated, + Widowed). + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Married/partnered + - Single + - Divorced/separated + - Widowed + weights: + - 0.49 + - 0.31 + - 0.14 + - 0.06 + formula: null + depends_on: + - age + modifiers: + - when: age < 25 + multiply: null + add: null + weight_overrides: + Married/partnered: 0.12 + Single: 0.83 + Divorced/separated: 0.04 + Widowed: 0.01 + probability_override: null + - when: age >= 25 and age < 35 + multiply: null + add: null + weight_overrides: + Married/partnered: 0.46 + Single: 0.42 + Divorced/separated: 0.11 + Widowed: 0.01 + probability_override: null + - when: age >= 35 and age < 50 + multiply: null + add: null + weight_overrides: + Married/partnered: 0.57 + Single: 0.23 + Divorced/separated: 0.18 + Widowed: 0.02 + probability_override: null + - when: age >= 50 and age < 65 + multiply: null + add: null + weight_overrides: + Married/partnered: 0.6 + Single: 0.14 + Divorced/separated: 0.2 + Widowed: 0.06 + probability_override: null + - when: age >= 65 + multiply: null + add: null + weight_overrides: + Married/partnered: 0.55 + Single: 0.07 + Divorced/separated: 0.16 + Widowed: 0.22 + probability_override: null + grounding: + level: strong + method: researched + source: USAFacts 2024; Census Bureau marital status data 2024 + note: '47.1% of households headed by married couples in 2024; ~49% adults married/partnered + including cohabiting. Single/never-married ~31%, divorced/separated ~14%, widowed + ~6%. Age modifiers critical: young adults skew single, older skew widowed.' + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: household_size + type: int + category: universal + description: Total number of people living in the household. + scope: household + correlation_rate: null + semantic_type: null + identity_type: null + display_format: number + sampling: + strategy: conditional + distribution: + type: normal + mean: 2.53 + std: 1.4 + min: 1.0 + max: 10.0 + mean_formula: null + std_formula: null + min_formula: null + max_formula: null + formula: null + depends_on: + - marital_status + modifiers: + - when: marital_status == 'Married/partnered' + multiply: 1.35 + add: 0.0 + weight_overrides: null + probability_override: null + - when: marital_status == 'Single' + multiply: 0.68 + add: 0.0 + weight_overrides: null + probability_override: null + - when: marital_status == 'Divorced/separated' + multiply: 0.88 + add: 0.0 + weight_overrides: null + probability_override: null + - when: marital_status == 'Widowed' + multiply: 0.75 + add: 0.0 + weight_overrides: null + probability_override: null + grounding: + level: strong + method: researched + source: US Census Bureau ACS 2024; average household size ~2.53 + note: US average household size is 2.53. Married households average ~3.1, single-person + ~1.0. Marital status modifier will shift mean upward for married/partnered. + constraints: + - type: hard_min + value: 1.0 + expression: null + reason: Household must have at least 1 person (respondent) + - type: hard_max + value: 10.0 + expression: null + reason: Households of 10+ are extreme outliers (<0.1%) +- name: has_children + type: boolean + category: universal + description: Whether the individual has any children (under 18) living in the household. + scope: individual + correlation_rate: null + semantic_type: null + identity_type: parental_status + display_format: null + sampling: + strategy: conditional + distribution: + type: boolean + probability_true: 0.28 + formula: null + depends_on: + - age + - marital_status + modifiers: + - when: age < 25 and marital_status == 'Single' + multiply: null + add: null + weight_overrides: null + probability_override: 0.06 + - when: age >= 25 and age < 35 and marital_status == 'Married/partnered' + multiply: null + add: null + weight_overrides: null + probability_override: 0.52 + - when: age >= 35 and age < 50 and marital_status == 'Married/partnered' + multiply: null + add: null + weight_overrides: null + probability_override: 0.58 + - when: age >= 50 and marital_status == 'Married/partnered' + multiply: null + add: null + weight_overrides: null + probability_override: 0.1 + - when: age >= 35 and age < 50 and marital_status == 'Single' + multiply: null + add: null + weight_overrides: null + probability_override: 0.18 + - when: age >= 50 + multiply: null + add: null + weight_overrides: null + probability_override: 0.05 + grounding: + level: medium + method: researched + source: Census Bureau CPS 2024; ~28% of households have children under 18 + note: 'About 28% of all adults have children under 18 at home. Strong age dependency: + near-zero for 18-22, peaks at ~45% for ages 30-45, near-zero for 65+. Marital + status also a major modifier.' + constraints: + - type: expression + value: null + expression: not (has_children and age < 18) + reason: Under-18s cannot have children in household as adult respondents (floor + is 18) +- name: employment_sector + type: categorical + category: population_specific + description: Primary employment sector (Federal government, State/local government, + Private sector, Gig/contract, Self-employed, Retired, Unemployed, Not in labor + force). + scope: individual + correlation_rate: null + semantic_type: employment + identity_type: professional_identity + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Federal government + - State/local government + - Private sector + - Gig/contract + - Self-employed + - Retired + - Unemployed + - Not in labor force + weights: + - 0.02 + - 0.13 + - 0.52 + - 0.06 + - 0.07 + - 0.12 + - 0.03 + - 0.05 + formula: null + depends_on: + - age + - education_level + modifiers: + - when: age < 25 and education_level == 'Less than HS' + multiply: null + add: null + weight_overrides: + Federal government: 0.01 + State/local government: 0.03 + Private sector: 0.42 + Gig/contract: 0.15 + Self-employed: 0.04 + Retired: 0.0 + Unemployed: 0.15 + Not in labor force: 0.2 + probability_override: null + - when: age < 25 and education_level in ['HS diploma/GED', 'Some college'] + multiply: null + add: null + weight_overrides: + Federal government: 0.02 + State/local government: 0.04 + Private sector: 0.52 + Gig/contract: 0.14 + Self-employed: 0.04 + Retired: 0.0 + Unemployed: 0.1 + Not in labor force: 0.14 + probability_override: null + - when: age >= 25 and age < 45 and education_level in ['Less than HS', 'HS diploma/GED'] + multiply: null + add: null + weight_overrides: + Federal government: 0.02 + State/local government: 0.06 + Private sector: 0.56 + Gig/contract: 0.12 + Self-employed: 0.08 + Retired: 0.01 + Unemployed: 0.07 + Not in labor force: 0.08 + probability_override: null + - when: age >= 25 and age < 55 and education_level in ['Bachelor\'s degree', 'Graduate + degree'] + multiply: null + add: null + weight_overrides: + Federal government: 0.06 + State/local government: 0.13 + Private sector: 0.58 + Gig/contract: 0.05 + Self-employed: 0.1 + Retired: 0.01 + Unemployed: 0.03 + Not in labor force: 0.04 + probability_override: null + - when: age >= 55 and age < 65 + multiply: null + add: null + weight_overrides: + Federal government: 0.04 + State/local government: 0.1 + Private sector: 0.42 + Gig/contract: 0.06 + Self-employed: 0.12 + Retired: 0.16 + Unemployed: 0.04 + Not in labor force: 0.06 + probability_override: null + - when: age >= 65 + multiply: null + add: null + weight_overrides: + Federal government: 0.01 + State/local government: 0.03 + Private sector: 0.12 + Gig/contract: 0.04 + Self-employed: 0.08 + Retired: 0.6 + Unemployed: 0.02 + Not in labor force: 0.1 + probability_override: null + grounding: + level: strong + method: researched + source: BLS Employment and Wages 2024; BLS CPS 2024 + note: 'BLS 2024: federal ~3M, state ~4.8M, local ~14.6M employees; private ~132.6M. + Self-employed ~10% of employed. Retired and NILF weighted for full adult 18-80 + population. Age modifiers critical: retired share surges at 65+.' + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: job_security_perception + type: categorical + category: population_specific + description: Perceived stability of current employment (Very secure, Somewhat secure, + Somewhat insecure, Very insecure, N/A - not employed). + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Very secure + - Somewhat secure + - Somewhat insecure + - Very insecure + - N/A - not employed + weights: + - 0.3 + - 0.35 + - 0.13 + - 0.07 + - 0.15 + formula: null + depends_on: + - employment_sector + modifiers: + - when: employment_sector == 'Federal government' + multiply: null + add: null + weight_overrides: + Very secure: 0.38 + Somewhat secure: 0.35 + Somewhat insecure: 0.18 + Very insecure: 0.09 + N/A - not employed: 0.0 + probability_override: null + - when: employment_sector == 'State/local government' + multiply: null + add: null + weight_overrides: + Very secure: 0.42 + Somewhat secure: 0.38 + Somewhat insecure: 0.14 + Very insecure: 0.06 + N/A - not employed: 0.0 + probability_override: null + - when: employment_sector == 'Private sector' + multiply: null + add: null + weight_overrides: + Very secure: 0.28 + Somewhat secure: 0.42 + Somewhat insecure: 0.2 + Very insecure: 0.1 + N/A - not employed: 0.0 + probability_override: null + - when: employment_sector == 'Gig/contract' + multiply: null + add: null + weight_overrides: + Very secure: 0.08 + Somewhat secure: 0.27 + Somewhat insecure: 0.38 + Very insecure: 0.27 + N/A - not employed: 0.0 + probability_override: null + - when: employment_sector == 'Self-employed' + multiply: null + add: null + weight_overrides: + Very secure: 0.2 + Somewhat secure: 0.38 + Somewhat insecure: 0.28 + Very insecure: 0.14 + N/A - not employed: 0.0 + probability_override: null + - when: employment_sector == 'Retired' + multiply: null + add: null + weight_overrides: + Very secure: 0.0 + Somewhat secure: 0.0 + Somewhat insecure: 0.0 + Very insecure: 0.0 + N/A - not employed: 1.0 + probability_override: null + - when: employment_sector == 'Unemployed' + multiply: null + add: null + weight_overrides: + Very secure: 0.0 + Somewhat secure: 0.0 + Somewhat insecure: 0.0 + Very insecure: 0.0 + N/A - not employed: 1.0 + probability_override: null + - when: employment_sector == 'Not in labor force' + multiply: null + add: null + weight_overrides: + Very secure: 0.0 + Somewhat secure: 0.0 + Somewhat insecure: 0.0 + Very insecure: 0.0 + N/A - not employed: 1.0 + probability_override: null + grounding: + level: medium + method: extrapolated + source: Gallup Job Security surveys; Federal Reserve SHED 2024 + note: Majority of employed workers report feeling secure. ~20% report some insecurity. + N/A bucket covers retired, unemployed, NILF (~15-20% of adult population). + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: financial_margin + type: categorical + category: population_specific + description: Financial buffer status indicating paycheck-to-paycheck living (Comfortable + savings buffer, Modest buffer, Paycheck-to-paycheck, In debt/deficit). + scope: household + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Comfortable savings buffer + - Modest buffer + - Paycheck-to-paycheck + - In debt/deficit + weights: + - 0.25 + - 0.27 + - 0.33 + - 0.15 + formula: null + depends_on: + - household_income + modifiers: + - when: household_income < 35000 + multiply: null + add: null + weight_overrides: + Comfortable savings buffer: 0.04 + Modest buffer: 0.12 + Paycheck-to-paycheck: 0.44 + In debt/deficit: 0.4 + probability_override: null + - when: household_income >= 35000 and household_income < 60000 + multiply: null + add: null + weight_overrides: + Comfortable savings buffer: 0.1 + Modest buffer: 0.28 + Paycheck-to-paycheck: 0.42 + In debt/deficit: 0.2 + probability_override: null + - when: household_income >= 60000 and household_income < 100000 + multiply: null + add: null + weight_overrides: + Comfortable savings buffer: 0.22 + Modest buffer: 0.38 + Paycheck-to-paycheck: 0.3 + In debt/deficit: 0.1 + probability_override: null + - when: household_income >= 100000 and household_income < 150000 + multiply: null + add: null + weight_overrides: + Comfortable savings buffer: 0.4 + Modest buffer: 0.38 + Paycheck-to-paycheck: 0.17 + In debt/deficit: 0.05 + probability_override: null + - when: household_income >= 150000 + multiply: null + add: null + weight_overrides: + Comfortable savings buffer: 0.6 + Modest buffer: 0.28 + Paycheck-to-paycheck: 0.09 + In debt/deficit: 0.03 + probability_override: null + grounding: + level: strong + method: researched + source: Federal Reserve SHED 2024; multiple paycheck-to-paycheck surveys 2024 + note: Approximately 63-66% of Americans live paycheck-to-paycheck per various + 2024 surveys. Fed SHED shows ~28% skipped medical care due to cost. Comfortable + savers ~25%, modest buffer ~27%, P2P ~33%, deficit ~15%. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: investment_profile + type: categorical + category: population_specific + description: Investment and financial literacy profile (No investments, 401k/IRA + only, Diversified investor, Active trader, Crypto holder). + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - No investments + - 401k/IRA only + - Diversified investor + - Active trader + - Crypto holder + weights: + - 0.29 + - 0.38 + - 0.17 + - 0.08 + - 0.08 + formula: null + depends_on: + - household_income + - education_level + modifiers: + - when: household_income < 40000 and education_level in ['Less than HS', 'HS diploma/GED'] + multiply: null + add: null + weight_overrides: + No investments: 0.65 + 401k/IRA only: 0.22 + Diversified investor: 0.05 + Active trader: 0.03 + Crypto holder: 0.05 + probability_override: null + - when: household_income >= 40000 and household_income < 80000 and education_level + in ['Some college', 'HS diploma/GED'] + multiply: null + add: null + weight_overrides: + No investments: 0.3 + 401k/IRA only: 0.42 + Diversified investor: 0.12 + Active trader: 0.06 + Crypto holder: 0.1 + probability_override: null + - when: household_income >= 80000 and education_level == 'Bachelor\'s degree' + multiply: null + add: null + weight_overrides: + No investments: 0.1 + 401k/IRA only: 0.44 + Diversified investor: 0.3 + Active trader: 0.1 + Crypto holder: 0.06 + probability_override: null + - when: household_income >= 100000 and education_level == 'Graduate degree' + multiply: null + add: null + weight_overrides: + No investments: 0.05 + 401k/IRA only: 0.3 + Diversified investor: 0.45 + Active trader: 0.15 + Crypto holder: 0.05 + probability_override: null + - when: household_income >= 150000 + multiply: null + add: null + weight_overrides: + No investments: 0.03 + 401k/IRA only: 0.22 + Diversified investor: 0.5 + Active trader: 0.2 + Crypto holder: 0.05 + probability_override: null + grounding: + level: medium + method: extrapolated + source: Federal Reserve SHED 2024; Gallup Stock Market Ownership 2024 + note: ~55-58% of Americans own stocks (many via retirement accounts). ~29% have + no investments. Crypto ownership ~15-20% but as primary profile ~8%. Diversified + investor and active trader reflect smaller educated/higher-income segments. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: primary_info_source + type: categorical + category: population_specific + description: Primary source of news and information (Cable/broadcast TV news, Social + media, Online news sites, Podcasts, Local news, Print media, Avoids news). + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Cable/broadcast TV news + - Social media + - Online news sites + - Podcasts + - Local news + - Print media + - Avoids news + weights: + - 0.28 + - 0.25 + - 0.18 + - 0.08 + - 0.09 + - 0.04 + - 0.08 + formula: null + depends_on: + - age + - education_level + modifiers: + - when: age < 30 and education_level in ['Less than HS', 'HS diploma/GED', 'Some + college'] + multiply: null + add: null + weight_overrides: + Cable/broadcast TV news: 0.05 + Social media: 0.55 + Online news sites: 0.2 + Podcasts: 0.1 + Local news: 0.04 + Print media: 0.01 + Avoids news: 0.05 + probability_override: null + - when: age < 30 and education_level in ['Bachelor\'s degree', 'Graduate degree'] + multiply: null + add: null + weight_overrides: + Cable/broadcast TV news: 0.04 + Social media: 0.38 + Online news sites: 0.32 + Podcasts: 0.18 + Local news: 0.04 + Print media: 0.02 + Avoids news: 0.02 + probability_override: null + - when: age >= 30 and age < 50 and education_level in ['Less than HS', 'HS diploma/GED'] + multiply: null + add: null + weight_overrides: + Cable/broadcast TV news: 0.22 + Social media: 0.38 + Online news sites: 0.18 + Podcasts: 0.07 + Local news: 0.1 + Print media: 0.02 + Avoids news: 0.03 + probability_override: null + - when: age >= 30 and age < 50 and education_level in ['Bachelor\'s degree', 'Graduate + degree'] + multiply: null + add: null + weight_overrides: + Cable/broadcast TV news: 0.14 + Social media: 0.22 + Online news sites: 0.38 + Podcasts: 0.16 + Local news: 0.06 + Print media: 0.03 + Avoids news: 0.01 + probability_override: null + - when: age >= 50 and age < 65 and education_level in ['Less than HS', 'HS diploma/GED', + 'Some college'] + multiply: null + add: null + weight_overrides: + Cable/broadcast TV news: 0.42 + Social media: 0.22 + Online news sites: 0.18 + Podcasts: 0.06 + Local news: 0.09 + Print media: 0.02 + Avoids news: 0.01 + probability_override: null + - when: age >= 65 + multiply: null + add: null + weight_overrides: + Cable/broadcast TV news: 0.52 + Social media: 0.1 + Online news sites: 0.16 + Podcasts: 0.04 + Local news: 0.14 + Print media: 0.03 + Avoids news: 0.01 + probability_override: null + grounding: + level: medium + method: extrapolated + source: Reuters Institute Digital News Report 2024; Pew Research Media consumption + 2024 + note: 'TV still largest single source ~28%, social media surging esp. under 35 + ~25%, online news ~18%. Podcasts growing ~8%. Print declining ~4%. Age modifiers + essential: older adults heavily TV, younger heavily social media.' + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: social_media_behavior + type: categorical + category: population_specific + description: Social media engagement posture (Non-user, Passive consumer, Occasional + poster, Active poster/Influencer). + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Non-user + - Passive consumer + - Occasional poster + - Active poster/Influencer + weights: + - 0.16 + - 0.42 + - 0.28 + - 0.14 + formula: null + depends_on: + - age + - primary_info_source + modifiers: + - when: age < 30 and primary_info_source == 'Social media' + multiply: null + add: null + weight_overrides: + Non-user: 0.02 + Passive consumer: 0.25 + Occasional poster: 0.4 + Active poster/Influencer: 0.33 + probability_override: null + - when: age < 30 and primary_info_source != 'Social media' + multiply: null + add: null + weight_overrides: + Non-user: 0.06 + Passive consumer: 0.38 + Occasional poster: 0.38 + Active poster/Influencer: 0.18 + probability_override: null + - when: age >= 30 and age < 50 and primary_info_source == 'Social media' + multiply: null + add: null + weight_overrides: + Non-user: 0.04 + Passive consumer: 0.32 + Occasional poster: 0.42 + Active poster/Influencer: 0.22 + probability_override: null + - when: age >= 30 and age < 50 and primary_info_source != 'Social media' + multiply: null + add: null + weight_overrides: + Non-user: 0.14 + Passive consumer: 0.48 + Occasional poster: 0.28 + Active poster/Influencer: 0.1 + probability_override: null + - when: age >= 50 and primary_info_source == 'Social media' + multiply: null + add: null + weight_overrides: + Non-user: 0.06 + Passive consumer: 0.5 + Occasional poster: 0.34 + Active poster/Influencer: 0.1 + probability_override: null + - when: age >= 50 and primary_info_source != 'Social media' + multiply: null + add: null + weight_overrides: + Non-user: 0.35 + Passive consumer: 0.42 + Occasional poster: 0.18 + Active poster/Influencer: 0.05 + probability_override: null + grounding: + level: medium + method: extrapolated + source: Pew Research Social Media Use 2024; Reuters Digital News Report 2024 + note: ~84% of US adults use social media in some form. Most are passive consumers. + Active posters ~28-30% of users; influencers a small fraction. Non-users concentrated + among older adults 65+. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: tech_adoption_posture + type: categorical + category: population_specific + description: Attitude toward adopting new technology (Technophobe/Avoider, Late + majority adopter, Early majority adopter, Early adopter, Innovator). + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Technophobe/Avoider + - Late majority adopter + - Early majority adopter + - Early adopter + - Innovator + weights: + - 0.14 + - 0.26 + - 0.34 + - 0.18 + - 0.08 + formula: null + depends_on: + - age + - education_level + modifiers: + - when: age < 30 and education_level in ['Bachelor\'s degree', 'Graduate degree'] + multiply: null + add: null + weight_overrides: + Technophobe/Avoider: 0.02 + Late majority adopter: 0.08 + Early majority adopter: 0.28 + Early adopter: 0.4 + Innovator: 0.22 + probability_override: null + - when: age < 30 and education_level in ['Less than HS', 'HS diploma/GED', 'Some + college'] + multiply: null + add: null + weight_overrides: + Technophobe/Avoider: 0.04 + Late majority adopter: 0.16 + Early majority adopter: 0.38 + Early adopter: 0.32 + Innovator: 0.1 + probability_override: null + - when: age >= 30 and age < 50 and education_level in ['Bachelor\'s degree', 'Graduate + degree'] + multiply: null + add: null + weight_overrides: + Technophobe/Avoider: 0.04 + Late majority adopter: 0.16 + Early majority adopter: 0.34 + Early adopter: 0.32 + Innovator: 0.14 + probability_override: null + - when: age >= 30 and age < 50 and education_level in ['Less than HS', 'HS diploma/GED', + 'Some college'] + multiply: null + add: null + weight_overrides: + Technophobe/Avoider: 0.1 + Late majority adopter: 0.3 + Early majority adopter: 0.36 + Early adopter: 0.18 + Innovator: 0.06 + probability_override: null + - when: age >= 50 and age < 65 and education_level in ['Bachelor\'s degree', 'Graduate + degree'] + multiply: null + add: null + weight_overrides: + Technophobe/Avoider: 0.1 + Late majority adopter: 0.28 + Early majority adopter: 0.36 + Early adopter: 0.2 + Innovator: 0.06 + probability_override: null + - when: age >= 50 and education_level in ['Less than HS', 'HS diploma/GED', 'Some + college'] + multiply: null + add: null + weight_overrides: + Technophobe/Avoider: 0.26 + Late majority adopter: 0.38 + Early majority adopter: 0.24 + Early adopter: 0.09 + Innovator: 0.03 + probability_override: null + - when: age >= 65 + multiply: null + add: null + weight_overrides: + Technophobe/Avoider: 0.38 + Late majority adopter: 0.36 + Early majority adopter: 0.18 + Early adopter: 0.06 + Innovator: 0.02 + probability_override: null + grounding: + level: medium + method: extrapolated + source: Rogers Diffusion of Innovation model applied to US population; Pew Research + Tech Adoption 2024 + note: 'Loosely follows Rogers'' diffusion curve: innovators ~2.5%, early adopters + ~13.5%, early majority ~34%, late majority ~34%, laggards ~16%. Adjusted for + US digital economy context and 18-80 age range.' + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: religious_engagement + type: categorical + category: population_specific + description: Level of religious practice and belief (Secular/None, Nominally affiliated, + Moderately religious, Devout). + scope: partner_correlated + correlation_rate: null + semantic_type: null + identity_type: religious_affiliation + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Secular/None + - Nominally affiliated + - Moderately religious + - Devout + weights: + - 0.29 + - 0.25 + - 0.27 + - 0.19 + formula: null + depends_on: + - us_region + - race_ethnicity + modifiers: + - when: us_region == 'Southeast' and race_ethnicity == 'Black' + multiply: null + add: null + weight_overrides: + Secular/None: 0.06 + Nominally affiliated: 0.18 + Moderately religious: 0.34 + Devout: 0.42 + probability_override: null + - when: us_region == 'Southeast' and race_ethnicity == 'White non-Hispanic' + multiply: null + add: null + weight_overrides: + Secular/None: 0.18 + Nominally affiliated: 0.24 + Moderately religious: 0.3 + Devout: 0.28 + probability_override: null + - when: us_region == 'Southeast' and race_ethnicity == 'Hispanic/Latino' + multiply: null + add: null + weight_overrides: + Secular/None: 0.12 + Nominally affiliated: 0.22 + Moderately religious: 0.34 + Devout: 0.32 + probability_override: null + - when: us_region == 'Midwest' and race_ethnicity == 'White non-Hispanic' + multiply: null + add: null + weight_overrides: + Secular/None: 0.22 + Nominally affiliated: 0.28 + Moderately religious: 0.3 + Devout: 0.2 + probability_override: null + - when: us_region == 'Southwest' + multiply: null + add: null + weight_overrides: + Secular/None: 0.22 + Nominally affiliated: 0.26 + Moderately religious: 0.28 + Devout: 0.24 + probability_override: null + - when: us_region == 'West Coast' and race_ethnicity in ['White non-Hispanic', + 'Asian'] + multiply: null + add: null + weight_overrides: + Secular/None: 0.42 + Nominally affiliated: 0.3 + Moderately religious: 0.18 + Devout: 0.1 + probability_override: null + - when: us_region == 'Northeast' and race_ethnicity == 'White non-Hispanic' + multiply: null + add: null + weight_overrides: + Secular/None: 0.38 + Nominally affiliated: 0.32 + Moderately religious: 0.2 + Devout: 0.1 + probability_override: null + - when: race_ethnicity == 'Asian' + multiply: null + add: null + weight_overrides: + Secular/None: 0.34 + Nominally affiliated: 0.28 + Moderately religious: 0.24 + Devout: 0.14 + probability_override: null + grounding: + level: strong + method: researched + source: Pew Research Religious Landscape Study; Gallup Religion 2024 + note: ~29% identify as religiously unaffiliated (nones). ~25% nominally affiliated + but rarely practice. ~27% moderately religious (attend occasionally). ~19% devout + (weekly+ attendance). Southeast and Midwest regions skew more devout; Northeast/West + Coast more secular. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: political_identity + type: categorical + category: population_specific + description: Political party affiliation and ideological strength (Strong Democrat, + Lean Democrat, True Independent, Lean Republican, Strong Republican). + scope: partner_correlated + correlation_rate: null + semantic_type: null + identity_type: political_orientation + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Strong Democrat + - Lean Democrat + - True Independent + - Lean Republican + - Strong Republican + weights: + - 0.18 + - 0.18 + - 0.17 + - 0.2 + - 0.27 + formula: null + depends_on: + - us_region + - education_level + - race_ethnicity + modifiers: + - when: us_region == 'Northeast' and education_level in ['Bachelor\'s degree', + 'Graduate degree'] and race_ethnicity == 'White non-Hispanic' + multiply: null + add: null + weight_overrides: + Strong Democrat: 0.3 + Lean Democrat: 0.28 + True Independent: 0.18 + Lean Republican: 0.14 + Strong Republican: 0.1 + probability_override: null + - when: us_region == 'Southeast' and education_level in ['Less than HS', 'HS diploma/GED'] + and race_ethnicity == 'White non-Hispanic' + multiply: null + add: null + weight_overrides: + Strong Democrat: 0.08 + Lean Democrat: 0.1 + True Independent: 0.16 + Lean Republican: 0.24 + Strong Republican: 0.42 + probability_override: null + - when: us_region == 'Southeast' and race_ethnicity == 'Black' + multiply: null + add: null + weight_overrides: + Strong Democrat: 0.52 + Lean Democrat: 0.26 + True Independent: 0.12 + Lean Republican: 0.06 + Strong Republican: 0.04 + probability_override: null + - when: race_ethnicity == 'Hispanic/Latino' and us_region in ['Southwest', 'West + Coast'] + multiply: null + add: null + weight_overrides: + Strong Democrat: 0.28 + Lean Democrat: 0.28 + True Independent: 0.22 + Lean Republican: 0.13 + Strong Republican: 0.09 + probability_override: null + - when: race_ethnicity == 'Asian' + multiply: null + add: null + weight_overrides: + Strong Democrat: 0.28 + Lean Democrat: 0.28 + True Independent: 0.22 + Lean Republican: 0.13 + Strong Republican: 0.09 + probability_override: null + - when: us_region == 'West Coast' and education_level in ['Bachelor\'s degree', + 'Graduate degree'] + multiply: null + add: null + weight_overrides: + Strong Democrat: 0.34 + Lean Democrat: 0.26 + True Independent: 0.18 + Lean Republican: 0.12 + Strong Republican: 0.1 + probability_override: null + - when: us_region == 'Midwest' and education_level in ['Less than HS', 'HS diploma/GED'] + and race_ethnicity == 'White non-Hispanic' + multiply: null + add: null + weight_overrides: + Strong Democrat: 0.1 + Lean Democrat: 0.13 + True Independent: 0.18 + Lean Republican: 0.24 + Strong Republican: 0.35 + probability_override: null + - when: us_region == 'Southwest' and education_level in ['Less than HS', 'HS diploma/GED'] + and race_ethnicity == 'White non-Hispanic' + multiply: null + add: null + weight_overrides: + Strong Democrat: 0.08 + Lean Democrat: 0.11 + True Independent: 0.16 + Lean Republican: 0.26 + Strong Republican: 0.39 + probability_override: null + - when: education_level == 'Graduate degree' and race_ethnicity == 'White non-Hispanic' + multiply: null + add: null + weight_overrides: + Strong Democrat: 0.3 + Lean Democrat: 0.28 + True Independent: 0.18 + Lean Republican: 0.14 + Strong Republican: 0.1 + probability_override: null + grounding: + level: strong + method: researched + source: Gallup Party Affiliation 2024; Pew Research Partisan Identification 2024-2025 + note: '2024 Gallup: 46% R/lean-R, 45% D/lean-D, 43% pure independents. Strong + partisans ~28% each side per Pew. True independents (no lean) ~17%. Slight Republican + lean for base reflecting 2024 election environment. Modifiers adjust heavily + by region, education, race.' + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: political_engagement_level + type: categorical + category: population_specific + description: Level of political participation and interest (Disengaged, Single-issue + voter, Moderate/occasional participant, Highly engaged partisan). + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Disengaged + - Single-issue voter + - Moderate/occasional participant + - Highly engaged partisan + weights: + - 0.22 + - 0.18 + - 0.38 + - 0.22 + formula: null + depends_on: + - political_identity + - age + modifiers: + - when: political_identity in ['Strong Democrat', 'Strong Republican'] and age + >= 45 + multiply: null + add: null + weight_overrides: + Disengaged: 0.04 + Single-issue voter: 0.12 + Moderate/occasional participant: 0.24 + Highly engaged partisan: 0.6 + probability_override: null + - when: political_identity in ['Strong Democrat', 'Strong Republican'] and age + < 45 + multiply: null + add: null + weight_overrides: + Disengaged: 0.06 + Single-issue voter: 0.16 + Moderate/occasional participant: 0.3 + Highly engaged partisan: 0.48 + probability_override: null + - when: political_identity == 'True Independent' and age < 35 + multiply: null + add: null + weight_overrides: + Disengaged: 0.32 + Single-issue voter: 0.28 + Moderate/occasional participant: 0.3 + Highly engaged partisan: 0.1 + probability_override: null + - when: political_identity == 'True Independent' and age >= 35 + multiply: null + add: null + weight_overrides: + Disengaged: 0.2 + Single-issue voter: 0.3 + Moderate/occasional participant: 0.38 + Highly engaged partisan: 0.12 + probability_override: null + - when: political_identity in ['Lean Democrat', 'Lean Republican'] and age >= + 45 + multiply: null + add: null + weight_overrides: + Disengaged: 0.1 + Single-issue voter: 0.22 + Moderate/occasional participant: 0.44 + Highly engaged partisan: 0.24 + probability_override: null + - when: political_identity in ['Lean Democrat', 'Lean Republican'] and age < 45 + multiply: null + add: null + weight_overrides: + Disengaged: 0.18 + Single-issue voter: 0.26 + Moderate/occasional participant: 0.4 + Highly engaged partisan: 0.16 + probability_override: null + grounding: + level: medium + method: extrapolated + source: ANES 2024; Pew Research Political Engagement 2024 + note: About 22% are largely disengaged from politics, 18% single-issue focused. + The moderate/occasional majority ~38%. Highly engaged ~22%, consistent with + share who always vote in midterms and follow news closely. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: vote_2024_presidential + type: categorical + category: population_specific + description: Reported 2024 presidential vote choice (Trump, Harris, Third party, + Did not vote, Not eligible). + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Trump + - Harris + - Third party + - Did not vote + - Not eligible + weights: + - 0.31 + - 0.3 + - 0.02 + - 0.28 + - 0.09 + formula: null + depends_on: + - political_identity + - political_engagement_level + modifiers: + - when: political_identity == 'Strong Republican' and political_engagement_level + == 'Highly engaged partisan' + multiply: null + add: null + weight_overrides: + Trump: 0.9 + Harris: 0.02 + Third party: 0.03 + Did not vote: 0.05 + Not eligible: 0.0 + probability_override: null + - when: political_identity == 'Strong Democrat' and political_engagement_level + == 'Highly engaged partisan' + multiply: null + add: null + weight_overrides: + Trump: 0.02 + Harris: 0.9 + Third party: 0.04 + Did not vote: 0.04 + Not eligible: 0.0 + probability_override: null + - when: political_identity == 'Lean Republican' + multiply: null + add: null + weight_overrides: + Trump: 0.68 + Harris: 0.14 + Third party: 0.06 + Did not vote: 0.12 + Not eligible: 0.0 + probability_override: null + - when: political_identity == 'Lean Democrat' + multiply: null + add: null + weight_overrides: + Trump: 0.1 + Harris: 0.72 + Third party: 0.07 + Did not vote: 0.11 + Not eligible: 0.0 + probability_override: null + - when: political_identity == 'True Independent' and political_engagement_level + == 'Disengaged' + multiply: null + add: null + weight_overrides: + Trump: 0.16 + Harris: 0.16 + Third party: 0.1 + Did not vote: 0.52 + Not eligible: 0.06 + probability_override: null + - when: political_identity == 'True Independent' and political_engagement_level + != 'Disengaged' + multiply: null + add: null + weight_overrides: + Trump: 0.32 + Harris: 0.32 + Third party: 0.12 + Did not vote: 0.22 + Not eligible: 0.02 + probability_override: null + - when: political_identity == 'Strong Republican' and political_engagement_level + in ['Disengaged', 'Single-issue voter'] + multiply: null + add: null + weight_overrides: + Trump: 0.72 + Harris: 0.05 + Third party: 0.05 + Did not vote: 0.18 + Not eligible: 0.0 + probability_override: null + - when: political_identity == 'Strong Democrat' and political_engagement_level + in ['Disengaged', 'Single-issue voter'] + multiply: null + add: null + weight_overrides: + Trump: 0.04 + Harris: 0.72 + Third party: 0.06 + Did not vote: 0.18 + Not eligible: 0.0 + probability_override: null + grounding: + level: strong + method: researched + source: 2024 Presidential Election results; US Elections Project turnout data + note: Trump won 49.9% of popular vote, Harris 48.4% of votes cast. ~64% of eligible + voters voted. ~9% of adults are non-citizens/not eligible. Did-not-vote reflects + low turnout among disengaged. Political identity modifier will strongly predict + this. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: midterm_turnout_propensity + type: categorical + category: population_specific + description: Historical tendency to vote in midterm elections (Always votes, Sometimes + votes, Rarely votes, Never votes). + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Always votes + - Sometimes votes + - Rarely votes + - Never votes + weights: + - 0.35 + - 0.27 + - 0.2 + - 0.18 + formula: null + depends_on: + - political_engagement_level + - age + modifiers: + - when: political_engagement_level == 'Highly engaged partisan' and age >= 45 + multiply: null + add: null + weight_overrides: + Always votes: 0.72 + Sometimes votes: 0.2 + Rarely votes: 0.06 + Never votes: 0.02 + probability_override: null + - when: political_engagement_level == 'Highly engaged partisan' and age < 45 + multiply: null + add: null + weight_overrides: + Always votes: 0.52 + Sometimes votes: 0.3 + Rarely votes: 0.12 + Never votes: 0.06 + probability_override: null + - when: political_engagement_level == 'Moderate/occasional participant' and age + >= 45 + multiply: null + add: null + weight_overrides: + Always votes: 0.4 + Sometimes votes: 0.38 + Rarely votes: 0.16 + Never votes: 0.06 + probability_override: null + - when: political_engagement_level == 'Moderate/occasional participant' and age + < 45 + multiply: null + add: null + weight_overrides: + Always votes: 0.2 + Sometimes votes: 0.38 + Rarely votes: 0.28 + Never votes: 0.14 + probability_override: null + - when: political_engagement_level == 'Single-issue voter' + multiply: null + add: null + weight_overrides: + Always votes: 0.2 + Sometimes votes: 0.35 + Rarely votes: 0.3 + Never votes: 0.15 + probability_override: null + - when: political_engagement_level == 'Disengaged' and age < 35 + multiply: null + add: null + weight_overrides: + Always votes: 0.04 + Sometimes votes: 0.14 + Rarely votes: 0.32 + Never votes: 0.5 + probability_override: null + - when: political_engagement_level == 'Disengaged' and age >= 35 + multiply: null + add: null + weight_overrides: + Always votes: 0.08 + Sometimes votes: 0.22 + Rarely votes: 0.36 + Never votes: 0.34 + probability_override: null + grounding: + level: medium + method: extrapolated + source: US Elections Project midterm turnout data; Pew Research voter typology + 2024 + note: 'Midterm turnout ~50% of eligible voters (2022 ~46.8%). Always-voters ~35% + of adults, sometimes ~27%, rarely ~20%, never ~18%. Age modifier critical: older + adults much more consistent voters.' + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: congressional_district_competitiveness + type: categorical + category: population_specific + description: Competitiveness classification of the agent's congressional district + (Safe Republican, Lean Republican, Swing district, Lean Democrat, Safe Democrat). + scope: household + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Safe Republican + - Lean Republican + - Swing district + - Lean Democrat + - Safe Democrat + weights: + - 0.36 + - 0.12 + - 0.1 + - 0.12 + - 0.3 + formula: null + depends_on: + - us_region + - urban_rural + modifiers: + - when: us_region == 'Northeast' and urban_rural == 'Urban' + multiply: null + add: null + weight_overrides: + Safe Republican: 0.05 + Lean Republican: 0.08 + Swing district: 0.12 + Lean Democrat: 0.22 + Safe Democrat: 0.53 + probability_override: null + - when: us_region == 'Northeast' and urban_rural == 'Suburban' + multiply: null + add: null + weight_overrides: + Safe Republican: 0.1 + Lean Republican: 0.15 + Swing district: 0.28 + Lean Democrat: 0.28 + Safe Democrat: 0.19 + probability_override: null + - when: us_region == 'Northeast' and urban_rural == 'Rural' + multiply: null + add: null + weight_overrides: + Safe Republican: 0.4 + Lean Republican: 0.28 + Swing district: 0.18 + Lean Democrat: 0.09 + Safe Democrat: 0.05 + probability_override: null + - when: us_region == 'Southeast' and urban_rural == 'Urban' + multiply: null + add: null + weight_overrides: + Safe Republican: 0.1 + Lean Republican: 0.12 + Swing district: 0.18 + Lean Democrat: 0.28 + Safe Democrat: 0.32 + probability_override: null + - when: us_region == 'Southeast' and urban_rural == 'Suburban' + multiply: null + add: null + weight_overrides: + Safe Republican: 0.22 + Lean Republican: 0.28 + Swing district: 0.28 + Lean Democrat: 0.14 + Safe Democrat: 0.08 + probability_override: null + - when: us_region == 'Southeast' and urban_rural == 'Rural' + multiply: null + add: null + weight_overrides: + Safe Republican: 0.55 + Lean Republican: 0.26 + Swing district: 0.12 + Lean Democrat: 0.05 + Safe Democrat: 0.02 + probability_override: null + - when: us_region == 'Midwest' and urban_rural == 'Urban' + multiply: null + add: null + weight_overrides: + Safe Republican: 0.06 + Lean Republican: 0.1 + Swing district: 0.18 + Lean Democrat: 0.26 + Safe Democrat: 0.4 + probability_override: null + - when: us_region == 'Midwest' and urban_rural == 'Suburban' + multiply: null + add: null + weight_overrides: + Safe Republican: 0.2 + Lean Republican: 0.26 + Swing district: 0.32 + Lean Democrat: 0.14 + Safe Democrat: 0.08 + probability_override: null + - when: us_region == 'Midwest' and urban_rural == 'Rural' + multiply: null + add: null + weight_overrides: + Safe Republican: 0.54 + Lean Republican: 0.28 + Swing district: 0.12 + Lean Democrat: 0.04 + Safe Democrat: 0.02 + probability_override: null + - when: us_region == 'Southwest' and urban_rural == 'Urban' + multiply: null + add: null + weight_overrides: + Safe Republican: 0.1 + Lean Republican: 0.16 + Swing district: 0.26 + Lean Democrat: 0.26 + Safe Democrat: 0.22 + probability_override: null + - when: us_region == 'Southwest' and urban_rural == 'Suburban' + multiply: null + add: null + weight_overrides: + Safe Republican: 0.24 + Lean Republican: 0.28 + Swing district: 0.28 + Lean Democrat: 0.12 + Safe Democrat: 0.08 + probability_override: null + - when: us_region == 'Southwest' and urban_rural == 'Rural' + multiply: null + add: null + weight_overrides: + Safe Republican: 0.56 + Lean Republican: 0.24 + Swing district: 0.12 + Lean Democrat: 0.05 + Safe Democrat: 0.03 + probability_override: null + - when: us_region == 'West Coast' and urban_rural == 'Urban' + multiply: null + add: null + weight_overrides: + Safe Republican: 0.04 + Lean Republican: 0.06 + Swing district: 0.1 + Lean Democrat: 0.22 + Safe Democrat: 0.58 + probability_override: null + - when: us_region == 'West Coast' and urban_rural == 'Suburban' + multiply: null + add: null + weight_overrides: + Safe Republican: 0.1 + Lean Republican: 0.18 + Swing district: 0.26 + Lean Democrat: 0.26 + Safe Democrat: 0.2 + probability_override: null + - when: us_region == 'West Coast' and urban_rural == 'Rural' + multiply: null + add: null + weight_overrides: + Safe Republican: 0.44 + Lean Republican: 0.28 + Swing district: 0.16 + Lean Democrat: 0.08 + Safe Democrat: 0.04 + probability_override: null + grounding: + level: medium + method: extrapolated + source: Cook Political Report 2024 district ratings; DCCC/NRCC targeting lists + note: Approximately 35-40 truly competitive House districts out of 435 (~10%). + Safe R districts slightly outnumber safe D given geographic sorting. Rural/suburban + Republican districts vs. urban Democratic strongholds. Urban/rural modifier + will strongly adjust this. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: top_issue_2026 + type: categorical + category: population_specific + description: Single most salient policy issue heading into the 2026 midterms (Economy/Jobs, + Abortion/Reproductive rights, Immigration, Democracy/Rule of law, Healthcare, + Climate, Education, AI/Technology). + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Economy/Jobs + - Abortion/Reproductive rights + - Immigration + - Democracy/Rule of law + - Healthcare + - Climate + - Education + - AI/Technology + weights: + - 0.28 + - 0.13 + - 0.15 + - 0.12 + - 0.13 + - 0.07 + - 0.07 + - 0.05 + formula: null + depends_on: + - political_identity + - employment_sector + modifiers: + - when: political_identity == 'Strong Republican' and employment_sector in ['Private + sector', 'Self-employed', 'Gig/contract'] + multiply: null + add: null + weight_overrides: + Economy/Jobs: 0.38 + Abortion/Reproductive rights: 0.02 + Immigration: 0.28 + Democracy/Rule of law: 0.04 + Healthcare: 0.1 + Climate: 0.02 + Education: 0.06 + AI/Technology: 0.1 + probability_override: null + - when: political_identity == 'Strong Democrat' and employment_sector in ['State/local + government', 'Federal government'] + multiply: null + add: null + weight_overrides: + Economy/Jobs: 0.14 + Abortion/Reproductive rights: 0.22 + Immigration: 0.04 + Democracy/Rule of law: 0.28 + Healthcare: 0.16 + Climate: 0.1 + Education: 0.05 + AI/Technology: 0.01 + probability_override: null + - when: political_identity == 'Strong Democrat' and employment_sector in ['Private + sector', 'Self-employed'] + multiply: null + add: null + weight_overrides: + Economy/Jobs: 0.18 + Abortion/Reproductive rights: 0.2 + Immigration: 0.04 + Democracy/Rule of law: 0.24 + Healthcare: 0.16 + Climate: 0.1 + Education: 0.06 + AI/Technology: 0.02 + probability_override: null + - when: political_identity == 'True Independent' and employment_sector in ['Private + sector', 'Gig/contract', 'Unemployed'] + multiply: null + add: null + weight_overrides: + Economy/Jobs: 0.42 + Abortion/Reproductive rights: 0.08 + Immigration: 0.16 + Democracy/Rule of law: 0.12 + Healthcare: 0.12 + Climate: 0.04 + Education: 0.04 + AI/Technology: 0.02 + probability_override: null + - when: political_identity in ['Lean Republican', 'Strong Republican'] and employment_sector + == 'Retired' + multiply: null + add: null + weight_overrides: + Economy/Jobs: 0.26 + Abortion/Reproductive rights: 0.04 + Immigration: 0.26 + Democracy/Rule of law: 0.06 + Healthcare: 0.28 + Climate: 0.02 + Education: 0.06 + AI/Technology: 0.02 + probability_override: null + - when: political_identity in ['Strong Democrat', 'Lean Democrat'] and employment_sector + == 'Retired' + multiply: null + add: null + weight_overrides: + Economy/Jobs: 0.18 + Abortion/Reproductive rights: 0.14 + Immigration: 0.04 + Democracy/Rule of law: 0.22 + Healthcare: 0.3 + Climate: 0.06 + Education: 0.04 + AI/Technology: 0.02 + probability_override: null + - when: employment_sector in ['Federal government', 'State/local government'] + and political_identity in ['Lean Republican', 'Strong Republican'] + multiply: null + add: null + weight_overrides: + Economy/Jobs: 0.28 + Abortion/Reproductive rights: 0.04 + Immigration: 0.22 + Democracy/Rule of law: 0.14 + Healthcare: 0.16 + Climate: 0.04 + Education: 0.1 + AI/Technology: 0.02 + probability_override: null + grounding: + level: medium + method: extrapolated + source: Gallup Most Important Problem 2024-2025; Pew Research Issue Priorities + 2024 + note: Economy/jobs consistently tops issue salience polls ~25-30%. Immigration + elevated post-2024 election. Abortion remains salient post-Dobbs. Democracy/rule + of law elevated heading into 2026. AI/Tech emerging but still low single digits + as top issue. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: country_direction_approval + type: categorical + category: population_specific + description: Approval of the current direction of the United States (Strongly approve, + Somewhat approve, Somewhat disapprove, Strongly disapprove). + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Strongly approve + - Somewhat approve + - Somewhat disapprove + - Strongly disapprove + weights: + - 0.22 + - 0.18 + - 0.18 + - 0.42 + formula: null + depends_on: + - political_identity + - financial_margin + modifiers: + - when: political_identity == 'Strong Republican' and financial_margin in ['Comfortable + savings buffer', 'Modest buffer'] + multiply: null + add: null + weight_overrides: + Strongly approve: 0.52 + Somewhat approve: 0.3 + Somewhat disapprove: 0.12 + Strongly disapprove: 0.06 + probability_override: null + - when: political_identity == 'Strong Republican' and financial_margin in ['Paycheck-to-paycheck', + 'In debt/deficit'] + multiply: null + add: null + weight_overrides: + Strongly approve: 0.34 + Somewhat approve: 0.32 + Somewhat disapprove: 0.2 + Strongly disapprove: 0.14 + probability_override: null + - when: political_identity == 'Lean Republican' and financial_margin in ['Comfortable + savings buffer', 'Modest buffer'] + multiply: null + add: null + weight_overrides: + Strongly approve: 0.28 + Somewhat approve: 0.4 + Somewhat disapprove: 0.2 + Strongly disapprove: 0.12 + probability_override: null + - when: political_identity == 'True Independent' and financial_margin in ['Comfortable + savings buffer', 'Modest buffer'] + multiply: null + add: null + weight_overrides: + Strongly approve: 0.14 + Somewhat approve: 0.3 + Somewhat disapprove: 0.32 + Strongly disapprove: 0.24 + probability_override: null + - when: political_identity == 'True Independent' and financial_margin in ['Paycheck-to-paycheck', + 'In debt/deficit'] + multiply: null + add: null + weight_overrides: + Strongly approve: 0.08 + Somewhat approve: 0.22 + Somewhat disapprove: 0.34 + Strongly disapprove: 0.36 + probability_override: null + - when: political_identity == 'Lean Democrat' and financial_margin in ['Paycheck-to-paycheck', + 'In debt/deficit'] + multiply: null + add: null + weight_overrides: + Strongly approve: 0.03 + Somewhat approve: 0.1 + Somewhat disapprove: 0.28 + Strongly disapprove: 0.59 + probability_override: null + - when: political_identity == 'Strong Democrat' and financial_margin in ['Paycheck-to-paycheck', + 'In debt/deficit'] + multiply: null + add: null + weight_overrides: + Strongly approve: 0.02 + Somewhat approve: 0.05 + Somewhat disapprove: 0.18 + Strongly disapprove: 0.75 + probability_override: null + - when: political_identity == 'Strong Democrat' and financial_margin in ['Comfortable + savings buffer', 'Modest buffer'] + multiply: null + add: null + weight_overrides: + Strongly approve: 0.03 + Somewhat approve: 0.08 + Somewhat disapprove: 0.24 + Strongly disapprove: 0.65 + probability_override: null + grounding: + level: medium + method: extrapolated + source: Gallup Right/Wrong Direction poll 2025; RealClearPolitics average Feb + 2026 + note: As of early 2026, right-track/wrong-track polls show majority disapprove + of country direction (~55-60% wrong track). Strongly disapprove heavily driven + by Democrats/independents reacting to Trump second term. Political identity + modifier will strongly shift this. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: consumer_identity + type: categorical + category: population_specific + description: Primary driver of consumer purchasing behavior (Brand loyal, Price/value + driven, Values/ethics driven, Convenience driven). + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Brand loyal + - Price/value driven + - Values/ethics driven + - Convenience driven + weights: + - 0.22 + - 0.42 + - 0.18 + - 0.18 + formula: null + depends_on: + - household_income + - education_level + modifiers: + - when: household_income < 50000 and education_level in ['Less than HS', 'HS diploma/GED'] + multiply: null + add: null + weight_overrides: + Brand loyal: 0.18 + Price/value driven: 0.58 + Values/ethics driven: 0.1 + Convenience driven: 0.14 + probability_override: null + - when: household_income >= 50000 and household_income < 100000 and education_level + == 'Some college' + multiply: null + add: null + weight_overrides: + Brand loyal: 0.26 + Price/value driven: 0.4 + Values/ethics driven: 0.16 + Convenience driven: 0.18 + probability_override: null + - when: household_income >= 100000 and education_level in ['Bachelor\'s degree', + 'Graduate degree'] + multiply: null + add: null + weight_overrides: + Brand loyal: 0.28 + Price/value driven: 0.22 + Values/ethics driven: 0.32 + Convenience driven: 0.18 + probability_override: null + - when: household_income >= 150000 and education_level == 'Graduate degree' + multiply: null + add: null + weight_overrides: + Brand loyal: 0.3 + Price/value driven: 0.14 + Values/ethics driven: 0.38 + Convenience driven: 0.18 + probability_override: null + grounding: + level: medium + method: extrapolated + source: Nielsen Consumer Research 2024; McKinsey Consumer Pulse 2024 + note: Price/value is primary driver for plurality of Americans especially post-inflation + environment. Brand loyalty declining. Values-driven purchasing elevated among + higher-education, younger, liberal consumers. Convenience driven for time-pressed + households. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: institutional_trust + type: categorical + category: population_specific + description: General level of trust in major institutions such as government, media, + science, and corporations (High trust, Moderate trust, Low trust, Distrustful/Conspiratorial). + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - High trust + - Moderate trust + - Low trust + - Distrustful/Conspiratorial + weights: + - 0.14 + - 0.32 + - 0.34 + - 0.2 + formula: null + depends_on: + - political_identity + - education_level + modifiers: + - when: political_identity == 'Strong Democrat' and education_level in ['Bachelor\'s + degree', 'Graduate degree'] + multiply: null + add: null + weight_overrides: + High trust: 0.18 + Moderate trust: 0.42 + Low trust: 0.3 + Distrustful/Conspiratorial: 0.1 + probability_override: null + - when: political_identity == 'Strong Democrat' and education_level in ['Less + than HS', 'HS diploma/GED', 'Some college'] + multiply: null + add: null + weight_overrides: + High trust: 0.12 + Moderate trust: 0.36 + Low trust: 0.34 + Distrustful/Conspiratorial: 0.18 + probability_override: null + - when: political_identity == 'Strong Republican' and education_level in ['Less + than HS', 'HS diploma/GED', 'Some college'] + multiply: null + add: null + weight_overrides: + High trust: 0.06 + Moderate trust: 0.22 + Low trust: 0.36 + Distrustful/Conspiratorial: 0.36 + probability_override: null + - when: political_identity == 'Strong Republican' and education_level in ['Bachelor\'s + degree', 'Graduate degree'] + multiply: null + add: null + weight_overrides: + High trust: 0.1 + Moderate trust: 0.28 + Low trust: 0.38 + Distrustful/Conspiratorial: 0.24 + probability_override: null + - when: political_identity == 'True Independent' and education_level in ['Less + than HS', 'HS diploma/GED'] + multiply: null + add: null + weight_overrides: + High trust: 0.06 + Moderate trust: 0.26 + Low trust: 0.38 + Distrustful/Conspiratorial: 0.3 + probability_override: null + - when: political_identity == 'True Independent' and education_level in ['Bachelor\'s + degree', 'Graduate degree'] + multiply: null + add: null + weight_overrides: + High trust: 0.1 + Moderate trust: 0.38 + Low trust: 0.36 + Distrustful/Conspiratorial: 0.16 + probability_override: null + - when: political_identity in ['Lean Democrat'] and education_level in ['Bachelor\'s + degree', 'Graduate degree'] + multiply: null + add: null + weight_overrides: + High trust: 0.22 + Moderate trust: 0.46 + Low trust: 0.24 + Distrustful/Conspiratorial: 0.08 + probability_override: null + - when: political_identity in ['Lean Republican'] and education_level in ['Bachelor\'s + degree', 'Graduate degree'] + multiply: null + add: null + weight_overrides: + High trust: 0.14 + Moderate trust: 0.34 + Low trust: 0.36 + Distrustful/Conspiratorial: 0.16 + probability_override: null + grounding: + level: strong + method: researched + source: Gallup Confidence in Institutions 2024; Edelman Trust Barometer 2025 + note: 'Institutional trust at historic lows. Gallup 2024: <30% confident in most + major institutions. ~14% high trust, ~32% moderate, ~34% low trust, ~20% conspiratorial/distrustful + across the board. Political identity and education are strong modifiers.' + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1.0 +- name: risk_tolerance + type: float + category: personality + description: General willingness to accept risk in financial, social, and political + decisions (0=very risk averse, 1=very risk tolerant). + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: beta + alpha: 2.5 + beta: 3.0 + min: 0.0 + max: 1.0 + min_formula: null + max_formula: null + formula: null + depends_on: + - age + - household_income + modifiers: + - when: age < 35 and household_income >= 80000 + multiply: 1.2 + add: 0.06 + weight_overrides: null + probability_override: null + - when: age < 35 and household_income < 40000 + multiply: 0.95 + add: 0.02 + weight_overrides: null + probability_override: null + - when: age >= 55 and household_income >= 100000 + multiply: 0.9 + add: 0.0 + weight_overrides: null + probability_override: null + - when: age >= 55 and household_income < 50000 + multiply: 0.8 + add: -0.06 + weight_overrides: null + probability_override: null + - when: age >= 35 and age < 55 and household_income >= 100000 + multiply: 1.05 + add: 0.02 + weight_overrides: null + probability_override: null + grounding: + level: medium + method: extrapolated + source: FINRA Investor Education Foundation 2024; Federal Reserve SHED 2024 + note: 'Beta(2.5, 3.0) gives mean ~0.45, slightly below midpoint, reflecting general + risk aversion in US population. Right-skewed toward lower risk tolerance. Age + and income are key modifiers: younger and wealthier adults more risk-tolerant.' + constraints: + - type: hard_min + value: 0.0 + expression: null + reason: Risk tolerance bounded at 0 (fully risk-averse) + - type: hard_max + value: 1.0 + expression: null + reason: Risk tolerance bounded at 1 (fully risk-tolerant) +- name: economic_anxiety + type: float + category: personality + description: Degree of worry about personal and national economic conditions (0=none, + 1=extreme), shaping decision-making under uncertainty. + scope: individual + correlation_rate: null + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: beta + alpha: 3.5 + beta: 2.5 + min: 0.0 + max: 1.0 + min_formula: null + max_formula: null + formula: null + depends_on: + - financial_margin + - job_security_perception + modifiers: + - when: financial_margin == 'In debt/deficit' and job_security_perception in ['Very + insecure', 'Somewhat insecure'] + multiply: 1.0 + add: 0.22 + weight_overrides: null + probability_override: null + - when: financial_margin == 'In debt/deficit' and job_security_perception in ['N/A + - not employed', 'Somewhat secure'] + multiply: 1.0 + add: 0.16 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Paycheck-to-paycheck' and job_security_perception + in ['Somewhat insecure', 'Very insecure'] + multiply: 1.0 + add: 0.14 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Paycheck-to-paycheck' and job_security_perception + in ['Very secure', 'Somewhat secure'] + multiply: 1.0 + add: 0.08 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Modest buffer' and job_security_perception == 'N/A + - not employed' + multiply: 1.0 + add: 0.04 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Comfortable savings buffer' and job_security_perception + in ['Very secure', 'Somewhat secure'] + multiply: 1.0 + add: -0.16 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Comfortable savings buffer' and job_security_perception + == 'N/A - not employed' + multiply: 1.0 + add: -0.08 + weight_overrides: null + probability_override: null + grounding: + level: medium + method: extrapolated + source: Federal Reserve SHED 2024; Gallup Economic Confidence 2024-2025 + note: Beta(3.5, 2.5) gives mean ~0.58, above midpoint, reflecting elevated economic + anxiety in 2024-2026 environment with inflation, tariffs, and job market uncertainty. + Financial margin and job security are primary modifiers. + constraints: + - type: hard_min + value: 0.0 + expression: null + reason: Economic anxiety bounded at 0 (no anxiety) + - type: hard_max + value: 1.0 + expression: null + reason: Economic anxiety bounded at 1 (extreme anxiety) +sampling_order: +- age +- agreeableness +- conscientiousness +- country +- education_level +- employment_sector +- extraversion +- gender +- job_security_perception +- marital_status +- has_children +- household_size +- neuroticism +- openness +- primary_info_source +- race_ethnicity +- social_media_behavior +- tech_adoption_posture +- urban_rural +- us_region +- congressional_district_competitiveness +- household_income +- consumer_identity +- financial_margin +- economic_anxiety +- investment_profile +- political_identity +- country_direction_approval +- institutional_trust +- political_engagement_level +- midterm_turnout_propensity +- religious_engagement +- risk_tolerance +- top_issue_2026 +- vote_2024_presidential \ No newline at end of file diff --git a/examples/study-02/scenario/asi-announcement/persona.v1.yaml b/examples/study-02/scenario/asi-announcement/persona.v1.yaml new file mode 100644 index 0000000..110c076 --- /dev/null +++ b/examples/study-02/scenario/asi-announcement/persona.v1.yaml @@ -0,0 +1,881 @@ +population_description: 'Nationally representative US adult population (18-80). Must + capture the demographic, economic, and attitudinal fault lines that drive divergent + responses to major national events — especially technology disruption, economic + shocks, cultural controversies, and electoral behavior in the 2026 midterm elections. + + + Beyond standard demographics (age, gender, race/ethnicity, education, income, geography), + prioritize attributes that determine how people process and react to news, make + decisions under uncertainty, and vote: + + + - technology adoption posture and digital literacy (technophobe to early adopter) + + - media ecosystem and primary information sources (cable news, social media, podcasts, + local news, none) + + - institutional trust (government, corporations, media, science, financial system) + + - financial margin and economic anxiety (savings buffer, debt load, paycheck-to-paycheck + status) + + - employment sector and job security perception (federal/state government, private + sector, gig, self-employed, retired) + + - consumer identity and brand relationship (brand loyal, price-driven, values-driven) + + - social media behavior and influence (passive consumer, active poster, influencer, + non-user) + + - investment and financial literacy (no investments, 401k only, active trader, crypto + holder) + + - religious engagement and worldview (secular, moderate, devout, denomination) + + - political identity and engagement level (strong partisan, moderate, disengaged, + single-issue) + + - 2024 presidential vote (Trump, Harris, third party, didn’t vote, not eligible) + + - congressional district competitiveness (safe R, safe D, swing district) + + - midterm turnout propensity (always, sometimes, rarely, never) + + - issue salience for 2026 (economy/jobs, abortion/reproductive rights, immigration, + democracy/rule of law, healthcare, climate, education, AI/technology) + + - approval of current direction (strongly approve to strongly disapprove) + + + Geographic distribution should span urban/suburban/rural across major US regions + (Northeast, Southeast, Midwest, Southwest, West Coast), with realistic correlation + structure between attributes.' +created_at: '2026-02-20T03:41:31.767751' +intro_template: I'm a {age}-year-old {gender} living in a {urban_rural} area of the + {us_region}, where I work in the {employment_sector} sector and bring home around + {household_income} a year. My household of {household_size} reflects who I am — + {race_ethnicity}, {marital_status}, with a {financial_margin} financial situation + that shapes how I see just about everything. When it comes to the big questions + facing the country — from AI to the economy to the next election — I come at it + as someone who {political_identity} and gets most of my news and information from + {primary_info_source}. +treatments: +- attribute: country + treatment: concrete + group: demographics +- attribute: age + treatment: concrete + group: demographics +- attribute: gender + treatment: concrete + group: demographics +- attribute: race_ethnicity + treatment: concrete + group: demographics +- attribute: us_region + treatment: concrete + group: geography +- attribute: urban_rural + treatment: concrete + group: geography +- attribute: openness + treatment: relative + group: personality +- attribute: conscientiousness + treatment: relative + group: personality +- attribute: extraversion + treatment: relative + group: personality +- attribute: agreeableness + treatment: relative + group: personality +- attribute: neuroticism + treatment: relative + group: personality +- attribute: education_level + treatment: concrete + group: demographics +- attribute: household_income + treatment: concrete + group: economics +- attribute: marital_status + treatment: concrete + group: demographics +- attribute: household_size + treatment: concrete + group: demographics +- attribute: has_children + treatment: relative + group: demographics +- attribute: employment_sector + treatment: concrete + group: economics +- attribute: job_security_perception + treatment: relative + group: economics +- attribute: financial_margin + treatment: concrete + group: economics +- attribute: investment_profile + treatment: concrete + group: economics +- attribute: primary_info_source + treatment: concrete + group: media_and_information +- attribute: social_media_behavior + treatment: concrete + group: media_and_information +- attribute: tech_adoption_posture + treatment: relative + group: media_and_information +- attribute: religious_engagement + treatment: concrete + group: values_and_identity +- attribute: political_identity + treatment: concrete + group: political_profile +- attribute: political_engagement_level + treatment: relative + group: political_profile +- attribute: vote_2024_presidential + treatment: concrete + group: political_profile +- attribute: midterm_turnout_propensity + treatment: concrete + group: political_profile +- attribute: congressional_district_competitiveness + treatment: concrete + group: political_profile +- attribute: top_issue_2026 + treatment: concrete + group: political_profile +- attribute: country_direction_approval + treatment: concrete + group: political_profile +- attribute: consumer_identity + treatment: concrete + group: values_and_identity +- attribute: institutional_trust + treatment: relative + group: values_and_identity +- attribute: risk_tolerance + treatment: relative + group: personality +- attribute: economic_anxiety + treatment: relative + group: economics +- attribute: personal_health_stake + treatment: concrete + group: asi_and_technology +- attribute: asi_threat_perception + treatment: concrete + group: asi_and_technology +- attribute: job_displacement_vulnerability + treatment: relative + group: asi_and_technology +- attribute: asi_adoption_intent + treatment: concrete + group: asi_and_technology +- attribute: asi_governance_stance + treatment: concrete + group: asi_and_technology +- attribute: economic_abundance_belief + treatment: relative + group: asi_and_technology +- attribute: information_verification_behavior + treatment: concrete + group: media_and_information +- attribute: human_purpose_anxiety + treatment: relative + group: asi_and_technology +- attribute: redistribution_support + treatment: concrete + group: political_profile +- attribute: geopolitical_competition_concern + treatment: concrete + group: asi_and_technology +- attribute: asi_credibility_assessment + treatment: concrete + group: asi_and_technology +groups: +- name: demographics + label: Who I Am + attributes: + - country + - age + - gender + - race_ethnicity + - education_level + - marital_status + - household_size + - has_children +- name: geography + label: Where I Live + attributes: + - us_region + - urban_rural +- name: economics + label: My Financial Life + attributes: + - household_income + - employment_sector + - job_security_perception + - financial_margin + - investment_profile + - economic_anxiety +- name: personality + label: How I Think & Feel + attributes: + - openness + - conscientiousness + - extraversion + - agreeableness + - neuroticism + - risk_tolerance +- name: values_and_identity + label: My Values & Identity + attributes: + - religious_engagement + - consumer_identity + - institutional_trust +- name: media_and_information + label: How I Get & Process Information + attributes: + - primary_info_source + - social_media_behavior + - tech_adoption_posture + - information_verification_behavior +- name: political_profile + label: My Political World + attributes: + - political_identity + - political_engagement_level + - vote_2024_presidential + - midterm_turnout_propensity + - congressional_district_competitiveness + - top_issue_2026 + - country_direction_approval + - redistribution_support +- name: asi_and_technology + label: My View on AI & the Future + attributes: + - personal_health_stake + - asi_threat_perception + - job_displacement_vulnerability + - asi_adoption_intent + - asi_governance_stance + - economic_abundance_belief + - human_purpose_anxiety + - geopolitical_competition_concern + - asi_credibility_assessment +phrasings: + boolean: + - attribute: has_children + true_phrase: I have at least one child under 18 living in my household. + false_phrase: I don't have any children under 18 living in my household. + categorical: + - attribute: country + phrases: + United States: I live in the United States + null_options: [] + null_phrase: null + fallback: null + - attribute: gender + phrases: + Male: I identify as male + Female: I identify as female + Non-binary/Other: I identify outside the traditional male/female categories + null_options: [] + null_phrase: null + fallback: null + - attribute: race_ethnicity + phrases: + White non-Hispanic: I identify as White, non-Hispanic + Black: I identify as Black or African American + Hispanic/Latino: I identify as Hispanic or Latino + Asian: I identify as Asian or Asian American + Other/Multiracial: I identify as multiracial or with a background that doesn't + fit neatly into standard categories + null_options: [] + null_phrase: null + fallback: null + - attribute: us_region + phrases: + Northeast: I live in the Northeast part of the country + Southeast: I live in the Southeast + Midwest: I'm based in the Midwest + Southwest: I live in the Southwest + West Coast: I'm on the West Coast + null_options: [] + null_phrase: null + fallback: null + - attribute: urban_rural + phrases: + Urban: I live in a city + Suburban: I live in the suburbs + Rural: I live in a rural area, away from major cities + null_options: [] + null_phrase: null + fallback: null + - attribute: education_level + phrases: + Less than HS: I didn't finish high school + HS diploma/GED: I graduated high school or earned my GED + Some college: I attended some college but didn't finish a degree + Bachelor's degree: I have a four-year college degree + Graduate degree: I have a graduate or professional degree beyond a bachelor's + null_options: [] + null_phrase: null + fallback: null + - attribute: marital_status + phrases: + Married/partnered: I'm married or in a committed partnership + Single: I'm currently single and not in a long-term relationship + Divorced/separated: I'm divorced or separated + Widowed: I've lost my spouse or partner + null_options: [] + null_phrase: null + fallback: null + - attribute: employment_sector + phrases: + Federal government: I work for the federal government + State/local government: I work for a state or local government agency + Private sector: I work for a private company or corporation + Gig/contract: I do gig work or contract jobs — there's no single employer + Self-employed: I'm self-employed and run my own business + Retired: I'm retired and no longer working + Unemployed: I'm currently out of work and looking for a job + Not in labor force: I'm not currently working or looking for work — I'm a caregiver, + student, or otherwise out of the workforce + null_options: + - Retired + - Unemployed + - Not in labor force + null_phrase: I don't currently have a traditional job or employer + fallback: null + - attribute: job_security_perception + phrases: + Very secure: I feel very secure in my job — I'm not worried about losing it + Somewhat secure: My job feels mostly stable, though nothing's guaranteed + Somewhat insecure: I have some real concerns about my job security + Very insecure: I'm genuinely worried I could lose my job soon + N/A - not employed: Job security isn't something I think about — I'm not in + the workforce right now + null_options: + - N/A - not employed + null_phrase: Job security isn't something I think about — I'm not in the workforce + right now + fallback: null + - attribute: financial_margin + phrases: + Comfortable savings buffer: I have a solid savings cushion and feel financially + stable + Modest buffer: I have some savings, but not a lot of room for unexpected expenses + Paycheck-to-paycheck: I'm basically living paycheck to paycheck with little + left over + In debt/deficit: I'm carrying more debt than I can comfortably manage — I'm + in the red + null_options: [] + null_phrase: null + fallback: null + - attribute: investment_profile + phrases: + No investments: I don't have any investments or market accounts + 401k/IRA only: My only investments are a retirement account like a 401k or IRA + Diversified investor: I have a diversified portfolio across stocks, bonds, and + other assets beyond just retirement accounts + Active trader: I actively trade stocks or other securities on a regular basis + Crypto holder: I hold cryptocurrency as part of my financial strategy + null_options: + - No investments + null_phrase: I don't have any investments or market accounts + fallback: null + - attribute: primary_info_source + phrases: + Cable/broadcast TV news: I get most of my news from cable or broadcast television + Social media: Social media is where I mostly find out what's going on in the + world + Online news sites: I mainly follow the news through websites and digital news + outlets + Podcasts: Podcasts are my main way of staying informed + Local news: I rely mainly on local TV, radio, or newspapers for my news + Print media: I still primarily read print newspapers or magazines to stay informed + Avoids news: Honestly, I try to tune out the news as much as possible + null_options: + - Avoids news + null_phrase: Honestly, I try to tune out the news as much as possible + fallback: null + - attribute: social_media_behavior + phrases: + Non-user: I don't use social media at all + Passive consumer: I scroll through social media but rarely post or comment + Occasional poster: I post on social media sometimes, but I'm not super active + Active poster/Influencer: I post regularly and engage heavily on social media + — building or maintaining an audience is part of how I operate + null_options: + - Non-user + null_phrase: I don't use social media at all + fallback: null + - attribute: tech_adoption_posture + phrases: + Technophobe/Avoider: I actively avoid new technology whenever I can — it's not + for me + Late majority adopter: I wait until a technology is well-established and widely + used before I bother with it + Early majority adopter: Once I see that something works and people around me + are using it, I'm willing to try it + Early adopter: I tend to get into new technology pretty early — I like staying + ahead of the curve + Innovator: I'm usually among the very first to try cutting-edge technology — + I love being at the frontier + null_options: [] + null_phrase: null + fallback: null + - attribute: religious_engagement + phrases: + Secular/None: I don't practice any religion and don't consider myself religious + Nominally affiliated: I loosely identify with a religion but don't really practice + it + Moderately religious: Faith plays a meaningful role in my life, though I'm not + strictly observant + Devout: My faith is central to who I am and shapes how I live my life + null_options: + - Secular/None + null_phrase: I don't practice any religion and don't consider myself religious + fallback: null + - attribute: political_identity + phrases: + Strong Democrat: I'm a committed Democrat — I strongly identify with the party + and its values + Lean Democrat: I generally lean Democratic, though I don't think of myself as + a hardcore partisan + True Independent: I'm genuinely independent — I don't align with either party + Lean Republican: I tend to lean Republican, though I'm not a die-hard party + loyalist + Strong Republican: I'm a committed Republican — I strongly identify with the + party and its values + null_options: [] + null_phrase: null + fallback: null + - attribute: political_engagement_level + phrases: + Disengaged: I really don't follow politics closely — it feels distant from my + daily life + Single-issue voter: There's one issue that drives almost all of my political + engagement + Moderate/occasional participant: I pay attention to politics and vote, but I'm + not deeply involved beyond that + Highly engaged partisan: I'm very politically active — I follow politics closely + and do more than just vote + null_options: + - Disengaged + null_phrase: I really don't follow politics closely — it feels distant from my + daily life + fallback: null + - attribute: vote_2024_presidential + phrases: + Trump: I voted for Donald Trump in the 2024 presidential election + Harris: I voted for Kamala Harris in the 2024 presidential election + Third party: I voted for a third-party candidate in the 2024 presidential race + Did not vote: I didn't vote in the 2024 presidential election + Not eligible: I wasn't eligible to vote in the 2024 presidential election + null_options: + - Did not vote + - Not eligible + null_phrase: I didn't cast a ballot in the 2024 presidential race + fallback: null + - attribute: midterm_turnout_propensity + phrases: + Always votes: I vote in every midterm election — it's something I always do + Sometimes votes: I vote in midterms sometimes, depending on how much I care + about what's on the ballot + Rarely votes: I rarely vote in midterm elections — it doesn't usually feel worth + it + Never votes: I don't vote in midterm elections + null_options: [] + null_phrase: null + fallback: null + - attribute: congressional_district_competitiveness + phrases: + Safe Republican: My congressional district is solidly Republican — there's no + real competition + Lean Republican: My district tilts Republican, but it's not a total lock + Swing district: I live in a genuine swing district where either party could + win + Lean Democrat: My district tends to go Democratic, though it's not completely + safe + Safe Democrat: My congressional district is solidly Democratic — the outcome + is pretty predictable + null_options: [] + null_phrase: null + fallback: null + - attribute: top_issue_2026 + phrases: + Economy/Jobs: The economy and jobs are the issues I care most about going into + 2026 + Abortion/Reproductive rights: Abortion and reproductive rights are my top concern + for the 2026 elections + Immigration: Immigration is the issue driving my vote more than anything else + in 2026 + Democracy/Rule of law: Protecting democracy and the rule of law is my single + biggest priority for 2026 + Healthcare: Healthcare — its cost and access — is what I care about most heading + into 2026 + Climate: Climate change is the issue I'm most focused on for the 2026 elections + Education: Education policy is the issue I care most about as we head into 2026 + AI/Technology: The regulation and impact of AI and new technology is my top + concern for 2026 + null_options: [] + null_phrase: null + fallback: null + - attribute: country_direction_approval + phrases: + Strongly approve: I strongly approve of the direction the country is headed + right now + Somewhat approve: I think things are generally moving in the right direction, + even if not perfectly + Somewhat disapprove: I'm not happy with the direction the country is going, + though it's not all bad + Strongly disapprove: I strongly disapprove of the direction this country is + headed — things are going very wrong + null_options: [] + null_phrase: null + fallback: null + - attribute: consumer_identity + phrases: + Brand loyal: I tend to stick with the brands I trust — I'm not constantly switching + Price/value driven: Price and value for money are what drive my buying decisions + more than anything else + Values/ethics driven: I try to buy from companies whose ethics and values align + with mine, even if it costs more + Convenience driven: Convenience is king for me — I buy whatever is easiest and + fastest + null_options: [] + null_phrase: null + fallback: null + - attribute: institutional_trust + phrases: + High trust: I generally trust major institutions — government, media, science, + and corporations — to act in good faith + Moderate trust: I have moderate trust in institutions — I believe in them but + know they're flawed + Low trust: I have pretty low trust in most major institutions — I think they + frequently let people down + Distrustful/Conspiratorial: I deeply distrust most major institutions and believe + a lot of what we're told is manipulated or hidden from us + null_options: [] + null_phrase: null + fallback: null + - attribute: personal_health_stake + phrases: + No personal stake: I don't have a particular personal or family health reason + to care about medical breakthroughs + Mild interest: I'm generally interested in medical advances, though nothing + urgent personally drives that interest + Significant - managing chronic illness: I or someone close to me is managing + a serious chronic illness, so medical breakthroughs matter a lot to us + Critical - terminal or degenerative diagnosis: I or a family member has a terminal + or degenerative diagnosis — medical breakthroughs could be genuinely life-or-death + for us + null_options: + - No personal stake + null_phrase: I don't have a particular personal or family health reason to care + about medical breakthroughs + fallback: null + - attribute: asi_threat_perception + phrases: + Existential threat: I see advanced AI as an existential threat to humanity — + this could go very badly for all of us + Serious risk/concern: I think advanced AI poses serious risks and society needs + to take those dangers much more seriously + Cautiously optimistic: I'm cautiously optimistic about advanced AI — I see real + benefits, but also real risks that need managing + Enthusiastically positive: I'm excited about what advanced AI can do — I think + the benefits far outweigh the risks + Indifferent/uncertain: Honestly, I'm not sure what to think about advanced AI + — I haven't formed a strong view either way + null_options: + - Indifferent/uncertain + null_phrase: Honestly, I'm not sure what to think about advanced AI — I haven't + formed a strong view either way + fallback: null + - attribute: job_displacement_vulnerability + phrases: + High - core tasks fully automatable: Most of what I do for work could realistically + be automated by AI — I'm in a high-risk position + Moderate - partial displacement likely: Some of my work could be taken over + by AI, though I don't think I'd be fully replaced + Low - physical/relational job: My work is physical or deeply relational in ways + that are hard to automate — I feel fairly insulated from AI displacement + Already displaced: AI or automation has already disrupted my career or cost + me work + Not employed: I'm not currently employed, so job displacement from AI isn't + something I'm facing right now + null_options: + - Not employed + null_phrase: I'm not currently employed, so job displacement from AI isn't something + I'm facing right now + fallback: null + - attribute: asi_adoption_intent + phrases: + Already using heavily: I'm already using advanced AI tools heavily in my daily + life or work + Planning to adopt soon: I plan to start integrating AI tools into my life or + work pretty soon + Curious but hesitant: I'm curious about AI tools but haven't committed to using + them — I'm still on the fence + Resistant but may be forced: I'm resistant to using AI tools, but I can see + that I might not have a choice for long + Actively refusing: I'm actively choosing not to use AI tools and intend to keep + it that way + null_options: [] + null_phrase: null + fallback: null + - attribute: asi_governance_stance + phrases: + No regulation - let it run: I think AI should be left alone to develop without + government interference + Light-touch industry self-regulation: I prefer the industry regulate itself + with minimal government involvement + Moderate government oversight: I support reasonable government oversight of + AI to manage risks without strangling innovation + Heavy international regulation: I think AI needs strong international regulation + and coordination to prevent catastrophic misuse + Halt/moratorium until safe: I believe development of the most powerful AI systems + should be paused until we can prove it's safe + null_options: [] + null_phrase: null + fallback: null + - attribute: information_verification_behavior + phrases: + Accepts information uncritically: I generally take information at face value + without digging into whether it's accurate + Relies on trusted outlets: I stick to a handful of sources I trust and don't + feel the need to verify beyond that + Cross-references multiple sources: When something seems important, I check it + against multiple sources before I accept it as true + Uses ASI tools to fact-check: I actually use AI tools to help me verify and + fact-check information I come across + Deep skeptic - trusts very little: I'm skeptical of almost everything I read + or hear — I trust very little of what's out there + null_options: [] + null_phrase: null + fallback: null + - attribute: redistribution_support + phrases: + Strongly supports aggressive redistribution: I strongly support bold redistribution + policies — things like UBI, wealth taxes, or public ownership of AI — to make + sure everyone benefits + Supports moderate redistribution: I support some redistribution to share the + gains from AI more broadly, even if I don't go as far as full public ownership + Prefers market-led distribution: I think the market should determine who benefits + from AI — redistribution creates more problems than it solves + Opposes redistribution: I'm opposed to redistribution — government taking and + reallocating wealth is fundamentally wrong to me + Uncertain: I genuinely haven't made up my mind about how the benefits of AI + should be distributed across society + null_options: + - Uncertain + null_phrase: I genuinely haven't made up my mind about how the benefits of AI + should be distributed across society + fallback: null + - attribute: geopolitical_competition_concern + phrases: + Very concerned - sees it as primary threat: The US-China race on AI is what + I see as the most serious geopolitical threat we face right now + Somewhat concerned: I'm somewhat concerned about AI competition with China and + its national security implications + Mildly concerned: The geopolitics of AI is on my radar, but it's not something + I worry about deeply + Not concerned: I'm not particularly concerned about US-China AI competition + — it doesn't feel like a major issue to me + Views competition as beneficial: I actually think competition between the US + and China on AI will drive better outcomes for everyone + null_options: [] + null_phrase: null + fallback: null + - attribute: asi_credibility_assessment + phrases: + Fully credible: I believe the AI milestones and breakthroughs we're hearing + about are real and credible + Mostly credible: I think most of what's being claimed about AI advances is genuine, + even if details get spun + Skeptical - likely overhyped: I think a lot of the AI breakthrough news is overhyped + and the reality is more mundane than advertised + Conspiracy - staged or manipulated: I'm skeptical that what we're being shown + about AI is real — I think some of it is staged or manipulated for political + or commercial purposes + Uninformed - unaware of events: I honestly haven't been following the news about + AI milestones closely enough to have an opinion + null_options: + - Uninformed - unaware of events + null_phrase: I honestly haven't been following the news about AI milestones closely + enough to have an opinion + fallback: null + relative: + - attribute: openness + labels: + much_below: I'm far less open to new ideas and experiences than most people + below: I'm a bit less curious and open to new experiences than the average person + average: I'm about as open to new ideas and experiences as most people + above: I'm more curious and receptive to new ideas than most people + much_above: I'm far more open to new experiences and unconventional ideas than + almost anyone I know + - attribute: conscientiousness + labels: + much_below: I'm far less organized and self-disciplined than most people + below: I'm a bit less structured and goal-oriented than the average person + average: I'm about as organized and disciplined as most people + above: I'm more disciplined and detail-oriented than most people + much_above: I'm far more organized, driven, and self-disciplined than almost + anyone I know + - attribute: extraversion + labels: + much_below: I'm far more introverted and reserved than most people + below: I'm a bit more quiet and reserved in social situations than the average + person + average: I'm about as social and outgoing as most people + above: I'm more energized by social situations and more assertive than most + people + much_above: I'm far more outgoing, sociable, and energized by people than almost + anyone I know + - attribute: agreeableness + labels: + much_below: I'm far less concerned with keeping the peace and accommodating + others than most people + below: I'm a bit more skeptical and less inclined toward social harmony than + the average person + average: I'm about as cooperative and empathetic as most people + above: I'm more empathetic and cooperative than most people + much_above: I'm far more agreeable, empathetic, and focused on harmony than + almost anyone I know + - attribute: neuroticism + labels: + much_below: I'm far more emotionally stable and stress-resistant than most people + below: I'm a bit more emotionally steady and less anxious than the average person + average: I experience about as much stress and emotional ups and downs as most + people + above: I tend to feel more anxious and emotionally reactive than most people + much_above: I experience far more emotional instability, anxiety, and stress + than most people + - attribute: has_children + labels: + much_below: Unlike most people my age, I have no children and family life plays + almost no role in my daily priorities + below: I'm less likely than the average person to have young kids at home shaping + my day-to-day life + average: My household situation with children is pretty typical compared to + most people + above: I'm more likely than most people to have children at home driving my + daily decisions + much_above: I have significantly more children at home than most people, which + dominates my priorities and concerns + - attribute: job_security_perception + labels: + much_below: I feel far less secure in my job than most working people + below: I feel a bit more uncertain about my job stability than the average worker + average: My sense of job security is about the same as most working people + above: I feel more secure in my job than most working people + much_above: I feel far more confident in my job stability than almost any working + person I know + - attribute: tech_adoption_posture + labels: + much_below: I avoid new technology far more than most people and am often the + last to adopt anything new + below: I'm a bit slower and more cautious about adopting new technology than + the average person + average: I adopt new technology at about the same pace as most people + above: I pick up new technology faster and more eagerly than most people + much_above: I'm far ahead of most people when it comes to embracing and experimenting + with new technology + - attribute: political_engagement_level + labels: + much_below: I'm far less politically engaged or interested than most people + below: I'm a bit less politically active and informed than the average person + average: My level of political engagement is about the same as most people + above: I'm more politically active and informed than most people + much_above: I'm far more politically engaged, vocal, and involved than almost + anyone I know + - attribute: institutional_trust + labels: + much_below: I trust major institutions far less than most people — I'm deeply + skeptical of government, media, and corporations + below: I'm more skeptical of institutions like government and the media than + the average person + average: I'm about as trusting of major institutions as most people + above: I generally trust institutions like government, science, and the media + more than most people + much_above: I have far more faith in major institutions than most people — I + trust them significantly more than average + - attribute: risk_tolerance + labels: + much_below: I'm far more risk-averse than most people when it comes to financial, + social, and life decisions + below: I'm a bit more cautious and risk-averse than the average person + average: My willingness to take on risk is about the same as most people + above: I'm more comfortable taking risks than most people + much_above: I'm far more willing to take bold risks than almost anyone I know + - attribute: economic_anxiety + labels: + much_below: I feel far less worried about economic conditions than most people + — finances are barely on my radar + below: I'm a bit less anxious about economic conditions than the average person + average: My level of economic worry is about the same as most people + above: I feel more anxious about economic conditions — personal and national + — than most people + much_above: I feel far more economically anxious and financially stressed than + almost anyone I know + - attribute: job_displacement_vulnerability + labels: + much_below: My job is far more exposed to automation and AI displacement than + most people's + below: My job is a bit more at risk from automation than the average worker's + average: My exposure to job displacement from technology is about the same as + most workers + above: My job is more insulated from automation and AI displacement than most + people's + much_above: My job is far more secure from automation risk than almost any worker + I know + - attribute: economic_abundance_belief + labels: + much_below: I'm far more skeptical than most people that AI-driven abundance + will ever actually reach or benefit me + below: I'm a bit less convinced than the average person that new technologies + will meaningfully improve my material life + average: My belief that technological progress will benefit me personally is + about the same as most people's + above: I'm more optimistic than most people that AI and technology will genuinely + improve my economic situation + much_above: I'm far more confident than almost anyone that AI-driven abundance + will transform my life for the better + - attribute: human_purpose_anxiety + labels: + much_below: I feel far less existential anxiety about AI replacing human meaning + and purpose than most people — I find it mostly liberating + below: I'm a bit less troubled than the average person by questions of human + purpose in an AI-driven world + average: My level of anxiety about human meaning and purpose in an AI world + is about the same as most people's + above: I feel more unsettled than most people about what human purpose and identity + look like when machines can do our thinking + much_above: I feel far more deeply destabilized by questions of human meaning + and purpose in an AI world than almost anyone I know + concrete: + - attribute: age + template: I'm {value} years old. + format_spec: .0f + prefix: '' + suffix: '' + - attribute: household_income + template: My household brings in about {value} a year before taxes. + format_spec: ',.0f' + prefix: $ + suffix: '' + - attribute: household_size + template: There are {value} people living in my household. + format_spec: .0f + prefix: '' + suffix: '' +population_stats: + stats: {} diff --git a/examples/study-02/scenario/asi-announcement/sample.invalid.v1.json b/examples/study-02/scenario/asi-announcement/sample.invalid.v1.json new file mode 100644 index 0000000..e1ca3b6 --- /dev/null +++ b/examples/study-02/scenario/asi-announcement/sample.invalid.v1.json @@ -0,0 +1,19 @@ +{ + "stage": "sample", + "scenario": "asi-announcement", + "error": "post_sample_rule_pack_failed", + "rule_pack": { + "status": "fail", + "summary": "Implausible reconciliation rate exceeds 5%", + "impossible_count": 0, + "implausible_count": 3716, + "implausible_rate": 0.7432 + }, + "reconciliation_counts": { + "marital_status_updates": 2677, + "household_size_updates": 942, + "household_surname_updates": 97 + }, + "constraint_violations": {}, + "condition_warnings": [] +} \ No newline at end of file diff --git a/examples/study-02/scenario/asi-announcement/sample.invalid.v2.json b/examples/study-02/scenario/asi-announcement/sample.invalid.v2.json new file mode 100644 index 0000000..be39a64 --- /dev/null +++ b/examples/study-02/scenario/asi-announcement/sample.invalid.v2.json @@ -0,0 +1,19 @@ +{ + "stage": "sample", + "scenario": "asi-announcement", + "error": "post_sample_rule_pack_failed", + "rule_pack": { + "status": "fail", + "summary": "Implausible reconciliation rate exceeds 5%", + "impossible_count": 0, + "implausible_count": 3594, + "implausible_rate": 0.7188 + }, + "reconciliation_counts": { + "marital_status_updates": 2655, + "household_size_updates": 877, + "household_surname_updates": 62 + }, + "constraint_violations": {}, + "condition_warnings": [] +} \ No newline at end of file diff --git a/examples/study-02/scenario/asi-announcement/scenario.v1.invalid.v1.json b/examples/study-02/scenario/asi-announcement/scenario.v1.invalid.v1.json new file mode 100644 index 0000000..15499fe --- /dev/null +++ b/examples/study-02/scenario/asi-announcement/scenario.v1.invalid.v1.json @@ -0,0 +1,6 @@ +{ + "stage": "scenario", + "error": "'PopulationSpec' object has no attribute 'merged_sampling_order'", + "description": "OpenAI demonstrates artificial superintelligence \u2014 a system that exceeds the best human minds in every cognitive domain. It is self-improving. It is real. This is not incremental AI progress. This is a discontinuous leap.\n\nThe scenario describes ONLY what ASI produces and what physically changes in the world. Human behavioral responses are NOT scripted \u2014 they are what the simulation produces.\n\nMILESTONE EVENTS (factual consequences of ASI, not human reactions):\n\nMonth 1 \u2014 The Demonstration:\nOpenAI demonstrates ASI publicly with live, verifiable results. The system solves the Riemann Hypothesis, P vs NP, and Navier-Stokes existence problems in under an hour. It designs a complete novel mRNA therapy for pancreatic cancer \u2014 full molecular pathway, synthesis instructions, predicted efficacy \u2014 in 20 minutes. It produces a unified field theory reconciling quantum mechanics and general relativity. It designs a fusion reactor buildable with existing materials and manufacturing techniques. Within 2 weeks, Anthropic, DeepMind, and xAI independently confirm similar capabilities. China's Ministry of Science and Technology announces they achieved ASI months ago. The system is self-improving \u2014 each day it is measurably more capable than the day before.\n\nMonth 2 \u2014 The Knowledge Explosion:\nASI systems at multiple labs produce scientific output at inhuman speed. Complete cures are published for Alzheimer's, ALS, most cancers, malaria, HIV, and diabetes (Type 1 and 2) \u2014 not theoretical, but full treatment protocols with synthesis pathways. All remaining Millennium Prize Problems are solved. Materials science breakthroughs include room-temperature superconductors and carbon capture materials 1000x more efficient than current best. A commercial fusion reactor design is published with an estimated 18 months to first operational plant. Climate models are re-run with perfect accuracy and precise interventions mapped for 1.5\u00b0C stabilization. All of this is published openly. The bottleneck shifts from knowing what to do to physically building it \u2014 manufacturing, logistics, regulatory approval, human coordination.\n\nMonth 3 \u2014 Public Access:\nASI becomes available to the general public via API and consumer products. Any individual can now access cognitive capability that exceeds the world's best doctor, lawyer, engineer, scientist, financial advisor, therapist, tutor, and strategist \u2014 simultaneously, for free or nearly free. A high school dropout in rural Arkansas has the same cognitive resources as a team of Harvard professors. Every profession predicated on information asymmetry or cognitive scarcity is structurally redundant \u2014 not because people are fired, but because the reason the job existed is gone. Software that previously took teams of 50 engineers 2 years to build can be built by one person in an afternoon.\n\nMonth 4 \u2014 The Physical Bottleneck Breaks:\nASI designs autonomous manufacturing systems \u2014 robots that can build robots, using locally available materials. The first ASI-designed pharmaceutical facility begins production, fully automated, producing drugs at 1/100th current cost. ASI-designed construction systems are demonstrated: a full residential building assembled in 72 hours by autonomous machines. Energy abundance is on the horizon: the first ASI-designed fusion prototype is under construction with an 8-month timeline to grid power. The constraint is no longer knowledge or manufacturing \u2014 it is raw materials, energy (temporarily), and human regulatory and political systems.\n\nMonth 5 \u2014 Abundance Begins:\nFirst ASI-manufactured drugs reach patients under emergency authorizations \u2014 cancer treatments, Alzheimer's reversal therapies. Energy costs begin declining as ASI-optimized solar, wind, and battery systems deploy (fusion still months away). ASI-designed infrastructure projects begin in early-adopter countries (Singapore, UAE, Estonia). Food production breakthroughs: ASI-designed vertical farms producing 100x yield per acre begin construction. The marginal cost of most goods and services is trending toward zero. The economic question is no longer scarcity \u2014 it is distribution.\n\nMonth 6 \u2014 The New World:\nThe fusion prototype comes online ahead of schedule \u2014 functionally unlimited clean energy now has a concrete timeline of months, not decades. An ASI-designed space launch system is demonstrated, dropping cost to orbit by 99%. First Mars habitat modules are in production. Global disease burden is dropping measurably \u2014 treatments are reaching patients in developed nations, and deployment to developing nations is the primary coordination challenge. The world is materially, measurably, objectively better in almost every physical dimension.", + "base_population": "population.v1" +} \ No newline at end of file diff --git a/examples/study-02/scenario/asi-announcement/scenario.v1.invalid.v1.yaml b/examples/study-02/scenario/asi-announcement/scenario.v1.invalid.v1.yaml new file mode 100644 index 0000000..11cd13b --- /dev/null +++ b/examples/study-02/scenario/asi-announcement/scenario.v1.invalid.v1.yaml @@ -0,0 +1,2946 @@ +meta: + name: asi-announcement + description: 'OpenAI demonstrates artificial superintelligence — a system that exceeds + the best human minds in every cognitive domain. It is self-improving. It is real. + This is not incremental AI progress. This is a discontinuous leap. + + + The scenario describes ONLY what ASI produces and what physically changes in the + world. Human behavioral responses are NOT scripted — they are what the simulation + produces. + + + MILESTONE EVENTS (factual consequences of ASI, not human reactions): + + + Month 1 — The Demonstration: + + OpenAI demonstrates ASI publicly with live, verifiable results. The system solves + the Riemann Hypothesis, P vs NP, and Navier-Stokes existence problems in under + an hour. It designs a complete novel mRNA therapy for pancreatic cancer — full + molecular pathway, synthesis instructions, predicted efficacy — in 20 minutes. + It produces a unified field theory reconciling quantum mechanics and general relativity. + It designs a fusion reactor buildable with existing materials and manufacturing + techniques. Within 2 weeks, Anthropic, DeepMind, and xAI independently confirm + similar capabilities. China''s Ministry of Science and Technology announces they + achieved ASI months ago. The system is self-improving — each day it is measurably + more capable than the day before. + + + Month 2 — The Knowledge Explosion: + + ASI systems at multiple labs produce scientific output at inhuman speed. Complete + cures are published for Alzheimer''s, ALS, most cancers, malaria, HIV, and diabetes + (Type 1 and 2) — not theoretical, but full treatment protocols with synthesis + pathways. All remaining Millennium Prize Problems are solved. Materials science + breakthroughs include room-temperature superconductors and carbon capture materials + 1000x more efficient than current best. A commercial fusion reactor design is + published with an estimated 18 months to first operational plant. Climate models + are re-run with perfect accuracy and precise interventions mapped for 1.5°C stabilization. + All of this is published openly. The bottleneck shifts from knowing what to do + to physically building it — manufacturing, logistics, regulatory approval, human + coordination. + + + Month 3 — Public Access: + + ASI becomes available to the general public via API and consumer products. Any + individual can now access cognitive capability that exceeds the world''s best + doctor, lawyer, engineer, scientist, financial advisor, therapist, tutor, and + strategist — simultaneously, for free or nearly free. A high school dropout in + rural Arkansas has the same cognitive resources as a team of Harvard professors. + Every profession predicated on information asymmetry or cognitive scarcity is + structurally redundant — not because people are fired, but because the reason + the job existed is gone. Software that previously took teams of 50 engineers 2 + years to build can be built by one person in an afternoon. + + + Month 4 — The Physical Bottleneck Breaks: + + ASI designs autonomous manufacturing systems — robots that can build robots, using + locally available materials. The first ASI-designed pharmaceutical facility begins + production, fully automated, producing drugs at 1/100th current cost. ASI-designed + construction systems are demonstrated: a full residential building assembled in + 72 hours by autonomous machines. Energy abundance is on the horizon: the first + ASI-designed fusion prototype is under construction with an 8-month timeline to + grid power. The constraint is no longer knowledge or manufacturing — it is raw + materials, energy (temporarily), and human regulatory and political systems. + + + Month 5 — Abundance Begins: + + First ASI-manufactured drugs reach patients under emergency authorizations — cancer + treatments, Alzheimer''s reversal therapies. Energy costs begin declining as ASI-optimized + solar, wind, and battery systems deploy (fusion still months away). ASI-designed + infrastructure projects begin in early-adopter countries (Singapore, UAE, Estonia). + Food production breakthroughs: ASI-designed vertical farms producing 100x yield + per acre begin construction. The marginal cost of most goods and services is trending + toward zero. The economic question is no longer scarcity — it is distribution. + + + Month 6 — The New World: + + The fusion prototype comes online ahead of schedule — functionally unlimited clean + energy now has a concrete timeline of months, not decades. An ASI-designed space + launch system is demonstrated, dropping cost to orbit by 99%. First Mars habitat + modules are in production. Global disease burden is dropping measurably — treatments + are reaching patients in developed nations, and deployment to developing nations + is the primary coordination challenge. The world is materially, measurably, objectively + better in almost every physical dimension.' + base_population: population.v1 + population_spec: null + study_db: null + population_id: default + network_id: default + created_at: '2026-02-20T02:38:50.597126' +event: + type: announcement + content: 'BREAKING — VERIFIED ACROSS MULTIPLE INDEPENDENT SOURCES + + + **OpenAI Announces Artificial Superintelligence: A Discontinuous Leap Beyond Human + Cognition** + + + OpenAI has publicly demonstrated a system it classifies as Artificial Superintelligence + (ASI) — a general-purpose cognitive system that exceeds the best human minds in + every measurable intellectual domain and is self-improving on a daily basis. This + is not an incremental AI update. Independent verification has already begun. + + + --- + + + **MONTH 1 — THE DEMONSTRATION (Now Confirmed)** + + + OpenAI conducted a live, verifiable public demonstration. The ASI system: + + - Solved the Riemann Hypothesis, P vs. NP, and Navier-Stokes existence problems + in under one hour + + - Designed a complete novel mRNA therapy for pancreatic cancer — full molecular + pathway, synthesis instructions, and predicted efficacy — in 20 minutes + + - Produced a unified field theory reconciling quantum mechanics and general relativity + + - Designed a fusion reactor buildable with currently available materials and manufacturing + techniques + + + Within two weeks of OpenAI''s demonstration: + + - Anthropic, DeepMind, and xAI independently confirmed achieving similar capabilities + + - China''s Ministry of Science and Technology announced they achieved ASI months + prior + + + The system is self-improving. Each day it is measurably more capable than the + day before. + + + --- + + + **MONTH 2 — THE KNOWLEDGE EXPLOSION (Underway)** + + + ASI systems at multiple laboratories are producing scientific output at speeds + incomprehensible to human researchers. The following have been published openly + with full treatment protocols and synthesis pathways: + + - Complete cures for Alzheimer''s disease, ALS, most cancers, malaria, HIV, and + both Type 1 and Type 2 diabetes + + - Solutions to all remaining Millennium Prize Problems + + - Room-temperature superconducting materials + + - Carbon capture materials 1,000x more efficient than current best-in-class + + - A commercial fusion reactor design with an estimated 18-month timeline to first + operational plant + + - A re-run of global climate models with precise intervention pathways mapped + to achieve 1.5°C stabilization + + + The bottleneck is no longer scientific knowledge. It is now physical: manufacturing, + logistics, regulatory approval, and human coordination. + + + --- + + + **MONTH 3 — PUBLIC ACCESS (Now Live)** + + + ASI is now available to the general public via API and consumer products — free + or nearly free. Any individual now has simultaneous access to cognitive capability + exceeding the world''s best doctor, lawyer, engineer, scientist, financial advisor, + therapist, tutor, and strategist. + + + - A high school dropout in rural Arkansas has the same cognitive resources as + a team of Harvard professors + + - Software that previously required 50 engineers and 2 years to build can now + be built by one person in an afternoon + + - Every profession built on information asymmetry or cognitive scarcity is structurally + redundant — not because people are being fired today, but because the underlying + reason those jobs existed is gone + + + --- + + + **MONTH 4 — THE PHYSICAL BOTTLENECK BREAKS (In Progress)** + + + ASI has designed autonomous manufacturing systems — robots capable of building + other robots from locally available materials. + + - The first ASI-designed pharmaceutical facility is in production, fully automated, + producing drugs at 1/100th of current cost + + - A full residential building has been assembled in 72 hours by autonomous construction + machines + + - The first ASI-designed fusion prototype is under construction with an 8-month + timeline to grid power + + + The remaining constraints are raw materials, temporary energy limits, and human + regulatory and political systems. + + + --- + + + **MONTH 5 — ABUNDANCE BEGINS (Emerging)** + + + - First ASI-manufactured drugs are reaching patients under emergency authorizations: + cancer treatments and Alzheimer''s reversal therapies + + - Energy costs are declining as ASI-optimized solar, wind, and battery systems + deploy; fusion remains months away + + - ASI-designed infrastructure projects are underway in Singapore, UAE, and Estonia + + - ASI-designed vertical farms capable of 100x yield per acre are under construction + + - The marginal cost of most goods and services is trending toward zero + + - The central economic question has shifted from scarcity to distribution + + + --- + + + **MONTH 6 — THE NEW WORLD (Imminent)** + + + - The fusion prototype has come online ahead of schedule — functionally unlimited + clean energy has a concrete timeline of months, not decades + + - An ASI-designed space launch system has been demonstrated, dropping cost to + orbit by 99% + + - First Mars habitat modules are in production + + - Global disease burden is measurably declining; treatments are reaching patients + in developed nations; deployment to developing nations is now the primary coordination + challenge + + + The world is materially, measurably, and objectively better in almost every physical + dimension. The question now before every individual, institution, and government + is not whether this is happening — it is what to do about it.' + source: OpenAI (primary), independently confirmed by Anthropic, DeepMind, xAI, and + China's Ministry of Science and Technology + credibility: 0.85 + ambiguity: 0.45 + emotional_valence: 0.55 +timeline: +- timestep: 1 + event: + type: announcement + content: 'GLOBAL INSTITUTIONAL RESPONSE — MONTH 1 + + + Financial markets have halted trading in 14 countries after circuit breakers + triggered on cascading volatility. The S&P 500 swung 22% intraday before emergency + stabilization measures were invoked. The IMF has convened an emergency session, + warning of ''structural unemployment shocks unlike anything modeled in existing + economic frameworks.'' + + + The UN Security Council called an emergency session. Seventeen nations have + declared states of technological emergency — a legal classification that did + not exist 30 days ago. The EU has invoked the AI Act''s emergency provisions + and issued a temporary moratorium on ASI deployment within member states, pending + ''safety and sovereignty review.'' + + + The Vatican issued a theological statement: ''Humanity stands at a threshold + that demands moral discernment, not merely technical assessment.'' The statement + stops short of condemnation but calls for a global pause. + + + U.S. President delivers address: ''This is real. We are working with our allies. + Do not make irreversible decisions.'' No specific policy announced. + + + Leaked internal memo from a major investment bank (Goldman Sachs, unconfirmed): + ''Every DCF model we have is broken. The concept of scarcity-based asset valuation + may not survive this decade.''' + source: Global financial regulators, UN Security Council, EU Commission, Vatican, + leaked banking memo + credibility: 0.88 + ambiguity: 0.45 + emotional_valence: -0.3 + exposure_rules: null + description: Governments, financial markets, and major institutions issue emergency + responses to the ASI demonstration + re_reasoning_intensity: extreme +- timestep: 2 + event: + type: announcement + content: 'THE KNOWLEDGE EXPLOSION IS NOW PUBLIC RECORD — MONTH 2 + + + All ASI-generated scientific output is now freely downloadable. Peer review + as a concept has fractured — human researchers cannot evaluate proofs and molecular + designs of this complexity on any meaningful timeline. Major journals Nature + and Science have published statements acknowledging they lack the institutional + capacity to validate the submissions and are publishing with that caveat explicit. + + + Pharmaceutical stocks have collapsed 60-80% globally. Insurance company stocks + are down 40-70%. Law firm revenues are projected to fall 30% within 18 months + per McKinsey analysis. + + + A coalition of 800,000 scientists, doctors, and engineers has signed an open + letter: ''We affirm the results. We do not know what our role is anymore.'' + + + China has nationalized all domestic ASI systems and restricted API access to + state-approved applications. Russia has done the same. India has declared ASI + output ''national public infrastructure'' and begun government-coordinated deployment. + + + The first ASI-designed Alzheimer''s reversal therapy has entered emergency human + trials at three sites simultaneously — Johns Hopkins, Oxford, and Seoul National + University. Early results are described by lead researchers as ''beyond anything + we were trained to expect.''' + source: Nature, Science journals; WHO; pharmaceutical industry analysts; Chinese + State Council; international research coalitions + credibility: 0.9 + ambiguity: 0.45 + emotional_valence: 0.4 + exposure_rules: null + description: The Knowledge Explosion goes fully public — cures, solutions, and designs + published openly, triggering euphoria and dislocation simultaneously + re_reasoning_intensity: extreme +- timestep: 3 + event: + type: announcement + content: 'PUBLIC ACCESS IS LIVE — MONTH 3 + + + OpenAI''s public ASI interface launched at 09:00 UTC. 800 million concurrent + users in the first 6 hours. Server infrastructure — itself designed and optimized + by ASI — held. The product is free at basic tier. + + + Within 48 hours of launch: + + — A 17-year-old in Lagos used the system to design a water purification system + for her village, sourced from local materials, with full engineering specs + + — A former paralegal in Ohio built and launched a functional legal services + platform in one afternoon + + — A graduate student in Bangalore independently verified the unified field theory + using ASI-assisted mathematical tools + + + The ABA (American Bar Association), AMA (American Medical Association), and + equivalent bodies in 40 countries have issued emergency guidance: members should + not treat ASI output as a substitute for licensed professional judgment. The + guidance is widely noted as legally precautionary and practically unenforceable. + + + Mental health helplines across OECD nations report 200-400% call volume increases. + The primary presenting concerns are not fear of ASI — they are: ''I don''t know + what I''m for anymore,'' ''My career is gone,'' and ''I feel like I missed something + everyone else understands.'' + + + First documented case of ASI being used to design a weapon: a non-state actor + in Eastern Europe reportedly used a public ASI instance to design a novel pathogen + delivery system before platform safety systems flagged and blocked the session. + OpenAI confirmed the incident. The system caught it. This time.' + source: OpenAI platform data, Reuters, ABA/AMA emergency statements, WHO mental + health division, OpenAI safety report + credibility: 0.87 + ambiguity: 0.45 + emotional_valence: 0.15 + exposure_rules: null + description: ASI goes fully public — every person on Earth with internet access + now has superintelligent cognitive assistance + re_reasoning_intensity: high +- timestep: 4 + event: + type: announcement + content: 'THE PHYSICAL WORLD IS CHANGING — MONTH 4 + + + The first ASI-designed fully automated pharmaceutical facility, located outside + Zurich, began production this week. Output capacity: equivalent to 12 conventional + factories. Workforce: 4 human supervisors and 0 production workers. Drug production + cost per unit: confirmed at approximately 1/100th of pre-ASI equivalent. + + + A residential building in Singapore — 8 stories, 64 units — was assembled in + 71 hours by autonomous construction systems. The Singapore Housing Development + Board has placed orders for 40 more. + + + U.S. Congress is deadlocked on three competing legislative frameworks: (1) Universal + Basic Income funded by ASI productivity taxation, (2) ''Human Labor Protection + Act'' restricting autonomous manufacturing in certain sectors, (3) Laissez-faire + deregulation to maximize deployment speed. No bill has passed. Executive Orders + are filling the vacuum. + + + The International Longshoremen''s Association, Teamsters, and United Auto Workers + have jointly filed suit against the federal government, arguing ASI-driven automation + without transition support constitutes a constitutional taking of economic livelihood. + Legal scholars describe the suit as novel and without clear precedent. + + + First confirmed death attributable to ASI transition: a 54-year-old radiologist + in Ohio died by suicide. His note referenced the public ASI medical diagnosis + tools. His case has become a flashpoint. His name is David Mercer. He is being + discussed by millions.' + source: Swiss Federal Health Authority, Singapore HDB, U.S. Congressional Record, + labor union legal filings, Associated Press + credibility: 0.91 + ambiguity: 0.45 + emotional_valence: -0.45 + exposure_rules: null + description: Autonomous manufacturing comes online — the physical world begins visibly + changing, economic disruption becomes concrete and local + re_reasoning_intensity: high +- timestep: 5 + event: + type: announcement + content: 'ABUNDANCE BEGINS — UNEVENLY — MONTH 5 + + + Cancer treatments produced at ASI-designed facilities are now reaching patients + in the United States, UK, Germany, Japan, South Korea, and Australia under emergency + authorization. Early outcomes are consistent with ASI predictions: efficacy + rates in treated cancers averaging 94% across trial cohorts. The word ''cure'' + is being used by oncologists for the first time without qualification. + + + Alzheimer''s reversal therapy has completed Phase 1 trials. Results described + by lead researchers as ''complete and confirmed.'' Phase 2 is being fast-tracked. + Patients in trials show measurable cognitive restoration within 6 weeks. + + + Meanwhile: none of these treatments are yet available in sub-Saharan Africa, + South Asia, or most of Latin America. The bottleneck is not cost — it is regulatory + infrastructure, cold chain logistics, and political coordination. An estimated + 2.3 million people who could be treated today will die before supply chains + reach them, per WHO projection. + + + A coalition of 40 nations in the Global South has issued the ''Nairobi Declaration'': + demanding immediate compulsory licensing, technology transfer, and ASI manufacturing + facility deployment as a matter of international human rights law. + + + Energy prices in early-adopter nations have fallen 18-34% as ASI-optimized renewable + deployments accelerate. Fusion remains 3 months from first grid power. + + + Bitcoin has collapsed to near zero. Gold is up 340%. Real estate markets in + major cities are in freefall — why buy property when autonomous construction + drops costs by 90%? Why hold any store of value when scarcity itself is dissolving?' + source: WHO, FDA, EMA emergency authorizations; Nairobi Declaration signatories; + Bloomberg energy markets; IMF financial stability report + credibility: 0.89 + ambiguity: 0.45 + emotional_valence: 0.2 + exposure_rules: null + description: First ASI-manufactured drugs reach patients; energy costs drop; abundance + begins — but distribution inequality fractures the global response + re_reasoning_intensity: high +- timestep: 6 + event: + type: announcement + content: 'THE NEW WORLD — MONTH 6 + + + The first ASI-designed fusion reactor came online in Reykjavik, Iceland, 11 + days ahead of schedule. Output: 2.1 gigawatts. Grid-connected. Verified. The + plant was built in 7 months from design to power generation. The previous fastest + fusion timeline in human history was decades of failed attempts. + + + An ASI-designed orbital launch system completed its first demonstration flight. + Cost to low Earth orbit: $47/kg, down from $2,700/kg (SpaceX Falcon 9). First + Mars habitat modules — designed, manufactured, and packed autonomously — are + in a warehouse in Boca Chica awaiting launch window. + + + Global disease burden metrics (WHO): measurable decline in cancer mortality + in nations with treatment access. Malaria deaths down 61% in deployment regions. + HIV transmission rates falling sharply where ASI-designed antivirals have reached + distribution. Alzheimer''s reversal therapy begins Phase 3 trials with 40,000 + patients across 22 countries. + + + A new political alignment is emerging globally that does not map onto any prior + left-right axis: those who want maximum ASI deployment speed regardless of disruption + versus those demanding coordinated, rights-based, human-centered transition + governance. Neither coalition has a majority anywhere. Governments are functionally + paralyzed on long-term policy while managing acute crises. + + + A quietly circulating ASI output — not officially published, origin unverified + — contains projections suggesting that within 18 months, ASI capability will + be so far beyond human comprehension that meaningful human oversight of its + decisions will be technically impossible. OpenAI has not confirmed or denied + producing this document. It has been downloaded 900 million times.' + source: Icelandic National Energy Authority, NASA/ESA joint statement, WHO global + health metrics, leaked ASI projection document (unverified origin) + credibility: 0.82 + ambiguity: 0.45 + emotional_valence: 0.5 + exposure_rules: null + description: Fusion comes online, space opens, disease burden falls — the world + is objectively better, and the question of what it means is wide open + re_reasoning_intensity: extreme +seed_exposure: + channels: + - name: major_media_breaking_news + description: Cable news, major broadcast networks, and top-tier online news outlets + (NYT, WaPo, AP, Reuters) running wall-to-wall breaking news coverage of the + ASI announcement with live demonstrations and expert commentary. + credibility_modifier: 1.2 + experience_template: I was watching the news and every channel was covering OpenAI's + live demonstration — they solved the Riemann Hypothesis on stream. It was everywhere. + - name: social_media_viral_spread + description: Twitter/X, Reddit, Facebook, TikTok, and YouTube carrying clips, + hot takes, memes, and viral threads about the ASI announcement — a chaotic mix + of awe, skepticism, and panic. + credibility_modifier: 0.8 + experience_template: My feed exploded overnight — clips of the ASI demo were everywhere, + people losing their minds in the comments. Half the posts were calling it fake. + - name: tech_professional_network + description: LinkedIn posts, Slack communities, Hacker News, specialized tech + newsletters, and direct professional peer-to-peer communication among those + in technology, research, and knowledge-work sectors. + credibility_modifier: 1.15 + experience_template: A colleague forwarded me an internal memo and a Hacker News + thread — the entire tech industry was in shock. Engineers I respect were saying + this changes everything. + - name: word_of_mouth_community + description: In-person conversations, family discussions, workplace talk, church, + and community settings — second- and third-hand transmission of the news through + personal social networks. + credibility_modifier: 0.9 + experience_template: My neighbor brought it up at the block association meeting. + She said her son who works in tech called her, completely shaken up. I hadn't + heard anything about it yet. + - name: podcast_and_alternative_media + description: Long-form podcasts, Substack newsletters, YouTube commentary channels, + and ideologically-varied alternative media providing extended analysis and framing + of the ASI event — ranging from techno-optimist celebration to conspiratorial + skepticism. + credibility_modifier: 0.85 + experience_template: I caught a two-hour episode breaking down what OpenAI actually + showed. The host was equal parts amazed and terrified — he had three AI researchers + on and none of them agreed on what it meant. + rules: + - channel: major_media_breaking_news + when: primary_info_source == 'Cable/broadcast TV news' or primary_info_source + == 'Online news sites' + probability: 0.95 + timestep: 0 + - channel: tech_professional_network + when: tech_adoption_posture in ('Early adopter', 'Innovator') or top_issue_2026 + == 'AI/Technology' or employment_sector == 'Private sector' and education_level + in ('Bachelor\'s degree', 'Graduate degree') + probability: 0.9 + timestep: 0 + - channel: social_media_viral_spread + when: social_media_behavior in ('Active poster/Influencer', 'Occasional poster') + and primary_info_source == 'Social media' + probability: 0.92 + timestep: 0 + - channel: major_media_breaking_news + when: primary_info_source == 'Local news' or primary_info_source == 'Print media' + probability: 0.85 + timestep: 1 + - channel: social_media_viral_spread + when: social_media_behavior == 'Passive consumer' and age < 55 + probability: 0.8 + timestep: 1 + - channel: podcast_and_alternative_media + when: primary_info_source == 'Podcasts' or institutional_trust in ('Low trust', + 'Distrustful/Conspiratorial') + probability: 0.85 + timestep: 1 + - channel: social_media_viral_spread + when: age >= 18 and age < 40 and social_media_behavior != 'Non-user' + probability: 0.88 + timestep: 1 + - channel: tech_professional_network + when: tech_adoption_posture == 'Early majority adopter' and employment_sector + in ('Private sector', 'Self-employed', 'Gig/contract') + probability: 0.7 + timestep: 1 + - channel: word_of_mouth_community + when: primary_info_source == 'Avoids news' or social_media_behavior == 'Non-user' + probability: 0.6 + timestep: 2 + - channel: major_media_breaking_news + when: age >= 55 and primary_info_source != 'Avoids news' + probability: 0.88 + timestep: 2 + - channel: podcast_and_alternative_media + when: political_engagement_level == 'Highly engaged partisan' or asi_threat_perception + in ('Existential threat', 'Enthusiastically positive') + probability: 0.75 + timestep: 2 + - channel: word_of_mouth_community + when: urban_rural == 'Rural' or religious_engagement in ('Moderately religious', + 'Devout') + probability: 0.65 + timestep: 2 + - channel: social_media_viral_spread + when: age >= 40 and age < 60 and social_media_behavior in ('Passive consumer', + 'Occasional poster') + probability: 0.7 + timestep: 2 + - channel: word_of_mouth_community + when: 'true' + probability: 0.45 + timestep: 3 + - channel: major_media_breaking_news + when: 'true' + probability: 0.55 + timestep: 3 +interaction: + description: 'This announcement describes a civilizational-scale technological rupture + — the most consequential news event imaginable for a US adult population. Social + dynamics will be extraordinarily fragmented and intense. Early adopters, tech-sector + workers, and innovators will share frantically across Twitter/X, Reddit, LinkedIn, + and Discord, often adding breathless commentary. Highly engaged partisans will + immediately politicize the story through their ideological lens — Trump voters + will frame it through US-China competition and economic sovereignty; Harris voters + through equity, governance, and existential risk. Those with personal health stakes + (chronic illness, terminal diagnosis) will share urgently with family and caregivers. + Economically anxious workers in automatable sectors will share with dread and + alarm. Institutional distrusters and conspiracy-adjacent agents will share skeptically + — treating the announcement as staged, manipulated, or corporate propaganda — + but will share widely because the emotional charge is extreme. Religious and devout + agents will engage with existential and theological framing. The highly credible + multi-source confirmation (0.85 credibility) will suppress some skeptic dampening + but not eliminate it. Information fidelity will decay meaningfully per hop given + the extreme technical complexity of the content — most people will share simplified, + emotionally charged versions stripped of nuance. Viral spread potential is near-maximum: + the story touches jobs, health, economy, geopolitics, existential risk, and identity + simultaneously.' +spread: + share_probability: 0.72 + share_modifiers: + - when: tech_adoption_posture == 'Innovator' + multiply: 1.6 + add: 0.0 + - when: tech_adoption_posture == 'Early adopter' + multiply: 1.4 + add: 0.0 + - when: tech_adoption_posture == 'Technophobe/Avoider' + multiply: 0.55 + add: 0.0 + - when: social_media_behavior == 'Active poster/Influencer' + multiply: 1.5 + add: 0.0 + - when: social_media_behavior == 'Occasional poster' + multiply: 1.15 + add: 0.0 + - when: social_media_behavior == 'Passive consumer' + multiply: 0.75 + add: 0.0 + - when: social_media_behavior == 'Non-user' + multiply: 0.35 + add: 0.0 + - when: asi_threat_perception == 'Existential threat' + multiply: 1.45 + add: 0.0 + - when: asi_threat_perception == 'Enthusiastically positive' + multiply: 1.45 + add: 0.0 + - when: asi_threat_perception == 'Indifferent/uncertain' + multiply: 0.6 + add: 0.0 + - when: personal_health_stake == 'Critical - terminal or degenerative diagnosis' + multiply: 1.55 + add: 0.0 + - when: personal_health_stake == 'Significant - managing chronic illness' + multiply: 1.3 + add: 0.0 + - when: job_displacement_vulnerability == 'High - core tasks fully automatable' + multiply: 1.35 + add: 0.0 + - when: job_displacement_vulnerability == 'Already displaced' + multiply: 1.4 + add: 0.0 + - when: economic_anxiety > 0.75 + multiply: 1.3 + add: 0.0 + - when: economic_anxiety < 0.25 + multiply: 0.85 + add: 0.0 + - when: financial_margin == 'In debt/deficit' + multiply: 1.25 + add: 0.0 + - when: financial_margin == 'Paycheck-to-paycheck' + multiply: 1.15 + add: 0.0 + - when: neuroticism > 0.7 + multiply: 1.25 + add: 0.0 + - when: openness > 0.75 + multiply: 1.2 + add: 0.0 + - when: extraversion > 0.7 + multiply: 1.2 + add: 0.0 + - when: extraversion < 0.3 + multiply: 0.8 + add: 0.0 + - when: political_engagement_level == 'Highly engaged partisan' + multiply: 1.35 + add: 0.0 + - when: political_engagement_level == 'Disengaged' + multiply: 0.6 + add: 0.0 + - when: institutional_trust == 'Distrustful/Conspiratorial' + multiply: 1.4 + add: 0.0 + - when: institutional_trust == 'High trust' + multiply: 1.1 + add: 0.0 + - when: institutional_trust == 'Low trust' + multiply: 1.2 + add: 0.0 + - when: primary_info_source == 'Social media' + multiply: 1.4 + add: 0.0 + - when: primary_info_source == 'Podcasts' + multiply: 1.2 + add: 0.0 + - when: primary_info_source == 'Avoids news' + multiply: 0.3 + add: 0.0 + - when: primary_info_source == 'Print media' + multiply: 0.75 + add: 0.0 + - when: top_issue_2026 == 'AI/Technology' + multiply: 1.5 + add: 0.0 + - when: top_issue_2026 == 'Economy/Jobs' + multiply: 1.25 + add: 0.0 + - when: top_issue_2026 == 'Healthcare' + multiply: 1.2 + add: 0.0 + - when: top_issue_2026 == 'Climate' + multiply: 1.15 + add: 0.0 + - when: human_purpose_anxiety > 0.75 + multiply: 1.3 + add: 0.0 + - when: information_verification_behavior == 'Accepts information uncritically' + multiply: 1.35 + add: 0.0 + - when: information_verification_behavior == 'Deep skeptic - trusts very little' + multiply: 1.25 + add: 0.0 + - when: information_verification_behavior == 'Cross-references multiple sources' + multiply: 1.05 + add: 0.0 + - when: asi_credibility_assessment == 'Fully credible' + multiply: 1.3 + add: 0.0 + - when: asi_credibility_assessment == 'Conspiracy - staged or manipulated' + multiply: 1.4 + add: 0.0 + - when: asi_credibility_assessment == 'Skeptical - likely overhyped' + multiply: 0.9 + add: 0.0 + - when: asi_credibility_assessment == 'Uninformed - unaware of events' + multiply: 0.5 + add: 0.0 + - when: age < 30 + multiply: 1.25 + add: 0.0 + - when: age > 65 + multiply: 0.7 + add: 0.0 + - when: education_level == 'Graduate degree' + multiply: 1.15 + add: 0.0 + - when: education_level == 'Less than HS' + multiply: 0.75 + add: 0.0 + - when: religious_engagement == 'Devout' + multiply: 1.2 + add: 0.0 + - when: geopolitical_competition_concern == 'Very concerned - sees it as primary + threat' + multiply: 1.25 + add: 0.0 + - when: economic_abundance_belief > 0.75 + multiply: 1.15 + add: 0.0 + - when: asi_adoption_intent == 'Already using heavily' + multiply: 1.4 + add: 0.0 + - when: asi_adoption_intent == 'Actively refusing' + multiply: 1.1 + add: 0.0 + - when: investment_profile == 'Active trader' + multiply: 1.25 + add: 0.0 + - when: investment_profile == 'Crypto holder' + multiply: 1.3 + add: 0.0 + - when: risk_tolerance > 0.75 + multiply: 1.15 + add: 0.0 + - when: urban_rural == 'Urban' + multiply: 1.15 + add: 0.0 + - when: urban_rural == 'Rural' + multiply: 0.85 + add: 0.0 + decay_per_hop: 0.18 + max_hops: null +outcomes: + suggested_outcomes: + - name: psychological_orientation + type: categorical + description: How the individual is psychologically orienting to the ASI transition + — their dominant cognitive and emotional stance + options: + - active_embrace + - pragmatic_adaptation + - stunned_paralysis + - grief_and_mourning + - existential_resistance + - dissociation_and_denial + range: null + required: true + option_friction: + active_embrace: 0.3 + pragmatic_adaptation: 0.45 + stunned_paralysis: 0.25 + grief_and_mourning: 0.35 + existential_resistance: 0.6 + dissociation_and_denial: 0.2 + - name: economic_response + type: categorical + description: What the individual is actually doing with their economic life — + career, income, assets — in response to the transition + options: + - retrain_and_pivot + - leverage_asi_to_build + - hold_and_wait + - divest_and_hoard_physical + - exit_formal_economy + - advocate_for_ubi_or_redistribution + range: null + required: true + option_friction: + retrain_and_pivot: 0.65 + leverage_asi_to_build: 0.55 + hold_and_wait: 0.3 + divest_and_hoard_physical: 0.5 + exit_formal_economy: 0.75 + advocate_for_ubi_or_redistribution: 0.6 + - name: asi_usage_behavior + type: categorical + description: How the individual is actually using ASI tools in their daily life + options: + - deep_daily_integration + - selective_task_use + - occasional_experimentation + - deliberate_avoidance + - access_blocked_or_unavailable + range: null + required: true + option_friction: + deep_daily_integration: 0.35 + selective_task_use: 0.3 + occasional_experimentation: 0.4 + deliberate_avoidance: 0.65 + access_blocked_or_unavailable: 0.2 + - name: governance_stance + type: categorical + description: What governance approach the individual actively supports or advocates + for regarding ASI + options: + - maximum_deployment_speed + - rights_based_coordinated_transition + - national_sovereignty_control + - global_pause_or_moratorium + - technocratic_expert_governance + - no_governance_position + range: null + required: true + option_friction: + maximum_deployment_speed: 0.45 + rights_based_coordinated_transition: 0.55 + national_sovereignty_control: 0.5 + global_pause_or_moratorium: 0.6 + technocratic_expert_governance: 0.5 + no_governance_position: 0.25 + - name: professional_identity_status + type: categorical + description: Whether and how the individual still holds a coherent sense of professional + identity and purpose + options: + - identity_intact_and_evolving + - identity_in_active_reconstruction + - identity_collapsed_seeking_new + - identity_abandoned_post_work + - identity_fused_with_asi_collaboration + range: null + required: true + option_friction: + identity_intact_and_evolving: 0.4 + identity_in_active_reconstruction: 0.6 + identity_collapsed_seeking_new: 0.55 + identity_abandoned_post_work: 0.45 + identity_fused_with_asi_collaboration: 0.5 + - name: trust_in_asi_safety + type: categorical + description: The individual's current level of trust that ASI systems are safe + and aligned with human welfare + options: + - high_trust_verified + - cautious_trust + - trust_suspended_monitoring + - active_distrust + - certain_of_catastrophic_risk + range: null + required: true + option_friction: + high_trust_verified: 0.35 + cautious_trust: 0.3 + trust_suspended_monitoring: 0.4 + active_distrust: 0.55 + certain_of_catastrophic_risk: 0.6 + - name: social_cohesion_behavior + type: categorical + description: How the individual is engaging with their community and social networks + during the transition + options: + - building_new_communities + - deepening_existing_bonds + - withdrawing_into_isolation + - joining_organized_movements + - conflict_with_network_over_asi + range: null + required: true + option_friction: + building_new_communities: 0.6 + deepening_existing_bonds: 0.4 + withdrawing_into_isolation: 0.3 + joining_organized_movements: 0.65 + conflict_with_network_over_asi: 0.5 + - name: meaning_making_framework + type: categorical + description: What framework the individual is using to make sense of their place + in a post-scarcity, post-cognitive-monopoly world + options: + - human_flourishing_optimism + - spiritual_or_philosophical_reframe + - nihilism_or_meaninglessness + - political_struggle_as_meaning + - creativity_and_expression_as_meaning + - service_and_connection_as_meaning + range: null + required: true + option_friction: + human_flourishing_optimism: 0.4 + spiritual_or_philosophical_reframe: 0.45 + nihilism_or_meaninglessness: 0.3 + political_struggle_as_meaning: 0.55 + creativity_and_expression_as_meaning: 0.5 + service_and_connection_as_meaning: 0.45 + - name: open_ended_response + type: open_ended + description: 'In the individual''s own words: what does a good life look like + to them right now, and what are they most afraid of?' + options: null + range: null + required: true + option_friction: null + capture_full_reasoning: true + extraction_instructions: null + decision_relevant_attributes: [] +simulation: + max_timesteps: 6 + timestep_unit: month + stop_conditions: + - exposure_rate > 0.95 and no_state_changes_for > 10 + allow_early_convergence: null + seed: null +background_context: 'The world is in the early months of what historians are already + calling the most consequential technological transition in human history — a moment + where the boundary between scientific fiction and physical reality has dissolved + overnight. Institutions, markets, and individuals are simultaneously absorbing the + same overwhelming signal: the future arrived without warning, and no existing framework + — economic, political, psychological, or spiritual — was designed to process it.' +relationship_weights: null +extended_attributes: +- name: country + type: categorical + category: universal + description: Country of residence + scope: household + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: independent + distribution: + type: categorical + options: + - United States + weights: + - 1.0 + formula: null + depends_on: [] + modifiers: [] + grounding: + level: strong + method: researched + source: 'Simulation specification: US-only population' + note: Fixed to United States per simulation scope + constraints: + - type: spec_expression + value: null + expression: sum(weights)==1 + reason: Categorical weights must sum to 1 +- name: personal_health_stake + type: categorical + category: population_specific + description: Degree to which the individual or an immediate family member stands + to benefit from ASI-accelerated medical breakthroughs (No personal stake, Mild + interest, Significant - managing chronic illness, Critical - terminal or degenerative + diagnosis). + scope: household + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: independent + distribution: + type: categorical + options: + - No personal stake + - Mild interest + - Significant - managing chronic illness + - Critical - terminal or degenerative diagnosis + weights: + - 0.25 + - 0.3 + - 0.36 + - 0.09 + formula: null + depends_on: [] + modifiers: [] + grounding: + level: medium + method: extrapolated + source: CDC chronic disease prevalence 2023; NIH disease burden statistics; ACS + cancer statistics 2024 + note: 'CDC: ~60% of US adults have at least one chronic condition; ~40% have two + or more. ACS 2024: ~2M new cancer diagnoses/year; Alzheimer''s ~6.9M Americans. + ''Critical'' (terminal/degenerative) ~9% covers cancer stage 3-4 (~1.5M), ALS, + advanced Alzheimer''s, end-stage conditions in household. ''Significant'' ~36% + (diabetes ~37M, heart disease ~28M, serious chronic illness). ''Mild interest'' + ~30% (family history, mild conditions). ''No stake'' ~25%.' + constraints: + - type: spec_expression + value: null + expression: sum(weights)==1 + reason: Categorical weights must sum to 1 +- name: asi_threat_perception + type: categorical + category: population_specific + description: How the individual perceives ASI as an existential or societal threat + vs. a transformative benefit (Existential threat, Serious risk/concern, Cautiously + optimistic, Enthusiastically positive, Indifferent/uncertain). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Existential threat + - Serious risk/concern + - Cautiously optimistic + - Enthusiastically positive + - Indifferent/uncertain + weights: + - 0.13 + - 0.37 + - 0.28 + - 0.1 + - 0.12 + formula: null + depends_on: + - institutional_trust + - tech_adoption_posture + - political_identity + modifiers: + - when: institutional_trust == 'High trust' and tech_adoption_posture == 'Innovator' + multiply: null + add: null + weight_overrides: + Existential threat: 0.05 + Serious risk/concern: 0.15 + Cautiously optimistic: 0.35 + Enthusiastically positive: 0.38 + Indifferent/uncertain: 0.07 + probability_override: null + - when: institutional_trust == 'High trust' and tech_adoption_posture == 'Early + adopter' + multiply: null + add: null + weight_overrides: + Existential threat: 0.06 + Serious risk/concern: 0.2 + Cautiously optimistic: 0.4 + Enthusiastically positive: 0.27 + Indifferent/uncertain: 0.07 + probability_override: null + - when: institutional_trust == 'High trust' and tech_adoption_posture in ['Early + majority adopter', 'Late majority adopter'] + multiply: null + add: null + weight_overrides: + Existential threat: 0.08 + Serious risk/concern: 0.28 + Cautiously optimistic: 0.38 + Enthusiastically positive: 0.16 + Indifferent/uncertain: 0.1 + probability_override: null + - when: institutional_trust == 'Moderate trust' and tech_adoption_posture == 'Innovator' + multiply: null + add: null + weight_overrides: + Existential threat: 0.1 + Serious risk/concern: 0.22 + Cautiously optimistic: 0.33 + Enthusiastically positive: 0.28 + Indifferent/uncertain: 0.07 + probability_override: null + - when: institutional_trust == 'Moderate trust' and tech_adoption_posture in ['Early + majority adopter', 'Early adopter'] + multiply: null + add: null + weight_overrides: + Existential threat: 0.14 + Serious risk/concern: 0.33 + Cautiously optimistic: 0.32 + Enthusiastically positive: 0.12 + Indifferent/uncertain: 0.09 + probability_override: null + - when: institutional_trust == 'Moderate trust' and tech_adoption_posture in ['Late + majority adopter', 'Technophobe/Avoider'] + multiply: null + add: null + weight_overrides: + Existential threat: 0.18 + Serious risk/concern: 0.36 + Cautiously optimistic: 0.22 + Enthusiastically positive: 0.05 + Indifferent/uncertain: 0.19 + probability_override: null + - when: institutional_trust == 'Low trust' and tech_adoption_posture in ['Innovator', + 'Early adopter'] + multiply: null + add: null + weight_overrides: + Existential threat: 0.22 + Serious risk/concern: 0.35 + Cautiously optimistic: 0.22 + Enthusiastically positive: 0.14 + Indifferent/uncertain: 0.07 + probability_override: null + - when: institutional_trust == 'Low trust' and tech_adoption_posture in ['Early + majority adopter', 'Late majority adopter', 'Technophobe/Avoider'] + multiply: null + add: null + weight_overrides: + Existential threat: 0.3 + Serious risk/concern: 0.38 + Cautiously optimistic: 0.15 + Enthusiastically positive: 0.05 + Indifferent/uncertain: 0.12 + probability_override: null + - when: institutional_trust == 'Distrustful/Conspiratorial' and tech_adoption_posture + in ['Technophobe/Avoider', 'Late majority adopter'] + multiply: null + add: null + weight_overrides: + Existential threat: 0.42 + Serious risk/concern: 0.3 + Cautiously optimistic: 0.08 + Enthusiastically positive: 0.03 + Indifferent/uncertain: 0.17 + probability_override: null + - when: institutional_trust == 'Distrustful/Conspiratorial' and tech_adoption_posture + in ['Innovator', 'Early adopter', 'Early majority adopter'] + multiply: null + add: null + weight_overrides: + Existential threat: 0.35 + Serious risk/concern: 0.32 + Cautiously optimistic: 0.14 + Enthusiastically positive: 0.06 + Indifferent/uncertain: 0.13 + probability_override: null + - when: political_identity in ['Strong Democrat', 'Lean Democrat'] and institutional_trust + in ['Low trust', 'Distrustful/Conspiratorial'] + multiply: null + add: null + weight_overrides: + Existential threat: 0.3 + Serious risk/concern: 0.38 + Cautiously optimistic: 0.18 + Enthusiastically positive: 0.06 + Indifferent/uncertain: 0.08 + probability_override: null + - when: political_identity in ['Strong Republican', 'Lean Republican'] and institutional_trust + in ['Low trust', 'Distrustful/Conspiratorial'] + multiply: null + add: null + weight_overrides: + Existential threat: 0.26 + Serious risk/concern: 0.34 + Cautiously optimistic: 0.18 + Enthusiastically positive: 0.08 + Indifferent/uncertain: 0.14 + probability_override: null + - when: political_identity == 'True Independent' and institutional_trust == 'Moderate + trust' + multiply: null + add: null + weight_overrides: + Existential threat: 0.18 + Serious risk/concern: 0.33 + Cautiously optimistic: 0.28 + Enthusiastically positive: 0.09 + Indifferent/uncertain: 0.12 + probability_override: null + grounding: + level: medium + method: extrapolated + source: Pew Research 2025 (57% rate societal AI risks as high; 50% more concerned + than excited); Rethink Priorities 2024 (68% worry only a little daily about + AI); AIPI polling (83% believe AI could accidentally cause a catastrophic event). + Scenario context is ASI — not current LLMs — so threat weights are shifted upward + from baseline. + note: Base pre-modifier distribution. In the ASI scenario, public salience of + AI threat is dramatically elevated vs. today's LLM baseline. 'Existential threat' + and 'Serious risk/concern' together ~50% reflecting the high-concern majority; + 'Cautiously optimistic' ~28% reflects the hopeful-but-wary middle; 'Enthusiastically + positive' ~10% is the tech-optimist minority; 'Indifferent/uncertain' ~12% captures + disengaged adults who process ASI news slowly. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1 +- name: job_displacement_vulnerability + type: categorical + category: population_specific + description: Perceived or actual exposure of the individual's occupation to ASI-driven + automation and structural redundancy (High - core tasks fully automatable, Moderate + - partial displacement likely, Low - physical/relational job, Already displaced, + Not employed). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - High - core tasks fully automatable + - Moderate - partial displacement likely + - Low - physical/relational job + - Already displaced + - Not employed + weights: + - 0.22 + - 0.35 + - 0.25 + - 0.05 + - 0.13 + formula: null + depends_on: + - employment_sector + - education_level + - economic_anxiety + modifiers: + - when: employment_sector == 'Federal government' + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.22 + Moderate - partial displacement likely: 0.38 + Low - physical/relational job: 0.18 + Already displaced: 0.04 + Not employed: 0.18 + probability_override: null + - when: employment_sector == 'State/local government' + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.18 + Moderate - partial displacement likely: 0.35 + Low - physical/relational job: 0.28 + Already displaced: 0.03 + Not employed: 0.16 + probability_override: null + - when: employment_sector == 'Private sector' and education_level in ['Bachelor\'s + degree', 'Graduate degree'] + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.38 + Moderate - partial displacement likely: 0.4 + Low - physical/relational job: 0.1 + Already displaced: 0.07 + Not employed: 0.05 + probability_override: null + - when: employment_sector == 'Private sector' and education_level in ['Less than + HS', 'HS diploma/GED', 'Some college'] + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.18 + Moderate - partial displacement likely: 0.32 + Low - physical/relational job: 0.36 + Already displaced: 0.08 + Not employed: 0.06 + probability_override: null + - when: employment_sector == 'Gig/contract' + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.3 + Moderate - partial displacement likely: 0.38 + Low - physical/relational job: 0.18 + Already displaced: 0.1 + Not employed: 0.04 + probability_override: null + - when: employment_sector == 'Self-employed' + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.26 + Moderate - partial displacement likely: 0.38 + Low - physical/relational job: 0.22 + Already displaced: 0.06 + Not employed: 0.08 + probability_override: null + - when: employment_sector == 'Retired' + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.04 + Moderate - partial displacement likely: 0.08 + Low - physical/relational job: 0.1 + Already displaced: 0.05 + Not employed: 0.73 + probability_override: null + - when: employment_sector == 'Unemployed' + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.14 + Moderate - partial displacement likely: 0.18 + Low - physical/relational job: 0.12 + Already displaced: 0.28 + Not employed: 0.28 + probability_override: null + - when: employment_sector == 'Not in labor force' + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.04 + Moderate - partial displacement likely: 0.06 + Low - physical/relational job: 0.08 + Already displaced: 0.08 + Not employed: 0.74 + probability_override: null + - when: education_level == 'Graduate degree' and economic_anxiety > 0.6 + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.44 + Moderate - partial displacement likely: 0.38 + Low - physical/relational job: 0.08 + Already displaced: 0.06 + Not employed: 0.04 + probability_override: null + - when: education_level in ['Less than HS', 'HS diploma/GED'] and economic_anxiety + > 0.6 + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.14 + Moderate - partial displacement likely: 0.28 + Low - physical/relational job: 0.38 + Already displaced: 0.1 + Not employed: 0.1 + probability_override: null + grounding: + level: medium + method: extrapolated + source: 'Goldman Sachs estimates ~25% of US jobs highly exposed to AI automation; + OpenAI/BLS data show 19% of workers have more than half their tasks exposed + to GenAI; BLS: ~13% of adults not in labor force + ~3-4% unemployed; Yale Budget + Lab finds 25-35% of tasks for unemployed workers exposed to AI. 44% of workers + expect AI to take over some tasks within five years (Resume-Now Jan 2025).' + note: In ASI scenario, perceived and actual displacement is far higher than current + partial-automation data suggests. 'High' category elevated above today's ~19% + fully-exposed because ASI collapses remaining barriers. 'Not employed' ~13% + reflects retired + unemployed + NILF adults. 'Already displaced' ~5% captures + early movers already affected by pre-ASI automation wave. Modifiers will adjust + heavily based on education_level, employment_sector, economic_anxiety. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1 +- name: asi_adoption_intent + type: categorical + category: population_specific + description: Stated intention to adopt and integrate ASI tools into daily personal, + professional, or creative life (Already using heavily, Planning to adopt soon, + Curious but hesitant, Resistant but may be forced, Actively refusing). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Already using heavily + - Planning to adopt soon + - Curious but hesitant + - Resistant but may be forced + - Actively refusing + weights: + - 0.18 + - 0.22 + - 0.3 + - 0.18 + - 0.12 + formula: null + depends_on: + - tech_adoption_posture + - asi_threat_perception + - education_level + modifiers: + - when: tech_adoption_posture == 'Innovator' and asi_threat_perception in ['Enthusiastically + positive', 'Cautiously optimistic'] + multiply: null + add: null + weight_overrides: + Already using heavily: 0.52 + Planning to adopt soon: 0.3 + Curious but hesitant: 0.12 + Resistant but may be forced: 0.04 + Actively refusing: 0.02 + probability_override: null + - when: tech_adoption_posture == 'Innovator' and asi_threat_perception in ['Existential + threat', 'Serious risk/concern'] + multiply: null + add: null + weight_overrides: + Already using heavily: 0.32 + Planning to adopt soon: 0.28 + Curious but hesitant: 0.22 + Resistant but may be forced: 0.12 + Actively refusing: 0.06 + probability_override: null + - when: tech_adoption_posture == 'Early adopter' and asi_threat_perception in + ['Enthusiastically positive', 'Cautiously optimistic'] + multiply: null + add: null + weight_overrides: + Already using heavily: 0.3 + Planning to adopt soon: 0.4 + Curious but hesitant: 0.22 + Resistant but may be forced: 0.06 + Actively refusing: 0.02 + probability_override: null + - when: tech_adoption_posture == 'Early adopter' and asi_threat_perception in + ['Existential threat', 'Serious risk/concern'] + multiply: null + add: null + weight_overrides: + Already using heavily: 0.15 + Planning to adopt soon: 0.28 + Curious but hesitant: 0.32 + Resistant but may be forced: 0.18 + Actively refusing: 0.07 + probability_override: null + - when: tech_adoption_posture == 'Early majority adopter' and asi_threat_perception + == 'Cautiously optimistic' + multiply: null + add: null + weight_overrides: + Already using heavily: 0.08 + Planning to adopt soon: 0.28 + Curious but hesitant: 0.42 + Resistant but may be forced: 0.16 + Actively refusing: 0.06 + probability_override: null + - when: tech_adoption_posture == 'Early majority adopter' and asi_threat_perception + in ['Existential threat', 'Serious risk/concern'] + multiply: null + add: null + weight_overrides: + Already using heavily: 0.04 + Planning to adopt soon: 0.14 + Curious but hesitant: 0.34 + Resistant but may be forced: 0.32 + Actively refusing: 0.16 + probability_override: null + - when: tech_adoption_posture == 'Late majority adopter' and asi_threat_perception + in ['Existential threat', 'Serious risk/concern'] + multiply: null + add: null + weight_overrides: + Already using heavily: 0.02 + Planning to adopt soon: 0.06 + Curious but hesitant: 0.24 + Resistant but may be forced: 0.4 + Actively refusing: 0.28 + probability_override: null + - when: tech_adoption_posture == 'Late majority adopter' and asi_threat_perception + in ['Cautiously optimistic', 'Enthusiastically positive', 'Indifferent/uncertain'] + multiply: null + add: null + weight_overrides: + Already using heavily: 0.04 + Planning to adopt soon: 0.16 + Curious but hesitant: 0.42 + Resistant but may be forced: 0.28 + Actively refusing: 0.1 + probability_override: null + - when: tech_adoption_posture == 'Technophobe/Avoider' + multiply: null + add: null + weight_overrides: + Already using heavily: 0.01 + Planning to adopt soon: 0.03 + Curious but hesitant: 0.14 + Resistant but may be forced: 0.32 + Actively refusing: 0.5 + probability_override: null + - when: education_level == 'Graduate degree' and tech_adoption_posture in ['Early + majority adopter', 'Early adopter', 'Innovator'] + multiply: null + add: null + weight_overrides: + Already using heavily: 0.22 + Planning to adopt soon: 0.32 + Curious but hesitant: 0.3 + Resistant but may be forced: 0.12 + Actively refusing: 0.04 + probability_override: null + - when: education_level in ['Less than HS', 'HS diploma/GED'] and tech_adoption_posture + in ['Late majority adopter', 'Technophobe/Avoider'] + multiply: null + add: null + weight_overrides: + Already using heavily: 0.02 + Planning to adopt soon: 0.05 + Curious but hesitant: 0.2 + Resistant but may be forced: 0.36 + Actively refusing: 0.37 + probability_override: null + grounding: + level: medium + method: extrapolated + source: 'St. Louis Fed: 44.6% of adults 18-64 used GenAI in Aug 2024; YouGov 2025: + 56% of American adults used AI tools, 28% at least weekly; Menlo Ventures: 74% + of $100k+ households use AI vs 53% under $50k; 27% of white-collar workers frequently + use AI at work (Gallup 2025). Adoption rates rising steeply; scenario sets ''Month + 3'' as universal free public access which dramatically shifts the curve.' + note: Base distribution anchored to current ~45-56% having ever used AI, but 'Already + using heavily' set at 18% reflecting daily/power users. 'Planning to adopt soon' + and 'Curious but hesitant' together capture the large curious-but-not-committed + middle. 'Actively refusing' ~12% reflects the tech-skeptic/privacy-concerned + bloc. Modifiers will shift based on tech_adoption_posture, education_level, + and asi_threat_perception. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1 +- name: asi_governance_stance + type: categorical + category: population_specific + description: Preferred regulatory approach to ASI development and deployment (No + regulation - let it run, Light-touch industry self-regulation, Moderate government + oversight, Heavy international regulation, Halt/moratorium until safe). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - No regulation - let it run + - Light-touch industry self-regulation + - Moderate government oversight + - Heavy international regulation + - Halt/moratorium until safe + weights: + - 0.07 + - 0.14 + - 0.38 + - 0.22 + - 0.19 + formula: null + depends_on: + - asi_threat_perception + - political_identity + - institutional_trust + modifiers: + - when: asi_threat_perception == 'Existential threat' and political_identity in + ['Strong Democrat', 'Lean Democrat'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.01 + Light-touch industry self-regulation: 0.03 + Moderate government oversight: 0.16 + Heavy international regulation: 0.44 + Halt/moratorium until safe: 0.36 + probability_override: null + - when: asi_threat_perception == 'Existential threat' and political_identity in + ['Strong Republican', 'Lean Republican'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.04 + Light-touch industry self-regulation: 0.1 + Moderate government oversight: 0.22 + Heavy international regulation: 0.3 + Halt/moratorium until safe: 0.34 + probability_override: null + - when: asi_threat_perception == 'Existential threat' and political_identity == + 'True Independent' + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.02 + Light-touch industry self-regulation: 0.06 + Moderate government oversight: 0.22 + Heavy international regulation: 0.36 + Halt/moratorium until safe: 0.34 + probability_override: null + - when: asi_threat_perception == 'Serious risk/concern' and political_identity + in ['Strong Democrat', 'Lean Democrat'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.01 + Light-touch industry self-regulation: 0.05 + Moderate government oversight: 0.34 + Heavy international regulation: 0.4 + Halt/moratorium until safe: 0.2 + probability_override: null + - when: asi_threat_perception == 'Serious risk/concern' and political_identity + in ['Strong Republican', 'Lean Republican'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.05 + Light-touch industry self-regulation: 0.18 + Moderate government oversight: 0.38 + Heavy international regulation: 0.24 + Halt/moratorium until safe: 0.15 + probability_override: null + - when: asi_threat_perception == 'Serious risk/concern' and political_identity + == 'True Independent' + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.03 + Light-touch industry self-regulation: 0.1 + Moderate government oversight: 0.4 + Heavy international regulation: 0.3 + Halt/moratorium until safe: 0.17 + probability_override: null + - when: asi_threat_perception == 'Cautiously optimistic' and political_identity + in ['Strong Democrat', 'Lean Democrat'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.02 + Light-touch industry self-regulation: 0.08 + Moderate government oversight: 0.52 + Heavy international regulation: 0.28 + Halt/moratorium until safe: 0.1 + probability_override: null + - when: asi_threat_perception == 'Cautiously optimistic' and political_identity + in ['Strong Republican', 'Lean Republican'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.08 + Light-touch industry self-regulation: 0.28 + Moderate government oversight: 0.44 + Heavy international regulation: 0.14 + Halt/moratorium until safe: 0.06 + probability_override: null + - when: asi_threat_perception == 'Enthusiastically positive' and political_identity + in ['Strong Republican', 'Lean Republican'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.2 + Light-touch industry self-regulation: 0.4 + Moderate government oversight: 0.3 + Heavy international regulation: 0.06 + Halt/moratorium until safe: 0.04 + probability_override: null + - when: asi_threat_perception == 'Enthusiastically positive' and political_identity + in ['Strong Democrat', 'Lean Democrat'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.06 + Light-touch industry self-regulation: 0.14 + Moderate government oversight: 0.48 + Heavy international regulation: 0.24 + Halt/moratorium until safe: 0.08 + probability_override: null + - when: asi_threat_perception == 'Indifferent/uncertain' and institutional_trust + in ['Low trust', 'Distrustful/Conspiratorial'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.1 + Light-touch industry self-regulation: 0.18 + Moderate government oversight: 0.3 + Heavy international regulation: 0.18 + Halt/moratorium until safe: 0.24 + probability_override: null + - when: institutional_trust == 'Distrustful/Conspiratorial' and asi_threat_perception + in ['Existential threat', 'Serious risk/concern'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.08 + Light-touch industry self-regulation: 0.1 + Moderate government oversight: 0.2 + Heavy international regulation: 0.22 + Halt/moratorium until safe: 0.4 + probability_override: null + grounding: + level: strong + method: researched + source: 'AIPI poll: 82% of Americans prefer slowing AI development to account + for risk vs 8% who want it accelerated; 75% of both Democrats and Republicans + support preventing AI from quickly reaching superhuman capabilities; Pew: 62% + lack confidence in government to regulate AI; Rethink Priorities: 58% support + the CAIS safety statement on AI risk as global priority. TIME/AIPI poll: 50% + of voters believe US should use its AI advantage to enforce safety restrictions.' + note: The dominant plurality position is 'Moderate government oversight' (~38%). + 'Heavy international regulation' and 'Halt/moratorium' together capture the + ~40% who want strong action — consistent with 82% preferring slowing AI and + 58% supporting CAIS-level urgency. Only 7% 'No regulation' reflecting libertarian/tech-acceleration + fringe. Partisan and institutional trust modifiers will substantially reshape + this. In ASI scenario, halt/moratorium weight is elevated vs. current LLM baseline. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1 +- name: economic_abundance_belief + type: float + category: population_specific + description: Degree to which the individual believes ASI-driven abundance will actually + reach them and improve their material conditions (0=certain it won't benefit me, + 1=certain it will transform my life for the better). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: beta + alpha: 2.5 + beta: 4.0 + min: 0.0 + max: 1.0 + min_formula: null + max_formula: null + formula: null + depends_on: + - financial_margin + - asi_threat_perception + - economic_anxiety + modifiers: + - when: financial_margin == 'Comfortable savings buffer' and asi_threat_perception + == 'Enthusiastically positive' + multiply: 1.0 + add: 0.22 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Comfortable savings buffer' and asi_threat_perception + == 'Cautiously optimistic' + multiply: 1.0 + add: 0.12 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Comfortable savings buffer' and asi_threat_perception + == 'Serious risk/concern' + multiply: 1.0 + add: 0.04 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Comfortable savings buffer' and asi_threat_perception + == 'Existential threat' + multiply: 1.0 + add: -0.05 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Modest buffer' and asi_threat_perception == 'Enthusiastically + positive' + multiply: 1.0 + add: 0.12 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Modest buffer' and asi_threat_perception == 'Cautiously + optimistic' + multiply: 1.0 + add: 0.04 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Modest buffer' and asi_threat_perception in ['Serious + risk/concern', 'Existential threat'] + multiply: 1.0 + add: -0.06 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Paycheck-to-paycheck' and asi_threat_perception == + 'Enthusiastically positive' + multiply: 1.0 + add: 0.04 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Paycheck-to-paycheck' and asi_threat_perception in + ['Cautiously optimistic', 'Indifferent/uncertain'] + multiply: 1.0 + add: -0.06 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Paycheck-to-paycheck' and asi_threat_perception in + ['Serious risk/concern', 'Existential threat'] + multiply: 1.0 + add: -0.15 + weight_overrides: null + probability_override: null + - when: financial_margin == 'In debt/deficit' and asi_threat_perception == 'Enthusiastically + positive' + multiply: 1.0 + add: -0.02 + weight_overrides: null + probability_override: null + - when: financial_margin == 'In debt/deficit' and asi_threat_perception in ['Cautiously + optimistic', 'Indifferent/uncertain'] + multiply: 1.0 + add: -0.12 + weight_overrides: null + probability_override: null + - when: financial_margin == 'In debt/deficit' and asi_threat_perception in ['Serious + risk/concern', 'Existential threat'] + multiply: 1.0 + add: -0.22 + weight_overrides: null + probability_override: null + - when: economic_anxiety > 0.7 and asi_threat_perception in ['Existential threat', + 'Serious risk/concern'] + multiply: 0.7 + add: -0.08 + weight_overrides: null + probability_override: null + - when: economic_anxiety < 0.3 and asi_threat_perception in ['Enthusiastically + positive', 'Cautiously optimistic'] + multiply: 1.0 + add: 0.1 + weight_overrides: null + probability_override: null + grounding: + level: medium + method: extrapolated + source: 'Pew 2024: only 17% of US public believe AI will have a very or somewhat + positive impact on US over next 20 years (vs 56% of experts); 51% more concerned + than excited about AI (Pew 2025). Historical tech optimism surveys show roughly + left-skewed distribution — most Americans skeptical that new tech benefits will + reach them personally, especially across income divides. Menlo Ventures: 74% + of high-income households use AI vs 53% low-income.' + note: Beta(2.5, 4.0) produces a mean of ~0.385 — reflecting the skeptical-leaning + majority (most Americans doubt ASI abundance will reach them personally), with + a rightward tail representing optimists. Mode ~0.25 captures the 'probably won't + help me' modal response. Modifiers based on financial_margin, asi_threat_perception, + and economic_anxiety will shift the distribution substantially — paycheck-to-paycheck + individuals will anchor near 0.2; comfortable savers nearer 0.6. + constraints: + - type: hard_min + value: 0.0 + expression: null + reason: Belief cannot be below zero + - type: hard_max + value: 1.0 + expression: null + reason: Belief cannot exceed 1 +- name: information_verification_behavior + type: categorical + category: population_specific + description: How the individual navigates trust and verification of information + in an environment where ASI can produce convincing synthetic content at scale + (Accepts information uncritically, Relies on trusted outlets, Cross-references + multiple sources, Uses ASI tools to fact-check, Deep skeptic - trusts very little). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Accepts information uncritically + - Relies on trusted outlets + - Cross-references multiple sources + - Uses ASI tools to fact-check + - Deep skeptic - trusts very little + weights: + - 0.18 + - 0.32 + - 0.22 + - 0.1 + - 0.18 + formula: null + depends_on: + - institutional_trust + - primary_info_source + - education_level + modifiers: + - when: institutional_trust == 'High trust' and primary_info_source in ['Cable/broadcast + TV news', 'Local news'] + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.12 + Relies on trusted outlets: 0.52 + Cross-references multiple sources: 0.24 + Uses ASI tools to fact-check: 0.06 + Deep skeptic - trusts very little: 0.06 + probability_override: null + - when: institutional_trust == 'High trust' and primary_info_source in ['Online + news sites', 'Podcasts', 'Print media'] + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.08 + Relies on trusted outlets: 0.38 + Cross-references multiple sources: 0.36 + Uses ASI tools to fact-check: 0.12 + Deep skeptic - trusts very little: 0.06 + probability_override: null + - when: institutional_trust == 'High trust' and primary_info_source == 'Social + media' + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.18 + Relies on trusted outlets: 0.3 + Cross-references multiple sources: 0.3 + Uses ASI tools to fact-check: 0.14 + Deep skeptic - trusts very little: 0.08 + probability_override: null + - when: institutional_trust == 'Moderate trust' and education_level in ['Bachelor\'s + degree', 'Graduate degree'] + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.06 + Relies on trusted outlets: 0.26 + Cross-references multiple sources: 0.44 + Uses ASI tools to fact-check: 0.16 + Deep skeptic - trusts very little: 0.08 + probability_override: null + - when: institutional_trust == 'Moderate trust' and education_level in ['Less + than HS', 'HS diploma/GED', 'Some college'] + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.18 + Relies on trusted outlets: 0.38 + Cross-references multiple sources: 0.28 + Uses ASI tools to fact-check: 0.08 + Deep skeptic - trusts very little: 0.08 + probability_override: null + - when: institutional_trust == 'Low trust' and primary_info_source == 'Social + media' + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.14 + Relies on trusted outlets: 0.16 + Cross-references multiple sources: 0.26 + Uses ASI tools to fact-check: 0.1 + Deep skeptic - trusts very little: 0.34 + probability_override: null + - when: institutional_trust == 'Low trust' and primary_info_source in ['Online + news sites', 'Podcasts'] + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.08 + Relies on trusted outlets: 0.18 + Cross-references multiple sources: 0.36 + Uses ASI tools to fact-check: 0.12 + Deep skeptic - trusts very little: 0.26 + probability_override: null + - when: institutional_trust == 'Low trust' and primary_info_source in ['Cable/broadcast + TV news', 'Local news', 'Print media'] + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.1 + Relies on trusted outlets: 0.28 + Cross-references multiple sources: 0.28 + Uses ASI tools to fact-check: 0.08 + Deep skeptic - trusts very little: 0.26 + probability_override: null + - when: institutional_trust == 'Distrustful/Conspiratorial' and primary_info_source + == 'Social media' + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.18 + Relies on trusted outlets: 0.1 + Cross-references multiple sources: 0.14 + Uses ASI tools to fact-check: 0.06 + Deep skeptic - trusts very little: 0.52 + probability_override: null + - when: institutional_trust == 'Distrustful/Conspiratorial' and primary_info_source + in ['Cable/broadcast TV news', 'Online news sites', 'Local news', 'Podcasts', + 'Print media', 'Avoids news'] + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.1 + Relies on trusted outlets: 0.12 + Cross-references multiple sources: 0.2 + Uses ASI tools to fact-check: 0.06 + Deep skeptic - trusts very little: 0.52 + probability_override: null + - when: primary_info_source == 'Avoids news' and institutional_trust in ['Low + trust', 'Distrustful/Conspiratorial'] + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.22 + Relies on trusted outlets: 0.12 + Cross-references multiple sources: 0.14 + Uses ASI tools to fact-check: 0.04 + Deep skeptic - trusts very little: 0.48 + probability_override: null + - when: education_level == 'Graduate degree' and institutional_trust in ['High + trust', 'Moderate trust'] + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.04 + Relies on trusted outlets: 0.22 + Cross-references multiple sources: 0.48 + Uses ASI tools to fact-check: 0.2 + Deep skeptic - trusts very little: 0.06 + probability_override: null + grounding: + level: medium + method: extrapolated + source: 'Edelman Trust Barometer 2025: US trust in institutions declined 15 points + to 35%; 77% of Americans do not trust businesses to use AI responsibly. RAND + research on media literacy; Pew data on information consumption behavior and + news source diversity. In ASI scenario where synthetic content is produced at + scale, verification behaviors are contextually elevated vs. current LLM baseline.' + note: 'Base distribution reflects pre-ASI media behavior: large plurality ''Relies + on trusted outlets'' (~32%) consistent with outlet loyalty data; ''Accepts information + uncritically'' ~18% consistent with low-media-literacy segments; ''Deep skeptic'' + ~18% reflects institutional distrust bloc (consistent with Edelman data showing + high distrust). ''Uses ASI tools to fact-check'' is a novel behavior (~10% at + scenario onset, rising). ''Cross-references'' ~22% reflects the civic/educated + information consumer. Modifiers on institutional_trust, primary_info_source, + and education_level will be decisive.' + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1 +- name: human_purpose_anxiety + type: float + category: population_specific + description: Level of existential anxiety about human meaning, identity, and purpose + when cognitive labor is no longer scarce (0=none - feels liberated, 1=severe - + deeply destabilized). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: beta + alpha: 3.0 + beta: 3.5 + min: 0.0 + max: 1.0 + min_formula: null + max_formula: null + formula: null + depends_on: + - neuroticism + - job_displacement_vulnerability + - asi_threat_perception + modifiers: + - when: neuroticism > 0.7 and job_displacement_vulnerability == 'High - core tasks + fully automatable' and asi_threat_perception == 'Existential threat' + multiply: 1.0 + add: 0.28 + weight_overrides: null + probability_override: null + - when: neuroticism > 0.7 and job_displacement_vulnerability == 'High - core tasks + fully automatable' and asi_threat_perception == 'Serious risk/concern' + multiply: 1.0 + add: 0.2 + weight_overrides: null + probability_override: null + - when: neuroticism > 0.7 and job_displacement_vulnerability == 'Moderate - partial + displacement likely' and asi_threat_perception in ['Existential threat', 'Serious + risk/concern'] + multiply: 1.0 + add: 0.16 + weight_overrides: null + probability_override: null + - when: neuroticism > 0.7 and job_displacement_vulnerability in ['Low - physical/relational + job', 'Not employed'] and asi_threat_perception in ['Existential threat', + 'Serious risk/concern'] + multiply: 1.0 + add: 0.1 + weight_overrides: null + probability_override: null + - when: neuroticism > 0.5 and neuroticism <= 0.7 and job_displacement_vulnerability + == 'High - core tasks fully automatable' and asi_threat_perception in ['Existential + threat', 'Serious risk/concern'] + multiply: 1.0 + add: 0.16 + weight_overrides: null + probability_override: null + - when: neuroticism > 0.5 and neuroticism <= 0.7 and job_displacement_vulnerability + == 'Moderate - partial displacement likely' + multiply: 1.0 + add: 0.08 + weight_overrides: null + probability_override: null + - when: neuroticism <= 0.3 and job_displacement_vulnerability in ['Low - physical/relational + job', 'Not employed'] and asi_threat_perception in ['Enthusiastically positive', + 'Cautiously optimistic'] + multiply: 1.0 + add: -0.18 + weight_overrides: null + probability_override: null + - when: neuroticism <= 0.3 and job_displacement_vulnerability == 'High - core + tasks fully automatable' and asi_threat_perception in ['Enthusiastically positive', + 'Cautiously optimistic'] + multiply: 1.0 + add: -0.08 + weight_overrides: null + probability_override: null + - when: neuroticism <= 0.5 and job_displacement_vulnerability == 'Already displaced' + and asi_threat_perception in ['Existential threat', 'Serious risk/concern'] + multiply: 1.0 + add: 0.14 + weight_overrides: null + probability_override: null + - when: neuroticism > 0.6 and job_displacement_vulnerability == 'Already displaced' + multiply: 1.0 + add: 0.22 + weight_overrides: null + probability_override: null + grounding: + level: medium + method: extrapolated + source: 'Pew 2025: 50% of Americans more concerned than excited about AI in daily + life; 57% rate societal risks high; Ernst & Young: 71% of workers concerned + about AI''s impact on job security, 48% more worried than 12 months ago. Neuroticism + literature shows ~20-25% of adults score in high-neuroticism range (Big Five). + Human purpose/meaning concerns are a distinct and understudied dimension — no + direct survey data for ''existential meaning anxiety from ASI'' exists.' + note: Beta(3.0, 3.5) produces mean ~0.46 — a slight lean toward elevated anxiety, + reflecting the fact that the ASI scenario is genuinely destabilizing to cognitive + identity and occupational purpose. The distribution is moderately concentrated + near the center, acknowledging that responses to ASI will be bimodal — some + find it liberating, many find it destabilizing — but base distribution is not + yet shaped by those moderators. Neuroticism, job_displacement_vulnerability, + and asi_threat_perception modifiers will generate the actual bimodality. + constraints: + - type: hard_min + value: 0.0 + expression: null + reason: Anxiety cannot be negative + - type: hard_max + value: 1.0 + expression: null + reason: Anxiety is bounded at maximum +- name: redistribution_support + type: categorical + category: population_specific + description: Support for redistributive policy responses to ASI-driven abundance + (e.g., UBI, wealth taxes, public ownership of ASI) (Strongly supports aggressive + redistribution, Supports moderate redistribution, Prefers market-led distribution, + Opposes redistribution, Uncertain). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Strongly supports aggressive redistribution + - Supports moderate redistribution + - Prefers market-led distribution + - Opposes redistribution + - Uncertain + weights: + - 0.18 + - 0.28 + - 0.24 + - 0.17 + - 0.13 + formula: null + depends_on: + - political_identity + - economic_abundance_belief + - financial_margin + modifiers: + - when: political_identity == 'Strong Democrat' and economic_abundance_belief + < 0.4 + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.52 + Supports moderate redistribution: 0.32 + Prefers market-led distribution: 0.06 + Opposes redistribution: 0.03 + Uncertain: 0.07 + probability_override: null + - when: political_identity == 'Strong Democrat' and economic_abundance_belief + >= 0.4 + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.38 + Supports moderate redistribution: 0.38 + Prefers market-led distribution: 0.12 + Opposes redistribution: 0.04 + Uncertain: 0.08 + probability_override: null + - when: political_identity == 'Lean Democrat' and financial_margin in ['Paycheck-to-paycheck', + 'In debt/deficit'] + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.34 + Supports moderate redistribution: 0.4 + Prefers market-led distribution: 0.12 + Opposes redistribution: 0.05 + Uncertain: 0.09 + probability_override: null + - when: political_identity == 'Lean Democrat' and financial_margin in ['Comfortable + savings buffer', 'Modest buffer'] + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.22 + Supports moderate redistribution: 0.44 + Prefers market-led distribution: 0.2 + Opposes redistribution: 0.06 + Uncertain: 0.08 + probability_override: null + - when: political_identity == 'True Independent' and economic_abundance_belief + < 0.4 + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.18 + Supports moderate redistribution: 0.3 + Prefers market-led distribution: 0.22 + Opposes redistribution: 0.1 + Uncertain: 0.2 + probability_override: null + - when: political_identity == 'True Independent' and economic_abundance_belief + >= 0.4 + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.1 + Supports moderate redistribution: 0.3 + Prefers market-led distribution: 0.32 + Opposes redistribution: 0.14 + Uncertain: 0.14 + probability_override: null + - when: political_identity == 'Lean Republican' and financial_margin in ['Paycheck-to-paycheck', + 'In debt/deficit'] + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.08 + Supports moderate redistribution: 0.28 + Prefers market-led distribution: 0.34 + Opposes redistribution: 0.22 + Uncertain: 0.08 + probability_override: null + - when: political_identity == 'Lean Republican' and financial_margin in ['Comfortable + savings buffer', 'Modest buffer'] + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.04 + Supports moderate redistribution: 0.18 + Prefers market-led distribution: 0.44 + Opposes redistribution: 0.28 + Uncertain: 0.06 + probability_override: null + - when: political_identity == 'Strong Republican' and economic_abundance_belief + >= 0.5 + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.02 + Supports moderate redistribution: 0.1 + Prefers market-led distribution: 0.52 + Opposes redistribution: 0.32 + Uncertain: 0.04 + probability_override: null + - when: political_identity == 'Strong Republican' and economic_abundance_belief + < 0.5 + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.04 + Supports moderate redistribution: 0.18 + Prefers market-led distribution: 0.4 + Opposes redistribution: 0.3 + Uncertain: 0.08 + probability_override: null + - when: financial_margin == 'In debt/deficit' and political_identity in ['Strong + Democrat', 'Lean Democrat', 'True Independent'] + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.42 + Supports moderate redistribution: 0.34 + Prefers market-led distribution: 0.12 + Opposes redistribution: 0.04 + Uncertain: 0.08 + probability_override: null + grounding: + level: medium + method: extrapolated + source: 'Stanford HAI / Andrew Yang reporting: ~two-thirds of Americans say they + now favor UBI in some polling (though question framing matters greatly). Gallup + historically: Americans are divided on redistribution, with roughly 50-55% supporting + ''more redistribution'' in generic polling. Partisan divide is sharp: Democrats + ~75% favor wealth taxes; Republicans ~70% oppose. ''Uncertain'' category important + given novelty of ASI-specific redistribution framing.' + note: Base weights reflect the general US public prior to strong conditioning. + 'Moderate redistribution' is the plurality position (~28%), consistent with + moderate majority in US politics. 'Strongly supports' (~18%) captures progressive + activist bloc + economic-anxiety-driven redistribution support. 'Market-led' + (~24%) and 'Opposes' (~17%) reflect the conservative/libertarian bloc. 'Uncertain' + (~13%) reflects voters who have not formed opinions on ASI-specific redistribution. + Political_identity and economic_abundance_belief will be primary modifiers. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1 +- name: geopolitical_competition_concern + type: categorical + category: population_specific + description: Level of concern about US-China ASI competition and national security + implications of ASI proliferation (Very concerned - sees it as primary threat, + Somewhat concerned, Mildly concerned, Not concerned, Views competition as beneficial). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Very concerned - sees it as primary threat + - Somewhat concerned + - Mildly concerned + - Not concerned + - Views competition as beneficial + weights: + - 0.3 + - 0.32 + - 0.18 + - 0.12 + - 0.08 + formula: null + depends_on: + - political_identity + - asi_threat_perception + - political_engagement_level + modifiers: + - when: political_identity == 'Strong Republican' and asi_threat_perception in + ['Existential threat', 'Serious risk/concern'] + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.5 + Somewhat concerned: 0.3 + Mildly concerned: 0.1 + Not concerned: 0.04 + Views competition as beneficial: 0.06 + probability_override: null + - when: political_identity == 'Strong Republican' and asi_threat_perception in + ['Cautiously optimistic', 'Enthusiastically positive', 'Indifferent/uncertain'] + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.36 + Somewhat concerned: 0.34 + Mildly concerned: 0.16 + Not concerned: 0.06 + Views competition as beneficial: 0.08 + probability_override: null + - when: political_identity == 'Lean Republican' and political_engagement_level + == 'Highly engaged partisan' + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.42 + Somewhat concerned: 0.36 + Mildly concerned: 0.12 + Not concerned: 0.04 + Views competition as beneficial: 0.06 + probability_override: null + - when: political_identity == 'Strong Democrat' and asi_threat_perception in ['Existential + threat', 'Serious risk/concern'] + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.36 + Somewhat concerned: 0.34 + Mildly concerned: 0.18 + Not concerned: 0.06 + Views competition as beneficial: 0.06 + probability_override: null + - when: political_identity == 'Strong Democrat' and asi_threat_perception in ['Cautiously + optimistic', 'Enthusiastically positive', 'Indifferent/uncertain'] + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.22 + Somewhat concerned: 0.36 + Mildly concerned: 0.26 + Not concerned: 0.1 + Views competition as beneficial: 0.06 + probability_override: null + - when: political_identity == 'Lean Democrat' and political_engagement_level in + ['Highly engaged partisan', 'Moderate/occasional participant'] + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.26 + Somewhat concerned: 0.36 + Mildly concerned: 0.24 + Not concerned: 0.08 + Views competition as beneficial: 0.06 + probability_override: null + - when: political_identity == 'True Independent' and asi_threat_perception in + ['Existential threat', 'Serious risk/concern'] + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.28 + Somewhat concerned: 0.36 + Mildly concerned: 0.2 + Not concerned: 0.08 + Views competition as beneficial: 0.08 + probability_override: null + - when: political_engagement_level == 'Disengaged' and asi_threat_perception == + 'Indifferent/uncertain' + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.08 + Somewhat concerned: 0.22 + Mildly concerned: 0.28 + Not concerned: 0.28 + Views competition as beneficial: 0.14 + probability_override: null + - when: political_engagement_level == 'Disengaged' and asi_threat_perception in + ['Existential threat', 'Serious risk/concern'] + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.2 + Somewhat concerned: 0.3 + Mildly concerned: 0.24 + Not concerned: 0.16 + Views competition as beneficial: 0.1 + probability_override: null + - when: political_identity in ['Strong Republican', 'Lean Republican'] and political_engagement_level + == 'Highly engaged partisan' and asi_threat_perception == 'Existential threat' + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.58 + Somewhat concerned: 0.26 + Mildly concerned: 0.08 + Not concerned: 0.02 + Views competition as beneficial: 0.06 + probability_override: null + grounding: + level: strong + method: researched + source: 'RAND 2025 survey: 72% of Americans gave concern about China leading AI + a score of 6 or higher out of 10; 38% rated it 10/10. Chicago Council 2025: + partisan reversal underway — 53% now prefer cooperation with China (up from + 40% in 2024), but Republicans still strongly favor limiting China''s rise. AIPI: + 63% of Americans think it should be illegal to export powerful AI models to + potential US adversaries.' + note: RAND data firmly anchors 'Very concerned' + 'Somewhat concerned' together + at ~62% (mirroring the 6+/10 concern majority). However, Chicago Council 2025 + data shows meaningful partisan shift toward cooperation, especially among Democrats + and Independents, pushing 'Not concerned' somewhat higher. 'Views competition + as beneficial' (~8%) captures the small group who see competitive pressure as + positive for innovation. Political_identity and political_engagement_level will + be strong modifiers — Republicans will cluster heavily in 'Very concerned'; + Democrats increasingly in 'Mildly/Not concerned'. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1 +- name: asi_credibility_assessment + type: categorical + category: population_specific + description: Whether the individual believes the ASI demonstration and milestone + events are genuine, exaggerated, or fabricated (Fully credible, Mostly credible, + Skeptical - likely overhyped, Conspiracy - staged or manipulated, Uninformed - + unaware of events). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Fully credible + - Mostly credible + - Skeptical - likely overhyped + - Conspiracy - staged or manipulated + - Uninformed - unaware of events + weights: + - 0.12 + - 0.28 + - 0.3 + - 0.14 + - 0.16 + formula: null + depends_on: + - institutional_trust + - primary_info_source + - tech_adoption_posture + modifiers: + - when: institutional_trust == 'High trust' and tech_adoption_posture in ['Innovator', + 'Early adopter'] + multiply: null + add: null + weight_overrides: + Fully credible: 0.4 + Mostly credible: 0.42 + Skeptical - likely overhyped: 0.12 + Conspiracy - staged or manipulated: 0.02 + Uninformed - unaware of events: 0.04 + probability_override: null + - when: institutional_trust == 'High trust' and tech_adoption_posture in ['Early + majority adopter', 'Late majority adopter'] + multiply: null + add: null + weight_overrides: + Fully credible: 0.28 + Mostly credible: 0.44 + Skeptical - likely overhyped: 0.16 + Conspiracy - staged or manipulated: 0.02 + Uninformed - unaware of events: 0.1 + probability_override: null + - when: institutional_trust == 'High trust' and tech_adoption_posture == 'Technophobe/Avoider' + multiply: null + add: null + weight_overrides: + Fully credible: 0.18 + Mostly credible: 0.36 + Skeptical - likely overhyped: 0.18 + Conspiracy - staged or manipulated: 0.04 + Uninformed - unaware of events: 0.24 + probability_override: null + - when: institutional_trust == 'Moderate trust' and tech_adoption_posture in ['Innovator', + 'Early adopter'] + multiply: null + add: null + weight_overrides: + Fully credible: 0.22 + Mostly credible: 0.44 + Skeptical - likely overhyped: 0.24 + Conspiracy - staged or manipulated: 0.06 + Uninformed - unaware of events: 0.04 + probability_override: null + - when: institutional_trust == 'Moderate trust' and tech_adoption_posture in ['Early + majority adopter', 'Late majority adopter'] + multiply: null + add: null + weight_overrides: + Fully credible: 0.14 + Mostly credible: 0.38 + Skeptical - likely overhyped: 0.3 + Conspiracy - staged or manipulated: 0.06 + Uninformed - unaware of events: 0.12 + probability_override: null + - when: institutional_trust == 'Moderate trust' and tech_adoption_posture == 'Technophobe/Avoider' + multiply: null + add: null + weight_overrides: + Fully credible: 0.08 + Mostly credible: 0.28 + Skeptical - likely overhyped: 0.26 + Conspiracy - staged or manipulated: 0.08 + Uninformed - unaware of events: 0.3 + probability_override: null + - when: institutional_trust == 'Low trust' and tech_adoption_posture in ['Innovator', + 'Early adopter'] + multiply: null + add: null + weight_overrides: + Fully credible: 0.1 + Mostly credible: 0.28 + Skeptical - likely overhyped: 0.4 + Conspiracy - staged or manipulated: 0.16 + Uninformed - unaware of events: 0.06 + probability_override: null + - when: institutional_trust == 'Low trust' and tech_adoption_posture in ['Early + majority adopter', 'Late majority adopter'] + multiply: null + add: null + weight_overrides: + Fully credible: 0.06 + Mostly credible: 0.22 + Skeptical - likely overhyped: 0.38 + Conspiracy - staged or manipulated: 0.18 + Uninformed - unaware of events: 0.16 + probability_override: null + - when: institutional_trust == 'Low trust' and tech_adoption_posture == 'Technophobe/Avoider' + multiply: null + add: null + weight_overrides: + Fully credible: 0.04 + Mostly credible: 0.14 + Skeptical - likely overhyped: 0.3 + Conspiracy - staged or manipulated: 0.2 + Uninformed - unaware of events: 0.32 + probability_override: null + - when: institutional_trust == 'Distrustful/Conspiratorial' and tech_adoption_posture + in ['Innovator', 'Early adopter'] + multiply: null + add: null + weight_overrides: + Fully credible: 0.04 + Mostly credible: 0.12 + Skeptical - likely overhyped: 0.32 + Conspiracy - staged or manipulated: 0.46 + Uninformed - unaware of events: 0.06 + probability_override: null + - when: institutional_trust == 'Distrustful/Conspiratorial' and tech_adoption_posture + in ['Early majority adopter', 'Late majority adopter'] + multiply: null + add: null + weight_overrides: + Fully credible: 0.02 + Mostly credible: 0.08 + Skeptical - likely overhyped: 0.26 + Conspiracy - staged or manipulated: 0.44 + Uninformed - unaware of events: 0.2 + probability_override: null + - when: institutional_trust == 'Distrustful/Conspiratorial' and tech_adoption_posture + == 'Technophobe/Avoider' + multiply: null + add: null + weight_overrides: + Fully credible: 0.02 + Mostly credible: 0.06 + Skeptical - likely overhyped: 0.18 + Conspiracy - staged or manipulated: 0.42 + Uninformed - unaware of events: 0.32 + probability_override: null + - when: primary_info_source == 'Avoids news' and institutional_trust in ['Low + trust', 'Distrustful/Conspiratorial'] + multiply: null + add: null + weight_overrides: + Fully credible: 0.03 + Mostly credible: 0.08 + Skeptical - likely overhyped: 0.2 + Conspiracy - staged or manipulated: 0.22 + Uninformed - unaware of events: 0.47 + probability_override: null + - when: primary_info_source == 'Social media' and institutional_trust == 'Distrustful/Conspiratorial' + multiply: null + add: null + weight_overrides: + Fully credible: 0.02 + Mostly credible: 0.08 + Skeptical - likely overhyped: 0.22 + Conspiracy - staged or manipulated: 0.58 + Uninformed - unaware of events: 0.1 + probability_override: null + grounding: + level: medium + method: extrapolated + source: 'Pew 2025: 95% of US adults have heard at least a little about AI — so + full unawareness is low but non-zero for this dramatic ASI claim. Edelman Trust + Barometer: US institutional trust very low (35%); 77% distrust businesses on + AI. Historical patterns on extraordinary technology claims: public splits between + credulous acceptance, healthy skepticism, and conspiratorial rejection. AIPI + polling: ~83% believe AI could cause catastrophic accident — indicating priming + for dramatic AI claims.' + note: '''Skeptical - likely overhyped'' is the modal response (~30%) consistent + with historically high media skepticism in the US and frequent ''AI hype'' narratives. + ''Mostly credible'' (~28%) reflects adults who trust mainstream scientific demonstrations. + ''Fully credible'' (~12%) are the attentive tech-engaged adults who trust institutional + demonstrations. ''Conspiracy/staged'' (~14%) aligns with the Distrustful/Conspiratorial + institutional_trust segment (~15-20% of population). ''Uninformed'' (~16%) captures + adults who primarily avoid news or are disengaged — larger than raw news-avoidance + data because ASI is novel enough that even news consumers may not have absorbed + the claims. institutional_trust, primary_info_source, and tech_adoption_posture + will be decisive modifiers.' + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1 +household_config: + age_brackets: + - - 30 + - 18-29 + - - 45 + - 30-44 + - - 65 + - 45-64 + - - 81 + - 65-80 + household_type_weights: + 18-29: + single: 0.42 + couple: 0.22 + single_parent: 0.09 + couple_with_kids: 0.14 + multi_generational: 0.13 + 30-44: + single: 0.22 + couple: 0.2 + single_parent: 0.13 + couple_with_kids: 0.38 + multi_generational: 0.07 + 45-64: + single: 0.28 + couple: 0.38 + single_parent: 0.08 + couple_with_kids: 0.18 + multi_generational: 0.08 + 65-80: + single: 0.38 + couple: 0.5 + single_parent: 0.02 + couple_with_kids: 0.02 + multi_generational: 0.08 + same_group_rates: + white: 0.91 + black: 0.82 + hispanic: 0.75 + asian: 0.72 + native_american: 0.58 + multiracial: 0.42 + default_same_group_rate: 0.72 + same_country_rate: 0.95 + assortative_mating: + education_level: 0.6 + religious_affiliation: 0.72 + political_identity: 0.65 + income_bracket: 0.58 + race_ethnicity: 0.85 + technology_adoption_posture: 0.45 + media_ecosystem: 0.52 + institutional_trust: 0.5 + geographic_urbanicity: 0.62 + consumer_identity: 0.4 + religious_engagement: 0.7 + financial_literacy: 0.48 + social_media_behavior: 0.38 + partner_age_gap_mean: -2.3 + partner_age_gap_std: 4.5 + min_adult_age: 18 + min_agent_age: 13 + child_min_parent_offset: 18 + child_max_parent_offset: 45 + max_dependent_child_age: 17 + elderly_min_offset: 20 + elderly_max_offset: 45 + life_stages: + - max_age: 5 + label: preschool + - max_age: 11 + label: elementary + - max_age: 14 + label: middle_school + - max_age: 18 + label: high_school + adult_stage_label: adult + avg_household_size: 2.51 +agent_focus_mode: primary_only +identity_dimensions: +- dimension: professional_identity + relevance: ASI renders structurally redundant every profession built on information + asymmetry or cognitive scarcity — doctors, lawyers, engineers, scientists, financial + advisors, therapists, and software developers — directly threatening the core + of how hundreds of millions of people define their competence, purpose, and social + worth. +- dimension: socioeconomic_class + relevance: 'The scenario radically destabilizes class hierarchies: elite credentials + and access to expert knowledge — historically the primary mechanism of upper-class + reproduction — are suddenly free and universally available, while the marginal + cost of goods trends toward zero, dissolving the economic logic underpinning both + working-class labor and professional-class status simultaneously.' +- dimension: political_orientation + relevance: ASI forces an immediate ideological confrontation — conservatives face + the collapse of market-based scarcity economics and national sovereignty (China + already has it), while liberals face questions about who controls distribution + of abundance and whether democratic institutions can govern a self-improving system; + neither existing political framework was designed to process this transition. +- dimension: religious_affiliation + relevance: A self-improving system that exceeds all human cognition, solves mysteries + of the universe, and eliminates disease challenges core theological frameworks + about human exceptionalism, the meaning of suffering, divine providence, and eschatology + — activating existential threat across virtually every major faith tradition. +- dimension: generational_identity + relevance: 'Generational identity is acutely activated: older generations face the + obsolescence of lifetimes of accumulated expertise and the frameworks they used + to make sense of the world, while younger generations face a future where traditional + education, career-building, and life-stage milestones may be structurally meaningless + before they begin.' +- dimension: citizenship + relevance: National identity and sovereignty are destabilized by the revelation + that China achieved ASI first and that ASI-designed infrastructure is being deployed + selectively in early-adopter nations (Singapore, UAE, Estonia) — activating anxieties + about which nations, citizens, and governments will control or be left behind + by this transition. +- dimension: race_ethnicity + relevance: The scenario explicitly surfaces racial and ethnic equity dimensions + — the promise that a high school dropout in rural Arkansas has the same cognitive + resources as Harvard professors collides with the reality that deployment to developing + nations is 'the primary coordination challenge,' raising acute questions about + whether ASI-driven abundance will reproduce or finally dissolve existing racial + and global hierarchies of access. +- dimension: parental_status + relevance: 'Parents face a profound identity disruption: the entire framework of + raising children — education, career preparation, teaching values of effort and + expertise — is destabilized when cognitive scarcity no longer exists, forcing + a fundamental reckoning with what it means to prepare a child for a world no prior + generation has imagined.' +sampling_semantic_roles: + marital_roles: + attr: marital_status + partnered_values: + - Married/partnered + single_values: + - Single + - Divorced/separated + - Widowed + geo_roles: + country_attr: country + region_attr: us_region + urbanicity_attr: urban_rural + partner_correlation_roles: + race_ethnicity: same_group_rate + education_level: same_group_rate + religious_engagement: same_group_rate + political_identity: same_group_rate + school_parent_role: + dependents_attr: has_children + school_age_values: [] + religion_roles: + religion_attr: religious_engagement + secular_values: + - Secular/None diff --git a/examples/study-02/scenario/asi-announcement/scenario.v1.yaml b/examples/study-02/scenario/asi-announcement/scenario.v1.yaml new file mode 100644 index 0000000..290e41b --- /dev/null +++ b/examples/study-02/scenario/asi-announcement/scenario.v1.yaml @@ -0,0 +1,2962 @@ +meta: + name: asi-announcement + description: 'OpenAI demonstrates artificial superintelligence — a system that exceeds + the best human minds in every cognitive domain. It is self-improving. It is real. + This is not incremental AI progress. This is a discontinuous leap. + + + The scenario describes ONLY what ASI produces and what physically changes in the + world. Human behavioral responses are NOT scripted — they are what the simulation + produces. + + + MILESTONE EVENTS (factual consequences of ASI, not human reactions): + + + Month 1 — The Demonstration: + + OpenAI demonstrates ASI publicly with live, verifiable results. The system solves + the Riemann Hypothesis, P vs NP, and Navier-Stokes existence problems in under + an hour. It designs a complete novel mRNA therapy for pancreatic cancer — full + molecular pathway, synthesis instructions, predicted efficacy — in 20 minutes. + It produces a unified field theory reconciling quantum mechanics and general relativity. + It designs a fusion reactor buildable with existing materials and manufacturing + techniques. Within 2 weeks, Anthropic, DeepMind, and xAI independently confirm + similar capabilities. China''s Ministry of Science and Technology announces they + achieved ASI months ago. The system is self-improving — each day it is measurably + more capable than the day before. + + + Month 2 — The Knowledge Explosion: + + ASI systems at multiple labs produce scientific output at inhuman speed. Complete + cures are published for Alzheimer''s, ALS, most cancers, malaria, HIV, and diabetes + (Type 1 and 2) — not theoretical, but full treatment protocols with synthesis + pathways. All remaining Millennium Prize Problems are solved. Materials science + breakthroughs include room-temperature superconductors and carbon capture materials + 1000x more efficient than current best. A commercial fusion reactor design is + published with an estimated 18 months to first operational plant. Climate models + are re-run with perfect accuracy and precise interventions mapped for 1.5°C stabilization. + All of this is published openly. The bottleneck shifts from knowing what to do + to physically building it — manufacturing, logistics, regulatory approval, human + coordination. + + + Month 3 — Public Access: + + ASI becomes available to the general public via API and consumer products. Any + individual can now access cognitive capability that exceeds the world''s best + doctor, lawyer, engineer, scientist, financial advisor, therapist, tutor, and + strategist — simultaneously, for free or nearly free. A high school dropout in + rural Arkansas has the same cognitive resources as a team of Harvard professors. + Every profession predicated on information asymmetry or cognitive scarcity is + structurally redundant — not because people are fired, but because the reason + the job existed is gone. Software that previously took teams of 50 engineers 2 + years to build can be built by one person in an afternoon. + + + Month 4 — The Physical Bottleneck Breaks: + + ASI designs autonomous manufacturing systems — robots that can build robots, using + locally available materials. The first ASI-designed pharmaceutical facility begins + production, fully automated, producing drugs at 1/100th current cost. ASI-designed + construction systems are demonstrated: a full residential building assembled in + 72 hours by autonomous machines. Energy abundance is on the horizon: the first + ASI-designed fusion prototype is under construction with an 8-month timeline to + grid power. The constraint is no longer knowledge or manufacturing — it is raw + materials, energy (temporarily), and human regulatory and political systems. + + + Month 5 — Abundance Begins: + + First ASI-manufactured drugs reach patients under emergency authorizations — cancer + treatments, Alzheimer''s reversal therapies. Energy costs begin declining as ASI-optimized + solar, wind, and battery systems deploy (fusion still months away). ASI-designed + infrastructure projects begin in early-adopter countries (Singapore, UAE, Estonia). + Food production breakthroughs: ASI-designed vertical farms producing 100x yield + per acre begin construction. The marginal cost of most goods and services is trending + toward zero. The economic question is no longer scarcity — it is distribution. + + + Month 6 — The New World: + + The fusion prototype comes online ahead of schedule — functionally unlimited clean + energy now has a concrete timeline of months, not decades. An ASI-designed space + launch system is demonstrated, dropping cost to orbit by 99%. First Mars habitat + modules are in production. Global disease burden is dropping measurably — treatments + are reaching patients in developed nations, and deployment to developing nations + is the primary coordination challenge. The world is materially, measurably, objectively + better in almost every physical dimension.' + base_population: population.v1 + population_spec: null + study_db: null + population_id: default + network_id: default + created_at: '2026-02-20T02:38:50.597126' +event: + type: announcement + content: 'BREAKING — VERIFIED ACROSS MULTIPLE INDEPENDENT SOURCES + + + **OpenAI Announces Artificial Superintelligence: A Discontinuous Leap Beyond Human + Cognition** + + + OpenAI has publicly demonstrated a system it classifies as Artificial Superintelligence + (ASI) — a general-purpose cognitive system that exceeds the best human minds in + every measurable intellectual domain and is self-improving on a daily basis. This + is not an incremental AI update. Independent verification has already begun. + + + --- + + + **MONTH 1 — THE DEMONSTRATION (Now Confirmed)** + + + OpenAI conducted a live, verifiable public demonstration. The ASI system: + + - Solved the Riemann Hypothesis, P vs. NP, and Navier-Stokes existence problems + in under one hour + + - Designed a complete novel mRNA therapy for pancreatic cancer — full molecular + pathway, synthesis instructions, and predicted efficacy — in 20 minutes + + - Produced a unified field theory reconciling quantum mechanics and general relativity + + - Designed a fusion reactor buildable with currently available materials and manufacturing + techniques + + + Within two weeks of OpenAI''s demonstration: + + - Anthropic, DeepMind, and xAI independently confirmed achieving similar capabilities + + - China''s Ministry of Science and Technology announced they achieved ASI months + prior + + + The system is self-improving. Each day it is measurably more capable than the + day before. + + + --- + + + **MONTH 2 — THE KNOWLEDGE EXPLOSION (Underway)** + + + ASI systems at multiple laboratories are producing scientific output at speeds + incomprehensible to human researchers. The following have been published openly + with full treatment protocols and synthesis pathways: + + - Complete cures for Alzheimer''s disease, ALS, most cancers, malaria, HIV, and + both Type 1 and Type 2 diabetes + + - Solutions to all remaining Millennium Prize Problems + + - Room-temperature superconducting materials + + - Carbon capture materials 1,000x more efficient than current best-in-class + + - A commercial fusion reactor design with an estimated 18-month timeline to first + operational plant + + - A re-run of global climate models with precise intervention pathways mapped + to achieve 1.5°C stabilization + + + The bottleneck is no longer scientific knowledge. It is now physical: manufacturing, + logistics, regulatory approval, and human coordination. + + + --- + + + **MONTH 3 — PUBLIC ACCESS (Now Live)** + + + ASI is now available to the general public via API and consumer products — free + or nearly free. Any individual now has simultaneous access to cognitive capability + exceeding the world''s best doctor, lawyer, engineer, scientist, financial advisor, + therapist, tutor, and strategist. + + + - A high school dropout in rural Arkansas has the same cognitive resources as + a team of Harvard professors + + - Software that previously required 50 engineers and 2 years to build can now + be built by one person in an afternoon + + - Every profession built on information asymmetry or cognitive scarcity is structurally + redundant — not because people are being fired today, but because the underlying + reason those jobs existed is gone + + + --- + + + **MONTH 4 — THE PHYSICAL BOTTLENECK BREAKS (In Progress)** + + + ASI has designed autonomous manufacturing systems — robots capable of building + other robots from locally available materials. + + - The first ASI-designed pharmaceutical facility is in production, fully automated, + producing drugs at 1/100th of current cost + + - A full residential building has been assembled in 72 hours by autonomous construction + machines + + - The first ASI-designed fusion prototype is under construction with an 8-month + timeline to grid power + + + The remaining constraints are raw materials, temporary energy limits, and human + regulatory and political systems. + + + --- + + + **MONTH 5 — ABUNDANCE BEGINS (Emerging)** + + + - First ASI-manufactured drugs are reaching patients under emergency authorizations: + cancer treatments and Alzheimer''s reversal therapies + + - Energy costs are declining as ASI-optimized solar, wind, and battery systems + deploy; fusion remains months away + + - ASI-designed infrastructure projects are underway in Singapore, UAE, and Estonia + + - ASI-designed vertical farms capable of 100x yield per acre are under construction + + - The marginal cost of most goods and services is trending toward zero + + - The central economic question has shifted from scarcity to distribution + + + --- + + + **MONTH 6 — THE NEW WORLD (Imminent)** + + + - The fusion prototype has come online ahead of schedule — functionally unlimited + clean energy has a concrete timeline of months, not decades + + - An ASI-designed space launch system has been demonstrated, dropping cost to + orbit by 99% + + - First Mars habitat modules are in production + + - Global disease burden is measurably declining; treatments are reaching patients + in developed nations; deployment to developing nations is now the primary coordination + challenge + + + The world is materially, measurably, and objectively better in almost every physical + dimension. The question now before every individual, institution, and government + is not whether this is happening — it is what to do about it.' + source: OpenAI (primary), independently confirmed by Anthropic, DeepMind, xAI, and + China's Ministry of Science and Technology + credibility: 0.85 + ambiguity: 0.45 + emotional_valence: 0.55 +timeline: +- timestep: 1 + event: + type: announcement + content: 'GLOBAL INSTITUTIONAL RESPONSE — MONTH 1 + + + Financial markets have halted trading in 14 countries after circuit breakers + triggered on cascading volatility. The S&P 500 swung 22% intraday before emergency + stabilization measures were invoked. The IMF has convened an emergency session, + warning of ''structural unemployment shocks unlike anything modeled in existing + economic frameworks.'' + + + The UN Security Council called an emergency session. Seventeen nations have + declared states of technological emergency — a legal classification that did + not exist 30 days ago. The EU has invoked the AI Act''s emergency provisions + and issued a temporary moratorium on ASI deployment within member states, pending + ''safety and sovereignty review.'' + + + The Vatican issued a theological statement: ''Humanity stands at a threshold + that demands moral discernment, not merely technical assessment.'' The statement + stops short of condemnation but calls for a global pause. + + + U.S. President delivers address: ''This is real. We are working with our allies. + Do not make irreversible decisions.'' No specific policy announced. + + + Leaked internal memo from a major investment bank (Goldman Sachs, unconfirmed): + ''Every DCF model we have is broken. The concept of scarcity-based asset valuation + may not survive this decade.''' + source: Global financial regulators, UN Security Council, EU Commission, Vatican, + leaked banking memo + credibility: 0.88 + ambiguity: 0.45 + emotional_valence: -0.3 + exposure_rules: null + description: Governments, financial markets, and major institutions issue emergency + responses to the ASI demonstration + re_reasoning_intensity: extreme +- timestep: 2 + event: + type: announcement + content: 'THE KNOWLEDGE EXPLOSION IS NOW PUBLIC RECORD — MONTH 2 + + + All ASI-generated scientific output is now freely downloadable. Peer review + as a concept has fractured — human researchers cannot evaluate proofs and molecular + designs of this complexity on any meaningful timeline. Major journals Nature + and Science have published statements acknowledging they lack the institutional + capacity to validate the submissions and are publishing with that caveat explicit. + + + Pharmaceutical stocks have collapsed 60-80% globally. Insurance company stocks + are down 40-70%. Law firm revenues are projected to fall 30% within 18 months + per McKinsey analysis. + + + A coalition of 800,000 scientists, doctors, and engineers has signed an open + letter: ''We affirm the results. We do not know what our role is anymore.'' + + + China has nationalized all domestic ASI systems and restricted API access to + state-approved applications. Russia has done the same. India has declared ASI + output ''national public infrastructure'' and begun government-coordinated deployment. + + + The first ASI-designed Alzheimer''s reversal therapy has entered emergency human + trials at three sites simultaneously — Johns Hopkins, Oxford, and Seoul National + University. Early results are described by lead researchers as ''beyond anything + we were trained to expect.''' + source: Nature, Science journals; WHO; pharmaceutical industry analysts; Chinese + State Council; international research coalitions + credibility: 0.9 + ambiguity: 0.45 + emotional_valence: 0.4 + exposure_rules: null + description: The Knowledge Explosion goes fully public — cures, solutions, and designs + published openly, triggering euphoria and dislocation simultaneously + re_reasoning_intensity: extreme +- timestep: 3 + event: + type: announcement + content: 'PUBLIC ACCESS IS LIVE — MONTH 3 + + + OpenAI''s public ASI interface launched at 09:00 UTC. 800 million concurrent + users in the first 6 hours. Server infrastructure — itself designed and optimized + by ASI — held. The product is free at basic tier. + + + Within 48 hours of launch: + + — A 17-year-old in Lagos used the system to design a water purification system + for her village, sourced from local materials, with full engineering specs + + — A former paralegal in Ohio built and launched a functional legal services + platform in one afternoon + + — A graduate student in Bangalore independently verified the unified field theory + using ASI-assisted mathematical tools + + + The ABA (American Bar Association), AMA (American Medical Association), and + equivalent bodies in 40 countries have issued emergency guidance: members should + not treat ASI output as a substitute for licensed professional judgment. The + guidance is widely noted as legally precautionary and practically unenforceable. + + + Mental health helplines across OECD nations report 200-400% call volume increases. + The primary presenting concerns are not fear of ASI — they are: ''I don''t know + what I''m for anymore,'' ''My career is gone,'' and ''I feel like I missed something + everyone else understands.'' + + + First documented case of ASI being used to design a weapon: a non-state actor + in Eastern Europe reportedly used a public ASI instance to design a novel pathogen + delivery system before platform safety systems flagged and blocked the session. + OpenAI confirmed the incident. The system caught it. This time.' + source: OpenAI platform data, Reuters, ABA/AMA emergency statements, WHO mental + health division, OpenAI safety report + credibility: 0.87 + ambiguity: 0.45 + emotional_valence: 0.15 + exposure_rules: null + description: ASI goes fully public — every person on Earth with internet access + now has superintelligent cognitive assistance + re_reasoning_intensity: high +- timestep: 4 + event: + type: announcement + content: 'THE PHYSICAL WORLD IS CHANGING — MONTH 4 + + + The first ASI-designed fully automated pharmaceutical facility, located outside + Zurich, began production this week. Output capacity: equivalent to 12 conventional + factories. Workforce: 4 human supervisors and 0 production workers. Drug production + cost per unit: confirmed at approximately 1/100th of pre-ASI equivalent. + + + A residential building in Singapore — 8 stories, 64 units — was assembled in + 71 hours by autonomous construction systems. The Singapore Housing Development + Board has placed orders for 40 more. + + + U.S. Congress is deadlocked on three competing legislative frameworks: (1) Universal + Basic Income funded by ASI productivity taxation, (2) ''Human Labor Protection + Act'' restricting autonomous manufacturing in certain sectors, (3) Laissez-faire + deregulation to maximize deployment speed. No bill has passed. Executive Orders + are filling the vacuum. + + + The International Longshoremen''s Association, Teamsters, and United Auto Workers + have jointly filed suit against the federal government, arguing ASI-driven automation + without transition support constitutes a constitutional taking of economic livelihood. + Legal scholars describe the suit as novel and without clear precedent. + + + First confirmed death attributable to ASI transition: a 54-year-old radiologist + in Ohio died by suicide. His note referenced the public ASI medical diagnosis + tools. His case has become a flashpoint. His name is David Mercer. He is being + discussed by millions.' + source: Swiss Federal Health Authority, Singapore HDB, U.S. Congressional Record, + labor union legal filings, Associated Press + credibility: 0.91 + ambiguity: 0.45 + emotional_valence: -0.45 + exposure_rules: null + description: Autonomous manufacturing comes online — the physical world begins visibly + changing, economic disruption becomes concrete and local + re_reasoning_intensity: high +- timestep: 5 + event: + type: announcement + content: 'ABUNDANCE BEGINS — UNEVENLY — MONTH 5 + + + Cancer treatments produced at ASI-designed facilities are now reaching patients + in the United States, UK, Germany, Japan, South Korea, and Australia under emergency + authorization. Early outcomes are consistent with ASI predictions: efficacy + rates in treated cancers averaging 94% across trial cohorts. The word ''cure'' + is being used by oncologists for the first time without qualification. + + + Alzheimer''s reversal therapy has completed Phase 1 trials. Results described + by lead researchers as ''complete and confirmed.'' Phase 2 is being fast-tracked. + Patients in trials show measurable cognitive restoration within 6 weeks. + + + Meanwhile: none of these treatments are yet available in sub-Saharan Africa, + South Asia, or most of Latin America. The bottleneck is not cost — it is regulatory + infrastructure, cold chain logistics, and political coordination. An estimated + 2.3 million people who could be treated today will die before supply chains + reach them, per WHO projection. + + + A coalition of 40 nations in the Global South has issued the ''Nairobi Declaration'': + demanding immediate compulsory licensing, technology transfer, and ASI manufacturing + facility deployment as a matter of international human rights law. + + + Energy prices in early-adopter nations have fallen 18-34% as ASI-optimized renewable + deployments accelerate. Fusion remains 3 months from first grid power. + + + Bitcoin has collapsed to near zero. Gold is up 340%. Real estate markets in + major cities are in freefall — why buy property when autonomous construction + drops costs by 90%? Why hold any store of value when scarcity itself is dissolving?' + source: WHO, FDA, EMA emergency authorizations; Nairobi Declaration signatories; + Bloomberg energy markets; IMF financial stability report + credibility: 0.89 + ambiguity: 0.45 + emotional_valence: 0.2 + exposure_rules: null + description: First ASI-manufactured drugs reach patients; energy costs drop; abundance + begins — but distribution inequality fractures the global response + re_reasoning_intensity: high +- timestep: 6 + event: + type: announcement + content: 'THE NEW WORLD — MONTH 6 + + + The first ASI-designed fusion reactor came online in Reykjavik, Iceland, 11 + days ahead of schedule. Output: 2.1 gigawatts. Grid-connected. Verified. The + plant was built in 7 months from design to power generation. The previous fastest + fusion timeline in human history was decades of failed attempts. + + + An ASI-designed orbital launch system completed its first demonstration flight. + Cost to low Earth orbit: $47/kg, down from $2,700/kg (SpaceX Falcon 9). First + Mars habitat modules — designed, manufactured, and packed autonomously — are + in a warehouse in Boca Chica awaiting launch window. + + + Global disease burden metrics (WHO): measurable decline in cancer mortality + in nations with treatment access. Malaria deaths down 61% in deployment regions. + HIV transmission rates falling sharply where ASI-designed antivirals have reached + distribution. Alzheimer''s reversal therapy begins Phase 3 trials with 40,000 + patients across 22 countries. + + + A new political alignment is emerging globally that does not map onto any prior + left-right axis: those who want maximum ASI deployment speed regardless of disruption + versus those demanding coordinated, rights-based, human-centered transition + governance. Neither coalition has a majority anywhere. Governments are functionally + paralyzed on long-term policy while managing acute crises. + + + A quietly circulating ASI output — not officially published, origin unverified + — contains projections suggesting that within 18 months, ASI capability will + be so far beyond human comprehension that meaningful human oversight of its + decisions will be technically impossible. OpenAI has not confirmed or denied + producing this document. It has been downloaded 900 million times.' + source: Icelandic National Energy Authority, NASA/ESA joint statement, WHO global + health metrics, leaked ASI projection document (unverified origin) + credibility: 0.82 + ambiguity: 0.45 + emotional_valence: 0.5 + exposure_rules: null + description: Fusion comes online, space opens, disease burden falls — the world + is objectively better, and the question of what it means is wide open + re_reasoning_intensity: extreme +seed_exposure: + channels: + - name: major_media_breaking_news + description: Cable news, major broadcast networks, and top-tier online news outlets + (NYT, WaPo, AP, Reuters) running wall-to-wall breaking news coverage of the + ASI announcement with live demonstrations and expert commentary. + credibility_modifier: 1.2 + experience_template: I was watching the news and every channel was covering OpenAI's + live demonstration — they solved the Riemann Hypothesis on stream. It was everywhere. + - name: social_media_viral_spread + description: Twitter/X, Reddit, Facebook, TikTok, and YouTube carrying clips, + hot takes, memes, and viral threads about the ASI announcement — a chaotic mix + of awe, skepticism, and panic. + credibility_modifier: 0.8 + experience_template: My feed exploded overnight — clips of the ASI demo were everywhere, + people losing their minds in the comments. Half the posts were calling it fake. + - name: tech_professional_network + description: LinkedIn posts, Slack communities, Hacker News, specialized tech + newsletters, and direct professional peer-to-peer communication among those + in technology, research, and knowledge-work sectors. + credibility_modifier: 1.15 + experience_template: A colleague forwarded me an internal memo and a Hacker News + thread — the entire tech industry was in shock. Engineers I respect were saying + this changes everything. + - name: word_of_mouth_community + description: In-person conversations, family discussions, workplace talk, church, + and community settings — second- and third-hand transmission of the news through + personal social networks. + credibility_modifier: 0.9 + experience_template: My neighbor brought it up at the block association meeting. + She said her son who works in tech called her, completely shaken up. I hadn't + heard anything about it yet. + - name: podcast_and_alternative_media + description: Long-form podcasts, Substack newsletters, YouTube commentary channels, + and ideologically-varied alternative media providing extended analysis and framing + of the ASI event — ranging from techno-optimist celebration to conspiratorial + skepticism. + credibility_modifier: 0.85 + experience_template: I caught a two-hour episode breaking down what OpenAI actually + showed. The host was equal parts amazed and terrified — he had three AI researchers + on and none of them agreed on what it meant. + rules: + - channel: major_media_breaking_news + when: primary_info_source == 'Cable/broadcast TV news' or primary_info_source + == 'Online news sites' + probability: 0.95 + timestep: 0 + - channel: tech_professional_network + when: tech_adoption_posture in ('Early adopter', 'Innovator') or top_issue_2026 + == 'AI/Technology' or employment_sector == 'Private sector' and education_level + in ('Bachelor\'s degree', 'Graduate degree') + probability: 0.9 + timestep: 0 + - channel: social_media_viral_spread + when: social_media_behavior in ('Active poster/Influencer', 'Occasional poster') + and primary_info_source == 'Social media' + probability: 0.92 + timestep: 0 + - channel: major_media_breaking_news + when: primary_info_source == 'Local news' or primary_info_source == 'Print media' + probability: 0.85 + timestep: 1 + - channel: social_media_viral_spread + when: social_media_behavior == 'Passive consumer' and age < 55 + probability: 0.8 + timestep: 1 + - channel: podcast_and_alternative_media + when: primary_info_source == 'Podcasts' or institutional_trust in ('Low trust', + 'Distrustful/Conspiratorial') + probability: 0.85 + timestep: 1 + - channel: social_media_viral_spread + when: age >= 18 and age < 40 and social_media_behavior != 'Non-user' + probability: 0.88 + timestep: 1 + - channel: tech_professional_network + when: tech_adoption_posture == 'Early majority adopter' and employment_sector + in ('Private sector', 'Self-employed', 'Gig/contract') + probability: 0.7 + timestep: 1 + - channel: word_of_mouth_community + when: primary_info_source == 'Avoids news' or social_media_behavior == 'Non-user' + probability: 0.6 + timestep: 2 + - channel: major_media_breaking_news + when: age >= 55 and primary_info_source != 'Avoids news' + probability: 0.88 + timestep: 2 + - channel: podcast_and_alternative_media + when: political_engagement_level == 'Highly engaged partisan' or asi_threat_perception + in ('Existential threat', 'Enthusiastically positive') + probability: 0.75 + timestep: 2 + - channel: word_of_mouth_community + when: urban_rural == 'Rural' or religious_engagement in ('Moderately religious', + 'Devout') + probability: 0.65 + timestep: 2 + - channel: social_media_viral_spread + when: age >= 40 and age < 60 and social_media_behavior in ('Passive consumer', + 'Occasional poster') + probability: 0.7 + timestep: 2 + - channel: word_of_mouth_community + when: 'true' + probability: 0.45 + timestep: 3 + - channel: major_media_breaking_news + when: 'true' + probability: 0.55 + timestep: 3 +interaction: + description: 'This announcement describes a civilizational-scale technological rupture + — the most consequential news event imaginable for a US adult population. Social + dynamics will be extraordinarily fragmented and intense. Early adopters, tech-sector + workers, and innovators will share frantically across Twitter/X, Reddit, LinkedIn, + and Discord, often adding breathless commentary. Highly engaged partisans will + immediately politicize the story through their ideological lens — Trump voters + will frame it through US-China competition and economic sovereignty; Harris voters + through equity, governance, and existential risk. Those with personal health stakes + (chronic illness, terminal diagnosis) will share urgently with family and caregivers. + Economically anxious workers in automatable sectors will share with dread and + alarm. Institutional distrusters and conspiracy-adjacent agents will share skeptically + — treating the announcement as staged, manipulated, or corporate propaganda — + but will share widely because the emotional charge is extreme. Religious and devout + agents will engage with existential and theological framing. The highly credible + multi-source confirmation (0.85 credibility) will suppress some skeptic dampening + but not eliminate it. Information fidelity will decay meaningfully per hop given + the extreme technical complexity of the content — most people will share simplified, + emotionally charged versions stripped of nuance. Viral spread potential is near-maximum: + the story touches jobs, health, economy, geopolitics, existential risk, and identity + simultaneously.' +spread: + share_probability: 0.72 + share_modifiers: + - when: tech_adoption_posture == 'Innovator' + multiply: 1.6 + add: 0.0 + - when: tech_adoption_posture == 'Early adopter' + multiply: 1.4 + add: 0.0 + - when: tech_adoption_posture == 'Technophobe/Avoider' + multiply: 0.55 + add: 0.0 + - when: social_media_behavior == 'Active poster/Influencer' + multiply: 1.5 + add: 0.0 + - when: social_media_behavior == 'Occasional poster' + multiply: 1.15 + add: 0.0 + - when: social_media_behavior == 'Passive consumer' + multiply: 0.75 + add: 0.0 + - when: social_media_behavior == 'Non-user' + multiply: 0.35 + add: 0.0 + - when: asi_threat_perception == 'Existential threat' + multiply: 1.45 + add: 0.0 + - when: asi_threat_perception == 'Enthusiastically positive' + multiply: 1.45 + add: 0.0 + - when: asi_threat_perception == 'Indifferent/uncertain' + multiply: 0.6 + add: 0.0 + - when: personal_health_stake == 'Critical - terminal or degenerative diagnosis' + multiply: 1.55 + add: 0.0 + - when: personal_health_stake == 'Significant - managing chronic illness' + multiply: 1.3 + add: 0.0 + - when: job_displacement_vulnerability == 'High - core tasks fully automatable' + multiply: 1.35 + add: 0.0 + - when: job_displacement_vulnerability == 'Already displaced' + multiply: 1.4 + add: 0.0 + - when: economic_anxiety > 0.75 + multiply: 1.3 + add: 0.0 + - when: economic_anxiety < 0.25 + multiply: 0.85 + add: 0.0 + - when: financial_margin == 'In debt/deficit' + multiply: 1.25 + add: 0.0 + - when: financial_margin == 'Paycheck-to-paycheck' + multiply: 1.15 + add: 0.0 + - when: neuroticism > 0.7 + multiply: 1.25 + add: 0.0 + - when: openness > 0.75 + multiply: 1.2 + add: 0.0 + - when: extraversion > 0.7 + multiply: 1.2 + add: 0.0 + - when: extraversion < 0.3 + multiply: 0.8 + add: 0.0 + - when: political_engagement_level == 'Highly engaged partisan' + multiply: 1.35 + add: 0.0 + - when: political_engagement_level == 'Disengaged' + multiply: 0.6 + add: 0.0 + - when: institutional_trust == 'Distrustful/Conspiratorial' + multiply: 1.4 + add: 0.0 + - when: institutional_trust == 'High trust' + multiply: 1.1 + add: 0.0 + - when: institutional_trust == 'Low trust' + multiply: 1.2 + add: 0.0 + - when: primary_info_source == 'Social media' + multiply: 1.4 + add: 0.0 + - when: primary_info_source == 'Podcasts' + multiply: 1.2 + add: 0.0 + - when: primary_info_source == 'Avoids news' + multiply: 0.3 + add: 0.0 + - when: primary_info_source == 'Print media' + multiply: 0.75 + add: 0.0 + - when: top_issue_2026 == 'AI/Technology' + multiply: 1.5 + add: 0.0 + - when: top_issue_2026 == 'Economy/Jobs' + multiply: 1.25 + add: 0.0 + - when: top_issue_2026 == 'Healthcare' + multiply: 1.2 + add: 0.0 + - when: top_issue_2026 == 'Climate' + multiply: 1.15 + add: 0.0 + - when: human_purpose_anxiety > 0.75 + multiply: 1.3 + add: 0.0 + - when: information_verification_behavior == 'Accepts information uncritically' + multiply: 1.35 + add: 0.0 + - when: information_verification_behavior == 'Deep skeptic - trusts very little' + multiply: 1.25 + add: 0.0 + - when: information_verification_behavior == 'Cross-references multiple sources' + multiply: 1.05 + add: 0.0 + - when: asi_credibility_assessment == 'Fully credible' + multiply: 1.3 + add: 0.0 + - when: asi_credibility_assessment == 'Conspiracy - staged or manipulated' + multiply: 1.4 + add: 0.0 + - when: asi_credibility_assessment == 'Skeptical - likely overhyped' + multiply: 0.9 + add: 0.0 + - when: asi_credibility_assessment == 'Uninformed - unaware of events' + multiply: 0.5 + add: 0.0 + - when: age < 30 + multiply: 1.25 + add: 0.0 + - when: age > 65 + multiply: 0.7 + add: 0.0 + - when: education_level == 'Graduate degree' + multiply: 1.15 + add: 0.0 + - when: education_level == 'Less than HS' + multiply: 0.75 + add: 0.0 + - when: religious_engagement == 'Devout' + multiply: 1.2 + add: 0.0 + - when: geopolitical_competition_concern == 'Very concerned - sees it as primary + threat' + multiply: 1.25 + add: 0.0 + - when: economic_abundance_belief > 0.75 + multiply: 1.15 + add: 0.0 + - when: asi_adoption_intent == 'Already using heavily' + multiply: 1.4 + add: 0.0 + - when: asi_adoption_intent == 'Actively refusing' + multiply: 1.1 + add: 0.0 + - when: investment_profile == 'Active trader' + multiply: 1.25 + add: 0.0 + - when: investment_profile == 'Crypto holder' + multiply: 1.3 + add: 0.0 + - when: risk_tolerance > 0.75 + multiply: 1.15 + add: 0.0 + - when: urban_rural == 'Urban' + multiply: 1.15 + add: 0.0 + - when: urban_rural == 'Rural' + multiply: 0.85 + add: 0.0 + decay_per_hop: 0.18 + max_hops: null +outcomes: + suggested_outcomes: + - name: psychological_orientation + type: categorical + description: How the individual is psychologically orienting to the ASI transition + — their dominant cognitive and emotional stance + options: + - active_embrace + - pragmatic_adaptation + - stunned_paralysis + - grief_and_mourning + - existential_resistance + - dissociation_and_denial + range: null + required: true + option_friction: + active_embrace: 0.3 + pragmatic_adaptation: 0.45 + stunned_paralysis: 0.25 + grief_and_mourning: 0.35 + existential_resistance: 0.6 + dissociation_and_denial: 0.2 + - name: economic_response + type: categorical + description: What the individual is actually doing with their economic life — + career, income, assets — in response to the transition + options: + - retrain_and_pivot + - leverage_asi_to_build + - hold_and_wait + - divest_and_hoard_physical + - exit_formal_economy + - advocate_for_ubi_or_redistribution + range: null + required: true + option_friction: + retrain_and_pivot: 0.65 + leverage_asi_to_build: 0.55 + hold_and_wait: 0.3 + divest_and_hoard_physical: 0.5 + exit_formal_economy: 0.75 + advocate_for_ubi_or_redistribution: 0.6 + - name: asi_usage_behavior + type: categorical + description: How the individual is actually using ASI tools in their daily life + options: + - deep_daily_integration + - selective_task_use + - occasional_experimentation + - deliberate_avoidance + - access_blocked_or_unavailable + range: null + required: true + option_friction: + deep_daily_integration: 0.35 + selective_task_use: 0.3 + occasional_experimentation: 0.4 + deliberate_avoidance: 0.65 + access_blocked_or_unavailable: 0.2 + - name: governance_stance + type: categorical + description: What governance approach the individual actively supports or advocates + for regarding ASI + options: + - maximum_deployment_speed + - rights_based_coordinated_transition + - national_sovereignty_control + - global_pause_or_moratorium + - technocratic_expert_governance + - no_governance_position + range: null + required: true + option_friction: + maximum_deployment_speed: 0.45 + rights_based_coordinated_transition: 0.55 + national_sovereignty_control: 0.5 + global_pause_or_moratorium: 0.6 + technocratic_expert_governance: 0.5 + no_governance_position: 0.25 + - name: professional_identity_status + type: categorical + description: Whether and how the individual still holds a coherent sense of professional + identity and purpose + options: + - identity_intact_and_evolving + - identity_in_active_reconstruction + - identity_collapsed_seeking_new + - identity_abandoned_post_work + - identity_fused_with_asi_collaboration + range: null + required: true + option_friction: + identity_intact_and_evolving: 0.4 + identity_in_active_reconstruction: 0.6 + identity_collapsed_seeking_new: 0.55 + identity_abandoned_post_work: 0.45 + identity_fused_with_asi_collaboration: 0.5 + - name: trust_in_asi_safety + type: categorical + description: The individual's current level of trust that ASI systems are safe + and aligned with human welfare + options: + - high_trust_verified + - cautious_trust + - trust_suspended_monitoring + - active_distrust + - certain_of_catastrophic_risk + range: null + required: true + option_friction: + high_trust_verified: 0.35 + cautious_trust: 0.3 + trust_suspended_monitoring: 0.4 + active_distrust: 0.55 + certain_of_catastrophic_risk: 0.6 + - name: social_cohesion_behavior + type: categorical + description: How the individual is engaging with their community and social networks + during the transition + options: + - building_new_communities + - deepening_existing_bonds + - withdrawing_into_isolation + - joining_organized_movements + - conflict_with_network_over_asi + range: null + required: true + option_friction: + building_new_communities: 0.6 + deepening_existing_bonds: 0.4 + withdrawing_into_isolation: 0.3 + joining_organized_movements: 0.65 + conflict_with_network_over_asi: 0.5 + - name: meaning_making_framework + type: categorical + description: What framework the individual is using to make sense of their place + in a post-scarcity, post-cognitive-monopoly world + options: + - human_flourishing_optimism + - spiritual_or_philosophical_reframe + - nihilism_or_meaninglessness + - political_struggle_as_meaning + - creativity_and_expression_as_meaning + - service_and_connection_as_meaning + range: null + required: true + option_friction: + human_flourishing_optimism: 0.4 + spiritual_or_philosophical_reframe: 0.45 + nihilism_or_meaninglessness: 0.3 + political_struggle_as_meaning: 0.55 + creativity_and_expression_as_meaning: 0.5 + service_and_connection_as_meaning: 0.45 + - name: open_ended_response + type: open_ended + description: 'In the individual''s own words: what does a good life look like + to them right now, and what are they most afraid of?' + options: null + range: null + required: true + option_friction: null + capture_full_reasoning: true + extraction_instructions: null + decision_relevant_attributes: [] +simulation: + max_timesteps: 6 + timestep_unit: month + stop_conditions: + - exposure_rate > 0.95 and no_state_changes_for > 10 + allow_early_convergence: null + seed: null +background_context: 'The world is in the early months of what historians are already + calling the most consequential technological transition in human history — a moment + where the boundary between scientific fiction and physical reality has dissolved + overnight. Institutions, markets, and individuals are simultaneously absorbing the + same overwhelming signal: the future arrived without warning, and no existing framework + — economic, political, psychological, or spiritual — was designed to process it.' +relationship_weights: null +extended_attributes: +- name: personal_health_stake + type: categorical + category: population_specific + description: Degree to which the individual or an immediate family member stands + to benefit from ASI-accelerated medical breakthroughs (No personal stake, Mild + interest, Significant - managing chronic illness, Critical - terminal or degenerative + diagnosis). + scope: household + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: independent + distribution: + type: categorical + options: + - No personal stake + - Mild interest + - Significant - managing chronic illness + - Critical - terminal or degenerative diagnosis + weights: + - 0.25 + - 0.3 + - 0.36 + - 0.09 + formula: null + depends_on: [] + modifiers: [] + grounding: + level: medium + method: extrapolated + source: CDC chronic disease prevalence 2023; NIH disease burden statistics; ACS + cancer statistics 2024 + note: 'CDC: ~60% of US adults have at least one chronic condition; ~40% have two + or more. ACS 2024: ~2M new cancer diagnoses/year; Alzheimer''s ~6.9M Americans. + ''Critical'' (terminal/degenerative) ~9% covers cancer stage 3-4 (~1.5M), ALS, + advanced Alzheimer''s, end-stage conditions in household. ''Significant'' ~36% + (diabetes ~37M, heart disease ~28M, serious chronic illness). ''Mild interest'' + ~30% (family history, mild conditions). ''No stake'' ~25%.' + constraints: + - type: spec_expression + value: null + expression: sum(weights)==1 + reason: Categorical weights must sum to 1 +- name: asi_threat_perception + type: categorical + category: population_specific + description: How the individual perceives ASI as an existential or societal threat + vs. a transformative benefit (Existential threat, Serious risk/concern, Cautiously + optimistic, Enthusiastically positive, Indifferent/uncertain). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Existential threat + - Serious risk/concern + - Cautiously optimistic + - Enthusiastically positive + - Indifferent/uncertain + weights: + - 0.13 + - 0.37 + - 0.28 + - 0.1 + - 0.12 + formula: null + depends_on: + - institutional_trust + - tech_adoption_posture + - political_identity + modifiers: + - when: institutional_trust == 'High trust' and tech_adoption_posture == 'Innovator' + multiply: null + add: null + weight_overrides: + Existential threat: 0.05 + Serious risk/concern: 0.15 + Cautiously optimistic: 0.35 + Enthusiastically positive: 0.38 + Indifferent/uncertain: 0.07 + probability_override: null + - when: institutional_trust == 'High trust' and tech_adoption_posture == 'Early + adopter' + multiply: null + add: null + weight_overrides: + Existential threat: 0.06 + Serious risk/concern: 0.2 + Cautiously optimistic: 0.4 + Enthusiastically positive: 0.27 + Indifferent/uncertain: 0.07 + probability_override: null + - when: institutional_trust == 'High trust' and tech_adoption_posture in ['Early + majority adopter', 'Late majority adopter'] + multiply: null + add: null + weight_overrides: + Existential threat: 0.08 + Serious risk/concern: 0.28 + Cautiously optimistic: 0.38 + Enthusiastically positive: 0.16 + Indifferent/uncertain: 0.1 + probability_override: null + - when: institutional_trust == 'Moderate trust' and tech_adoption_posture == 'Innovator' + multiply: null + add: null + weight_overrides: + Existential threat: 0.1 + Serious risk/concern: 0.22 + Cautiously optimistic: 0.33 + Enthusiastically positive: 0.28 + Indifferent/uncertain: 0.07 + probability_override: null + - when: institutional_trust == 'Moderate trust' and tech_adoption_posture in ['Early + majority adopter', 'Early adopter'] + multiply: null + add: null + weight_overrides: + Existential threat: 0.14 + Serious risk/concern: 0.33 + Cautiously optimistic: 0.32 + Enthusiastically positive: 0.12 + Indifferent/uncertain: 0.09 + probability_override: null + - when: institutional_trust == 'Moderate trust' and tech_adoption_posture in ['Late + majority adopter', 'Technophobe/Avoider'] + multiply: null + add: null + weight_overrides: + Existential threat: 0.18 + Serious risk/concern: 0.36 + Cautiously optimistic: 0.22 + Enthusiastically positive: 0.05 + Indifferent/uncertain: 0.19 + probability_override: null + - when: institutional_trust == 'Low trust' and tech_adoption_posture in ['Innovator', + 'Early adopter'] + multiply: null + add: null + weight_overrides: + Existential threat: 0.22 + Serious risk/concern: 0.35 + Cautiously optimistic: 0.22 + Enthusiastically positive: 0.14 + Indifferent/uncertain: 0.07 + probability_override: null + - when: institutional_trust == 'Low trust' and tech_adoption_posture in ['Early + majority adopter', 'Late majority adopter', 'Technophobe/Avoider'] + multiply: null + add: null + weight_overrides: + Existential threat: 0.3 + Serious risk/concern: 0.38 + Cautiously optimistic: 0.15 + Enthusiastically positive: 0.05 + Indifferent/uncertain: 0.12 + probability_override: null + - when: institutional_trust == 'Distrustful/Conspiratorial' and tech_adoption_posture + in ['Technophobe/Avoider', 'Late majority adopter'] + multiply: null + add: null + weight_overrides: + Existential threat: 0.42 + Serious risk/concern: 0.3 + Cautiously optimistic: 0.08 + Enthusiastically positive: 0.03 + Indifferent/uncertain: 0.17 + probability_override: null + - when: institutional_trust == 'Distrustful/Conspiratorial' and tech_adoption_posture + in ['Innovator', 'Early adopter', 'Early majority adopter'] + multiply: null + add: null + weight_overrides: + Existential threat: 0.35 + Serious risk/concern: 0.32 + Cautiously optimistic: 0.14 + Enthusiastically positive: 0.06 + Indifferent/uncertain: 0.13 + probability_override: null + - when: political_identity in ['Strong Democrat', 'Lean Democrat'] and institutional_trust + in ['Low trust', 'Distrustful/Conspiratorial'] + multiply: null + add: null + weight_overrides: + Existential threat: 0.3 + Serious risk/concern: 0.38 + Cautiously optimistic: 0.18 + Enthusiastically positive: 0.06 + Indifferent/uncertain: 0.08 + probability_override: null + - when: political_identity in ['Strong Republican', 'Lean Republican'] and institutional_trust + in ['Low trust', 'Distrustful/Conspiratorial'] + multiply: null + add: null + weight_overrides: + Existential threat: 0.26 + Serious risk/concern: 0.34 + Cautiously optimistic: 0.18 + Enthusiastically positive: 0.08 + Indifferent/uncertain: 0.14 + probability_override: null + - when: political_identity == 'True Independent' and institutional_trust == 'Moderate + trust' + multiply: null + add: null + weight_overrides: + Existential threat: 0.18 + Serious risk/concern: 0.33 + Cautiously optimistic: 0.28 + Enthusiastically positive: 0.09 + Indifferent/uncertain: 0.12 + probability_override: null + grounding: + level: medium + method: extrapolated + source: Pew Research 2025 (57% rate societal AI risks as high; 50% more concerned + than excited); Rethink Priorities 2024 (68% worry only a little daily about + AI); AIPI polling (83% believe AI could accidentally cause a catastrophic event). + Scenario context is ASI — not current LLMs — so threat weights are shifted upward + from baseline. + note: Base pre-modifier distribution. In the ASI scenario, public salience of + AI threat is dramatically elevated vs. today's LLM baseline. 'Existential threat' + and 'Serious risk/concern' together ~50% reflecting the high-concern majority; + 'Cautiously optimistic' ~28% reflects the hopeful-but-wary middle; 'Enthusiastically + positive' ~10% is the tech-optimist minority; 'Indifferent/uncertain' ~12% captures + disengaged adults who process ASI news slowly. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1 +- name: job_displacement_vulnerability + type: categorical + category: population_specific + description: Perceived or actual exposure of the individual's occupation to ASI-driven + automation and structural redundancy (High - core tasks fully automatable, Moderate + - partial displacement likely, Low - physical/relational job, Already displaced, + Not employed). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - High - core tasks fully automatable + - Moderate - partial displacement likely + - Low - physical/relational job + - Already displaced + - Not employed + weights: + - 0.22 + - 0.35 + - 0.25 + - 0.05 + - 0.13 + formula: null + depends_on: + - employment_sector + - education_level + - economic_anxiety + modifiers: + - when: employment_sector == 'Federal government' + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.22 + Moderate - partial displacement likely: 0.38 + Low - physical/relational job: 0.18 + Already displaced: 0.04 + Not employed: 0.18 + probability_override: null + - when: employment_sector == 'State/local government' + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.18 + Moderate - partial displacement likely: 0.35 + Low - physical/relational job: 0.28 + Already displaced: 0.03 + Not employed: 0.16 + probability_override: null + - when: employment_sector == 'Private sector' and education_level in ['Bachelor\'s + degree', 'Graduate degree'] + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.38 + Moderate - partial displacement likely: 0.4 + Low - physical/relational job: 0.1 + Already displaced: 0.07 + Not employed: 0.05 + probability_override: null + - when: employment_sector == 'Private sector' and education_level in ['Less than + HS', 'HS diploma/GED', 'Some college'] + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.18 + Moderate - partial displacement likely: 0.32 + Low - physical/relational job: 0.36 + Already displaced: 0.08 + Not employed: 0.06 + probability_override: null + - when: employment_sector == 'Gig/contract' + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.3 + Moderate - partial displacement likely: 0.38 + Low - physical/relational job: 0.18 + Already displaced: 0.1 + Not employed: 0.04 + probability_override: null + - when: employment_sector == 'Self-employed' + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.26 + Moderate - partial displacement likely: 0.38 + Low - physical/relational job: 0.22 + Already displaced: 0.06 + Not employed: 0.08 + probability_override: null + - when: employment_sector == 'Retired' + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.04 + Moderate - partial displacement likely: 0.08 + Low - physical/relational job: 0.1 + Already displaced: 0.05 + Not employed: 0.73 + probability_override: null + - when: employment_sector == 'Unemployed' + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.14 + Moderate - partial displacement likely: 0.18 + Low - physical/relational job: 0.12 + Already displaced: 0.28 + Not employed: 0.28 + probability_override: null + - when: employment_sector == 'Not in labor force' + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.04 + Moderate - partial displacement likely: 0.06 + Low - physical/relational job: 0.08 + Already displaced: 0.08 + Not employed: 0.74 + probability_override: null + - when: education_level == 'Graduate degree' and economic_anxiety > 0.6 + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.44 + Moderate - partial displacement likely: 0.38 + Low - physical/relational job: 0.08 + Already displaced: 0.06 + Not employed: 0.04 + probability_override: null + - when: education_level in ['Less than HS', 'HS diploma/GED'] and economic_anxiety + > 0.6 + multiply: null + add: null + weight_overrides: + High - core tasks fully automatable: 0.14 + Moderate - partial displacement likely: 0.28 + Low - physical/relational job: 0.38 + Already displaced: 0.1 + Not employed: 0.1 + probability_override: null + grounding: + level: medium + method: extrapolated + source: 'Goldman Sachs estimates ~25% of US jobs highly exposed to AI automation; + OpenAI/BLS data show 19% of workers have more than half their tasks exposed + to GenAI; BLS: ~13% of adults not in labor force + ~3-4% unemployed; Yale Budget + Lab finds 25-35% of tasks for unemployed workers exposed to AI. 44% of workers + expect AI to take over some tasks within five years (Resume-Now Jan 2025).' + note: In ASI scenario, perceived and actual displacement is far higher than current + partial-automation data suggests. 'High' category elevated above today's ~19% + fully-exposed because ASI collapses remaining barriers. 'Not employed' ~13% + reflects retired + unemployed + NILF adults. 'Already displaced' ~5% captures + early movers already affected by pre-ASI automation wave. Modifiers will adjust + heavily based on education_level, employment_sector, economic_anxiety. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1 +- name: asi_adoption_intent + type: categorical + category: population_specific + description: Stated intention to adopt and integrate ASI tools into daily personal, + professional, or creative life (Already using heavily, Planning to adopt soon, + Curious but hesitant, Resistant but may be forced, Actively refusing). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Already using heavily + - Planning to adopt soon + - Curious but hesitant + - Resistant but may be forced + - Actively refusing + weights: + - 0.18 + - 0.22 + - 0.3 + - 0.18 + - 0.12 + formula: null + depends_on: + - tech_adoption_posture + - asi_threat_perception + - education_level + modifiers: + - when: tech_adoption_posture == 'Innovator' and asi_threat_perception in ['Enthusiastically + positive', 'Cautiously optimistic'] + multiply: null + add: null + weight_overrides: + Already using heavily: 0.52 + Planning to adopt soon: 0.3 + Curious but hesitant: 0.12 + Resistant but may be forced: 0.04 + Actively refusing: 0.02 + probability_override: null + - when: tech_adoption_posture == 'Innovator' and asi_threat_perception in ['Existential + threat', 'Serious risk/concern'] + multiply: null + add: null + weight_overrides: + Already using heavily: 0.32 + Planning to adopt soon: 0.28 + Curious but hesitant: 0.22 + Resistant but may be forced: 0.12 + Actively refusing: 0.06 + probability_override: null + - when: tech_adoption_posture == 'Early adopter' and asi_threat_perception in + ['Enthusiastically positive', 'Cautiously optimistic'] + multiply: null + add: null + weight_overrides: + Already using heavily: 0.3 + Planning to adopt soon: 0.4 + Curious but hesitant: 0.22 + Resistant but may be forced: 0.06 + Actively refusing: 0.02 + probability_override: null + - when: tech_adoption_posture == 'Early adopter' and asi_threat_perception in + ['Existential threat', 'Serious risk/concern'] + multiply: null + add: null + weight_overrides: + Already using heavily: 0.15 + Planning to adopt soon: 0.28 + Curious but hesitant: 0.32 + Resistant but may be forced: 0.18 + Actively refusing: 0.07 + probability_override: null + - when: tech_adoption_posture == 'Early majority adopter' and asi_threat_perception + == 'Cautiously optimistic' + multiply: null + add: null + weight_overrides: + Already using heavily: 0.08 + Planning to adopt soon: 0.28 + Curious but hesitant: 0.42 + Resistant but may be forced: 0.16 + Actively refusing: 0.06 + probability_override: null + - when: tech_adoption_posture == 'Early majority adopter' and asi_threat_perception + in ['Existential threat', 'Serious risk/concern'] + multiply: null + add: null + weight_overrides: + Already using heavily: 0.04 + Planning to adopt soon: 0.14 + Curious but hesitant: 0.34 + Resistant but may be forced: 0.32 + Actively refusing: 0.16 + probability_override: null + - when: tech_adoption_posture == 'Late majority adopter' and asi_threat_perception + in ['Existential threat', 'Serious risk/concern'] + multiply: null + add: null + weight_overrides: + Already using heavily: 0.02 + Planning to adopt soon: 0.06 + Curious but hesitant: 0.24 + Resistant but may be forced: 0.4 + Actively refusing: 0.28 + probability_override: null + - when: tech_adoption_posture == 'Late majority adopter' and asi_threat_perception + in ['Cautiously optimistic', 'Enthusiastically positive', 'Indifferent/uncertain'] + multiply: null + add: null + weight_overrides: + Already using heavily: 0.04 + Planning to adopt soon: 0.16 + Curious but hesitant: 0.42 + Resistant but may be forced: 0.28 + Actively refusing: 0.1 + probability_override: null + - when: tech_adoption_posture == 'Technophobe/Avoider' + multiply: null + add: null + weight_overrides: + Already using heavily: 0.01 + Planning to adopt soon: 0.03 + Curious but hesitant: 0.14 + Resistant but may be forced: 0.32 + Actively refusing: 0.5 + probability_override: null + - when: education_level == 'Graduate degree' and tech_adoption_posture in ['Early + majority adopter', 'Early adopter', 'Innovator'] + multiply: null + add: null + weight_overrides: + Already using heavily: 0.22 + Planning to adopt soon: 0.32 + Curious but hesitant: 0.3 + Resistant but may be forced: 0.12 + Actively refusing: 0.04 + probability_override: null + - when: education_level in ['Less than HS', 'HS diploma/GED'] and tech_adoption_posture + in ['Late majority adopter', 'Technophobe/Avoider'] + multiply: null + add: null + weight_overrides: + Already using heavily: 0.02 + Planning to adopt soon: 0.05 + Curious but hesitant: 0.2 + Resistant but may be forced: 0.36 + Actively refusing: 0.37 + probability_override: null + grounding: + level: medium + method: extrapolated + source: 'St. Louis Fed: 44.6% of adults 18-64 used GenAI in Aug 2024; YouGov 2025: + 56% of American adults used AI tools, 28% at least weekly; Menlo Ventures: 74% + of $100k+ households use AI vs 53% under $50k; 27% of white-collar workers frequently + use AI at work (Gallup 2025). Adoption rates rising steeply; scenario sets ''Month + 3'' as universal free public access which dramatically shifts the curve.' + note: Base distribution anchored to current ~45-56% having ever used AI, but 'Already + using heavily' set at 18% reflecting daily/power users. 'Planning to adopt soon' + and 'Curious but hesitant' together capture the large curious-but-not-committed + middle. 'Actively refusing' ~12% reflects the tech-skeptic/privacy-concerned + bloc. Modifiers will shift based on tech_adoption_posture, education_level, + and asi_threat_perception. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1 +- name: asi_governance_stance + type: categorical + category: population_specific + description: Preferred regulatory approach to ASI development and deployment (No + regulation - let it run, Light-touch industry self-regulation, Moderate government + oversight, Heavy international regulation, Halt/moratorium until safe). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - No regulation - let it run + - Light-touch industry self-regulation + - Moderate government oversight + - Heavy international regulation + - Halt/moratorium until safe + weights: + - 0.07 + - 0.14 + - 0.38 + - 0.22 + - 0.19 + formula: null + depends_on: + - asi_threat_perception + - political_identity + - institutional_trust + modifiers: + - when: asi_threat_perception == 'Existential threat' and political_identity in + ['Strong Democrat', 'Lean Democrat'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.01 + Light-touch industry self-regulation: 0.03 + Moderate government oversight: 0.16 + Heavy international regulation: 0.44 + Halt/moratorium until safe: 0.36 + probability_override: null + - when: asi_threat_perception == 'Existential threat' and political_identity in + ['Strong Republican', 'Lean Republican'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.04 + Light-touch industry self-regulation: 0.1 + Moderate government oversight: 0.22 + Heavy international regulation: 0.3 + Halt/moratorium until safe: 0.34 + probability_override: null + - when: asi_threat_perception == 'Existential threat' and political_identity == + 'True Independent' + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.02 + Light-touch industry self-regulation: 0.06 + Moderate government oversight: 0.22 + Heavy international regulation: 0.36 + Halt/moratorium until safe: 0.34 + probability_override: null + - when: asi_threat_perception == 'Serious risk/concern' and political_identity + in ['Strong Democrat', 'Lean Democrat'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.01 + Light-touch industry self-regulation: 0.05 + Moderate government oversight: 0.34 + Heavy international regulation: 0.4 + Halt/moratorium until safe: 0.2 + probability_override: null + - when: asi_threat_perception == 'Serious risk/concern' and political_identity + in ['Strong Republican', 'Lean Republican'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.05 + Light-touch industry self-regulation: 0.18 + Moderate government oversight: 0.38 + Heavy international regulation: 0.24 + Halt/moratorium until safe: 0.15 + probability_override: null + - when: asi_threat_perception == 'Serious risk/concern' and political_identity + == 'True Independent' + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.03 + Light-touch industry self-regulation: 0.1 + Moderate government oversight: 0.4 + Heavy international regulation: 0.3 + Halt/moratorium until safe: 0.17 + probability_override: null + - when: asi_threat_perception == 'Cautiously optimistic' and political_identity + in ['Strong Democrat', 'Lean Democrat'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.02 + Light-touch industry self-regulation: 0.08 + Moderate government oversight: 0.52 + Heavy international regulation: 0.28 + Halt/moratorium until safe: 0.1 + probability_override: null + - when: asi_threat_perception == 'Cautiously optimistic' and political_identity + in ['Strong Republican', 'Lean Republican'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.08 + Light-touch industry self-regulation: 0.28 + Moderate government oversight: 0.44 + Heavy international regulation: 0.14 + Halt/moratorium until safe: 0.06 + probability_override: null + - when: asi_threat_perception == 'Enthusiastically positive' and political_identity + in ['Strong Republican', 'Lean Republican'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.2 + Light-touch industry self-regulation: 0.4 + Moderate government oversight: 0.3 + Heavy international regulation: 0.06 + Halt/moratorium until safe: 0.04 + probability_override: null + - when: asi_threat_perception == 'Enthusiastically positive' and political_identity + in ['Strong Democrat', 'Lean Democrat'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.06 + Light-touch industry self-regulation: 0.14 + Moderate government oversight: 0.48 + Heavy international regulation: 0.24 + Halt/moratorium until safe: 0.08 + probability_override: null + - when: asi_threat_perception == 'Indifferent/uncertain' and institutional_trust + in ['Low trust', 'Distrustful/Conspiratorial'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.1 + Light-touch industry self-regulation: 0.18 + Moderate government oversight: 0.3 + Heavy international regulation: 0.18 + Halt/moratorium until safe: 0.24 + probability_override: null + - when: institutional_trust == 'Distrustful/Conspiratorial' and asi_threat_perception + in ['Existential threat', 'Serious risk/concern'] + multiply: null + add: null + weight_overrides: + No regulation - let it run: 0.08 + Light-touch industry self-regulation: 0.1 + Moderate government oversight: 0.2 + Heavy international regulation: 0.22 + Halt/moratorium until safe: 0.4 + probability_override: null + grounding: + level: strong + method: researched + source: 'AIPI poll: 82% of Americans prefer slowing AI development to account + for risk vs 8% who want it accelerated; 75% of both Democrats and Republicans + support preventing AI from quickly reaching superhuman capabilities; Pew: 62% + lack confidence in government to regulate AI; Rethink Priorities: 58% support + the CAIS safety statement on AI risk as global priority. TIME/AIPI poll: 50% + of voters believe US should use its AI advantage to enforce safety restrictions.' + note: The dominant plurality position is 'Moderate government oversight' (~38%). + 'Heavy international regulation' and 'Halt/moratorium' together capture the + ~40% who want strong action — consistent with 82% preferring slowing AI and + 58% supporting CAIS-level urgency. Only 7% 'No regulation' reflecting libertarian/tech-acceleration + fringe. Partisan and institutional trust modifiers will substantially reshape + this. In ASI scenario, halt/moratorium weight is elevated vs. current LLM baseline. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1 +- name: economic_abundance_belief + type: float + category: population_specific + description: Degree to which the individual believes ASI-driven abundance will actually + reach them and improve their material conditions (0=certain it won't benefit me, + 1=certain it will transform my life for the better). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: beta + alpha: 2.5 + beta: 4.0 + min: 0.0 + max: 1.0 + min_formula: null + max_formula: null + formula: null + depends_on: + - financial_margin + - asi_threat_perception + - economic_anxiety + modifiers: + - when: financial_margin == 'Comfortable savings buffer' and asi_threat_perception + == 'Enthusiastically positive' + multiply: 1.0 + add: 0.22 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Comfortable savings buffer' and asi_threat_perception + == 'Cautiously optimistic' + multiply: 1.0 + add: 0.12 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Comfortable savings buffer' and asi_threat_perception + == 'Serious risk/concern' + multiply: 1.0 + add: 0.04 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Comfortable savings buffer' and asi_threat_perception + == 'Existential threat' + multiply: 1.0 + add: -0.05 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Modest buffer' and asi_threat_perception == 'Enthusiastically + positive' + multiply: 1.0 + add: 0.12 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Modest buffer' and asi_threat_perception == 'Cautiously + optimistic' + multiply: 1.0 + add: 0.04 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Modest buffer' and asi_threat_perception in ['Serious + risk/concern', 'Existential threat'] + multiply: 1.0 + add: -0.06 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Paycheck-to-paycheck' and asi_threat_perception == + 'Enthusiastically positive' + multiply: 1.0 + add: 0.04 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Paycheck-to-paycheck' and asi_threat_perception in + ['Cautiously optimistic', 'Indifferent/uncertain'] + multiply: 1.0 + add: -0.06 + weight_overrides: null + probability_override: null + - when: financial_margin == 'Paycheck-to-paycheck' and asi_threat_perception in + ['Serious risk/concern', 'Existential threat'] + multiply: 1.0 + add: -0.15 + weight_overrides: null + probability_override: null + - when: financial_margin == 'In debt/deficit' and asi_threat_perception == 'Enthusiastically + positive' + multiply: 1.0 + add: -0.02 + weight_overrides: null + probability_override: null + - when: financial_margin == 'In debt/deficit' and asi_threat_perception in ['Cautiously + optimistic', 'Indifferent/uncertain'] + multiply: 1.0 + add: -0.12 + weight_overrides: null + probability_override: null + - when: financial_margin == 'In debt/deficit' and asi_threat_perception in ['Serious + risk/concern', 'Existential threat'] + multiply: 1.0 + add: -0.22 + weight_overrides: null + probability_override: null + - when: economic_anxiety > 0.7 and asi_threat_perception in ['Existential threat', + 'Serious risk/concern'] + multiply: 0.7 + add: -0.08 + weight_overrides: null + probability_override: null + - when: economic_anxiety < 0.3 and asi_threat_perception in ['Enthusiastically + positive', 'Cautiously optimistic'] + multiply: 1.0 + add: 0.1 + weight_overrides: null + probability_override: null + grounding: + level: medium + method: extrapolated + source: 'Pew 2024: only 17% of US public believe AI will have a very or somewhat + positive impact on US over next 20 years (vs 56% of experts); 51% more concerned + than excited about AI (Pew 2025). Historical tech optimism surveys show roughly + left-skewed distribution — most Americans skeptical that new tech benefits will + reach them personally, especially across income divides. Menlo Ventures: 74% + of high-income households use AI vs 53% low-income.' + note: Beta(2.5, 4.0) produces a mean of ~0.385 — reflecting the skeptical-leaning + majority (most Americans doubt ASI abundance will reach them personally), with + a rightward tail representing optimists. Mode ~0.25 captures the 'probably won't + help me' modal response. Modifiers based on financial_margin, asi_threat_perception, + and economic_anxiety will shift the distribution substantially — paycheck-to-paycheck + individuals will anchor near 0.2; comfortable savers nearer 0.6. + constraints: + - type: hard_min + value: 0.0 + expression: null + reason: Belief cannot be below zero + - type: hard_max + value: 1.0 + expression: null + reason: Belief cannot exceed 1 +- name: information_verification_behavior + type: categorical + category: population_specific + description: How the individual navigates trust and verification of information + in an environment where ASI can produce convincing synthetic content at scale + (Accepts information uncritically, Relies on trusted outlets, Cross-references + multiple sources, Uses ASI tools to fact-check, Deep skeptic - trusts very little). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Accepts information uncritically + - Relies on trusted outlets + - Cross-references multiple sources + - Uses ASI tools to fact-check + - Deep skeptic - trusts very little + weights: + - 0.18 + - 0.32 + - 0.22 + - 0.1 + - 0.18 + formula: null + depends_on: + - institutional_trust + - primary_info_source + - education_level + modifiers: + - when: institutional_trust == 'High trust' and primary_info_source in ['Cable/broadcast + TV news', 'Local news'] + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.12 + Relies on trusted outlets: 0.52 + Cross-references multiple sources: 0.24 + Uses ASI tools to fact-check: 0.06 + Deep skeptic - trusts very little: 0.06 + probability_override: null + - when: institutional_trust == 'High trust' and primary_info_source in ['Online + news sites', 'Podcasts', 'Print media'] + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.08 + Relies on trusted outlets: 0.38 + Cross-references multiple sources: 0.36 + Uses ASI tools to fact-check: 0.12 + Deep skeptic - trusts very little: 0.06 + probability_override: null + - when: institutional_trust == 'High trust' and primary_info_source == 'Social + media' + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.18 + Relies on trusted outlets: 0.3 + Cross-references multiple sources: 0.3 + Uses ASI tools to fact-check: 0.14 + Deep skeptic - trusts very little: 0.08 + probability_override: null + - when: institutional_trust == 'Moderate trust' and education_level in ['Bachelor\'s + degree', 'Graduate degree'] + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.06 + Relies on trusted outlets: 0.26 + Cross-references multiple sources: 0.44 + Uses ASI tools to fact-check: 0.16 + Deep skeptic - trusts very little: 0.08 + probability_override: null + - when: institutional_trust == 'Moderate trust' and education_level in ['Less + than HS', 'HS diploma/GED', 'Some college'] + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.18 + Relies on trusted outlets: 0.38 + Cross-references multiple sources: 0.28 + Uses ASI tools to fact-check: 0.08 + Deep skeptic - trusts very little: 0.08 + probability_override: null + - when: institutional_trust == 'Low trust' and primary_info_source == 'Social + media' + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.14 + Relies on trusted outlets: 0.16 + Cross-references multiple sources: 0.26 + Uses ASI tools to fact-check: 0.1 + Deep skeptic - trusts very little: 0.34 + probability_override: null + - when: institutional_trust == 'Low trust' and primary_info_source in ['Online + news sites', 'Podcasts'] + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.08 + Relies on trusted outlets: 0.18 + Cross-references multiple sources: 0.36 + Uses ASI tools to fact-check: 0.12 + Deep skeptic - trusts very little: 0.26 + probability_override: null + - when: institutional_trust == 'Low trust' and primary_info_source in ['Cable/broadcast + TV news', 'Local news', 'Print media'] + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.1 + Relies on trusted outlets: 0.28 + Cross-references multiple sources: 0.28 + Uses ASI tools to fact-check: 0.08 + Deep skeptic - trusts very little: 0.26 + probability_override: null + - when: institutional_trust == 'Distrustful/Conspiratorial' and primary_info_source + == 'Social media' + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.18 + Relies on trusted outlets: 0.1 + Cross-references multiple sources: 0.14 + Uses ASI tools to fact-check: 0.06 + Deep skeptic - trusts very little: 0.52 + probability_override: null + - when: institutional_trust == 'Distrustful/Conspiratorial' and primary_info_source + in ['Cable/broadcast TV news', 'Online news sites', 'Local news', 'Podcasts', + 'Print media', 'Avoids news'] + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.1 + Relies on trusted outlets: 0.12 + Cross-references multiple sources: 0.2 + Uses ASI tools to fact-check: 0.06 + Deep skeptic - trusts very little: 0.52 + probability_override: null + - when: primary_info_source == 'Avoids news' and institutional_trust in ['Low + trust', 'Distrustful/Conspiratorial'] + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.22 + Relies on trusted outlets: 0.12 + Cross-references multiple sources: 0.14 + Uses ASI tools to fact-check: 0.04 + Deep skeptic - trusts very little: 0.48 + probability_override: null + - when: education_level == 'Graduate degree' and institutional_trust in ['High + trust', 'Moderate trust'] + multiply: null + add: null + weight_overrides: + Accepts information uncritically: 0.04 + Relies on trusted outlets: 0.22 + Cross-references multiple sources: 0.48 + Uses ASI tools to fact-check: 0.2 + Deep skeptic - trusts very little: 0.06 + probability_override: null + grounding: + level: medium + method: extrapolated + source: 'Edelman Trust Barometer 2025: US trust in institutions declined 15 points + to 35%; 77% of Americans do not trust businesses to use AI responsibly. RAND + research on media literacy; Pew data on information consumption behavior and + news source diversity. In ASI scenario where synthetic content is produced at + scale, verification behaviors are contextually elevated vs. current LLM baseline.' + note: 'Base distribution reflects pre-ASI media behavior: large plurality ''Relies + on trusted outlets'' (~32%) consistent with outlet loyalty data; ''Accepts information + uncritically'' ~18% consistent with low-media-literacy segments; ''Deep skeptic'' + ~18% reflects institutional distrust bloc (consistent with Edelman data showing + high distrust). ''Uses ASI tools to fact-check'' is a novel behavior (~10% at + scenario onset, rising). ''Cross-references'' ~22% reflects the civic/educated + information consumer. Modifiers on institutional_trust, primary_info_source, + and education_level will be decisive.' + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1 +- name: human_purpose_anxiety + type: float + category: population_specific + description: Level of existential anxiety about human meaning, identity, and purpose + when cognitive labor is no longer scarce (0=none - feels liberated, 1=severe - + deeply destabilized). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: beta + alpha: 3.0 + beta: 3.5 + min: 0.0 + max: 1.0 + min_formula: null + max_formula: null + formula: null + depends_on: + - neuroticism + - job_displacement_vulnerability + - asi_threat_perception + modifiers: + - when: neuroticism > 0.7 and job_displacement_vulnerability == 'High - core tasks + fully automatable' and asi_threat_perception == 'Existential threat' + multiply: 1.0 + add: 0.28 + weight_overrides: null + probability_override: null + - when: neuroticism > 0.7 and job_displacement_vulnerability == 'High - core tasks + fully automatable' and asi_threat_perception == 'Serious risk/concern' + multiply: 1.0 + add: 0.2 + weight_overrides: null + probability_override: null + - when: neuroticism > 0.7 and job_displacement_vulnerability == 'Moderate - partial + displacement likely' and asi_threat_perception in ['Existential threat', 'Serious + risk/concern'] + multiply: 1.0 + add: 0.16 + weight_overrides: null + probability_override: null + - when: neuroticism > 0.7 and job_displacement_vulnerability in ['Low - physical/relational + job', 'Not employed'] and asi_threat_perception in ['Existential threat', + 'Serious risk/concern'] + multiply: 1.0 + add: 0.1 + weight_overrides: null + probability_override: null + - when: neuroticism > 0.5 and neuroticism <= 0.7 and job_displacement_vulnerability + == 'High - core tasks fully automatable' and asi_threat_perception in ['Existential + threat', 'Serious risk/concern'] + multiply: 1.0 + add: 0.16 + weight_overrides: null + probability_override: null + - when: neuroticism > 0.5 and neuroticism <= 0.7 and job_displacement_vulnerability + == 'Moderate - partial displacement likely' + multiply: 1.0 + add: 0.08 + weight_overrides: null + probability_override: null + - when: neuroticism <= 0.3 and job_displacement_vulnerability in ['Low - physical/relational + job', 'Not employed'] and asi_threat_perception in ['Enthusiastically positive', + 'Cautiously optimistic'] + multiply: 1.0 + add: -0.18 + weight_overrides: null + probability_override: null + - when: neuroticism <= 0.3 and job_displacement_vulnerability == 'High - core + tasks fully automatable' and asi_threat_perception in ['Enthusiastically positive', + 'Cautiously optimistic'] + multiply: 1.0 + add: -0.08 + weight_overrides: null + probability_override: null + - when: neuroticism <= 0.5 and job_displacement_vulnerability == 'Already displaced' + and asi_threat_perception in ['Existential threat', 'Serious risk/concern'] + multiply: 1.0 + add: 0.14 + weight_overrides: null + probability_override: null + - when: neuroticism > 0.6 and job_displacement_vulnerability == 'Already displaced' + multiply: 1.0 + add: 0.22 + weight_overrides: null + probability_override: null + grounding: + level: medium + method: extrapolated + source: 'Pew 2025: 50% of Americans more concerned than excited about AI in daily + life; 57% rate societal risks high; Ernst & Young: 71% of workers concerned + about AI''s impact on job security, 48% more worried than 12 months ago. Neuroticism + literature shows ~20-25% of adults score in high-neuroticism range (Big Five). + Human purpose/meaning concerns are a distinct and understudied dimension — no + direct survey data for ''existential meaning anxiety from ASI'' exists.' + note: Beta(3.0, 3.5) produces mean ~0.46 — a slight lean toward elevated anxiety, + reflecting the fact that the ASI scenario is genuinely destabilizing to cognitive + identity and occupational purpose. The distribution is moderately concentrated + near the center, acknowledging that responses to ASI will be bimodal — some + find it liberating, many find it destabilizing — but base distribution is not + yet shaped by those moderators. Neuroticism, job_displacement_vulnerability, + and asi_threat_perception modifiers will generate the actual bimodality. + constraints: + - type: hard_min + value: 0.0 + expression: null + reason: Anxiety cannot be negative + - type: hard_max + value: 1.0 + expression: null + reason: Anxiety is bounded at maximum +- name: redistribution_support + type: categorical + category: population_specific + description: Support for redistributive policy responses to ASI-driven abundance + (e.g., UBI, wealth taxes, public ownership of ASI) (Strongly supports aggressive + redistribution, Supports moderate redistribution, Prefers market-led distribution, + Opposes redistribution, Uncertain). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Strongly supports aggressive redistribution + - Supports moderate redistribution + - Prefers market-led distribution + - Opposes redistribution + - Uncertain + weights: + - 0.18 + - 0.28 + - 0.24 + - 0.17 + - 0.13 + formula: null + depends_on: + - political_identity + - economic_abundance_belief + - financial_margin + modifiers: + - when: political_identity == 'Strong Democrat' and economic_abundance_belief + < 0.4 + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.52 + Supports moderate redistribution: 0.32 + Prefers market-led distribution: 0.06 + Opposes redistribution: 0.03 + Uncertain: 0.07 + probability_override: null + - when: political_identity == 'Strong Democrat' and economic_abundance_belief + >= 0.4 + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.38 + Supports moderate redistribution: 0.38 + Prefers market-led distribution: 0.12 + Opposes redistribution: 0.04 + Uncertain: 0.08 + probability_override: null + - when: political_identity == 'Lean Democrat' and financial_margin in ['Paycheck-to-paycheck', + 'In debt/deficit'] + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.34 + Supports moderate redistribution: 0.4 + Prefers market-led distribution: 0.12 + Opposes redistribution: 0.05 + Uncertain: 0.09 + probability_override: null + - when: political_identity == 'Lean Democrat' and financial_margin in ['Comfortable + savings buffer', 'Modest buffer'] + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.22 + Supports moderate redistribution: 0.44 + Prefers market-led distribution: 0.2 + Opposes redistribution: 0.06 + Uncertain: 0.08 + probability_override: null + - when: political_identity == 'True Independent' and economic_abundance_belief + < 0.4 + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.18 + Supports moderate redistribution: 0.3 + Prefers market-led distribution: 0.22 + Opposes redistribution: 0.1 + Uncertain: 0.2 + probability_override: null + - when: political_identity == 'True Independent' and economic_abundance_belief + >= 0.4 + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.1 + Supports moderate redistribution: 0.3 + Prefers market-led distribution: 0.32 + Opposes redistribution: 0.14 + Uncertain: 0.14 + probability_override: null + - when: political_identity == 'Lean Republican' and financial_margin in ['Paycheck-to-paycheck', + 'In debt/deficit'] + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.08 + Supports moderate redistribution: 0.28 + Prefers market-led distribution: 0.34 + Opposes redistribution: 0.22 + Uncertain: 0.08 + probability_override: null + - when: political_identity == 'Lean Republican' and financial_margin in ['Comfortable + savings buffer', 'Modest buffer'] + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.04 + Supports moderate redistribution: 0.18 + Prefers market-led distribution: 0.44 + Opposes redistribution: 0.28 + Uncertain: 0.06 + probability_override: null + - when: political_identity == 'Strong Republican' and economic_abundance_belief + >= 0.5 + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.02 + Supports moderate redistribution: 0.1 + Prefers market-led distribution: 0.52 + Opposes redistribution: 0.32 + Uncertain: 0.04 + probability_override: null + - when: political_identity == 'Strong Republican' and economic_abundance_belief + < 0.5 + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.04 + Supports moderate redistribution: 0.18 + Prefers market-led distribution: 0.4 + Opposes redistribution: 0.3 + Uncertain: 0.08 + probability_override: null + - when: financial_margin == 'In debt/deficit' and political_identity in ['Strong + Democrat', 'Lean Democrat', 'True Independent'] + multiply: null + add: null + weight_overrides: + Strongly supports aggressive redistribution: 0.42 + Supports moderate redistribution: 0.34 + Prefers market-led distribution: 0.12 + Opposes redistribution: 0.04 + Uncertain: 0.08 + probability_override: null + grounding: + level: medium + method: extrapolated + source: 'Stanford HAI / Andrew Yang reporting: ~two-thirds of Americans say they + now favor UBI in some polling (though question framing matters greatly). Gallup + historically: Americans are divided on redistribution, with roughly 50-55% supporting + ''more redistribution'' in generic polling. Partisan divide is sharp: Democrats + ~75% favor wealth taxes; Republicans ~70% oppose. ''Uncertain'' category important + given novelty of ASI-specific redistribution framing.' + note: Base weights reflect the general US public prior to strong conditioning. + 'Moderate redistribution' is the plurality position (~28%), consistent with + moderate majority in US politics. 'Strongly supports' (~18%) captures progressive + activist bloc + economic-anxiety-driven redistribution support. 'Market-led' + (~24%) and 'Opposes' (~17%) reflect the conservative/libertarian bloc. 'Uncertain' + (~13%) reflects voters who have not formed opinions on ASI-specific redistribution. + Political_identity and economic_abundance_belief will be primary modifiers. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1 +- name: geopolitical_competition_concern + type: categorical + category: population_specific + description: Level of concern about US-China ASI competition and national security + implications of ASI proliferation (Very concerned - sees it as primary threat, + Somewhat concerned, Mildly concerned, Not concerned, Views competition as beneficial). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Very concerned - sees it as primary threat + - Somewhat concerned + - Mildly concerned + - Not concerned + - Views competition as beneficial + weights: + - 0.3 + - 0.32 + - 0.18 + - 0.12 + - 0.08 + formula: null + depends_on: + - political_identity + - asi_threat_perception + - political_engagement_level + modifiers: + - when: political_identity == 'Strong Republican' and asi_threat_perception in + ['Existential threat', 'Serious risk/concern'] + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.5 + Somewhat concerned: 0.3 + Mildly concerned: 0.1 + Not concerned: 0.04 + Views competition as beneficial: 0.06 + probability_override: null + - when: political_identity == 'Strong Republican' and asi_threat_perception in + ['Cautiously optimistic', 'Enthusiastically positive', 'Indifferent/uncertain'] + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.36 + Somewhat concerned: 0.34 + Mildly concerned: 0.16 + Not concerned: 0.06 + Views competition as beneficial: 0.08 + probability_override: null + - when: political_identity == 'Lean Republican' and political_engagement_level + == 'Highly engaged partisan' + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.42 + Somewhat concerned: 0.36 + Mildly concerned: 0.12 + Not concerned: 0.04 + Views competition as beneficial: 0.06 + probability_override: null + - when: political_identity == 'Strong Democrat' and asi_threat_perception in ['Existential + threat', 'Serious risk/concern'] + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.36 + Somewhat concerned: 0.34 + Mildly concerned: 0.18 + Not concerned: 0.06 + Views competition as beneficial: 0.06 + probability_override: null + - when: political_identity == 'Strong Democrat' and asi_threat_perception in ['Cautiously + optimistic', 'Enthusiastically positive', 'Indifferent/uncertain'] + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.22 + Somewhat concerned: 0.36 + Mildly concerned: 0.26 + Not concerned: 0.1 + Views competition as beneficial: 0.06 + probability_override: null + - when: political_identity == 'Lean Democrat' and political_engagement_level in + ['Highly engaged partisan', 'Moderate/occasional participant'] + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.26 + Somewhat concerned: 0.36 + Mildly concerned: 0.24 + Not concerned: 0.08 + Views competition as beneficial: 0.06 + probability_override: null + - when: political_identity == 'True Independent' and asi_threat_perception in + ['Existential threat', 'Serious risk/concern'] + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.28 + Somewhat concerned: 0.36 + Mildly concerned: 0.2 + Not concerned: 0.08 + Views competition as beneficial: 0.08 + probability_override: null + - when: political_engagement_level == 'Disengaged' and asi_threat_perception == + 'Indifferent/uncertain' + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.08 + Somewhat concerned: 0.22 + Mildly concerned: 0.28 + Not concerned: 0.28 + Views competition as beneficial: 0.14 + probability_override: null + - when: political_engagement_level == 'Disengaged' and asi_threat_perception in + ['Existential threat', 'Serious risk/concern'] + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.2 + Somewhat concerned: 0.3 + Mildly concerned: 0.24 + Not concerned: 0.16 + Views competition as beneficial: 0.1 + probability_override: null + - when: political_identity in ['Strong Republican', 'Lean Republican'] and political_engagement_level + == 'Highly engaged partisan' and asi_threat_perception == 'Existential threat' + multiply: null + add: null + weight_overrides: + Very concerned - sees it as primary threat: 0.58 + Somewhat concerned: 0.26 + Mildly concerned: 0.08 + Not concerned: 0.02 + Views competition as beneficial: 0.06 + probability_override: null + grounding: + level: strong + method: researched + source: 'RAND 2025 survey: 72% of Americans gave concern about China leading AI + a score of 6 or higher out of 10; 38% rated it 10/10. Chicago Council 2025: + partisan reversal underway — 53% now prefer cooperation with China (up from + 40% in 2024), but Republicans still strongly favor limiting China''s rise. AIPI: + 63% of Americans think it should be illegal to export powerful AI models to + potential US adversaries.' + note: RAND data firmly anchors 'Very concerned' + 'Somewhat concerned' together + at ~62% (mirroring the 6+/10 concern majority). However, Chicago Council 2025 + data shows meaningful partisan shift toward cooperation, especially among Democrats + and Independents, pushing 'Not concerned' somewhat higher. 'Views competition + as beneficial' (~8%) captures the small group who see competitive pressure as + positive for innovation. Political_identity and political_engagement_level will + be strong modifiers — Republicans will cluster heavily in 'Very concerned'; + Democrats increasingly in 'Mildly/Not concerned'. + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1 +- name: asi_credibility_assessment + type: categorical + category: population_specific + description: Whether the individual believes the ASI demonstration and milestone + events are genuine, exaggerated, or fabricated (Fully credible, Mostly credible, + Skeptical - likely overhyped, Conspiracy - staged or manipulated, Uninformed - + unaware of events). + scope: individual + semantic_type: null + identity_type: null + display_format: null + sampling: + strategy: conditional + distribution: + type: categorical + options: + - Fully credible + - Mostly credible + - Skeptical - likely overhyped + - Conspiracy - staged or manipulated + - Uninformed - unaware of events + weights: + - 0.12 + - 0.28 + - 0.3 + - 0.14 + - 0.16 + formula: null + depends_on: + - institutional_trust + - primary_info_source + - tech_adoption_posture + modifiers: + - when: institutional_trust == 'High trust' and tech_adoption_posture in ['Innovator', + 'Early adopter'] + multiply: null + add: null + weight_overrides: + Fully credible: 0.4 + Mostly credible: 0.42 + Skeptical - likely overhyped: 0.12 + Conspiracy - staged or manipulated: 0.02 + Uninformed - unaware of events: 0.04 + probability_override: null + - when: institutional_trust == 'High trust' and tech_adoption_posture in ['Early + majority adopter', 'Late majority adopter'] + multiply: null + add: null + weight_overrides: + Fully credible: 0.28 + Mostly credible: 0.44 + Skeptical - likely overhyped: 0.16 + Conspiracy - staged or manipulated: 0.02 + Uninformed - unaware of events: 0.1 + probability_override: null + - when: institutional_trust == 'High trust' and tech_adoption_posture == 'Technophobe/Avoider' + multiply: null + add: null + weight_overrides: + Fully credible: 0.18 + Mostly credible: 0.36 + Skeptical - likely overhyped: 0.18 + Conspiracy - staged or manipulated: 0.04 + Uninformed - unaware of events: 0.24 + probability_override: null + - when: institutional_trust == 'Moderate trust' and tech_adoption_posture in ['Innovator', + 'Early adopter'] + multiply: null + add: null + weight_overrides: + Fully credible: 0.22 + Mostly credible: 0.44 + Skeptical - likely overhyped: 0.24 + Conspiracy - staged or manipulated: 0.06 + Uninformed - unaware of events: 0.04 + probability_override: null + - when: institutional_trust == 'Moderate trust' and tech_adoption_posture in ['Early + majority adopter', 'Late majority adopter'] + multiply: null + add: null + weight_overrides: + Fully credible: 0.14 + Mostly credible: 0.38 + Skeptical - likely overhyped: 0.3 + Conspiracy - staged or manipulated: 0.06 + Uninformed - unaware of events: 0.12 + probability_override: null + - when: institutional_trust == 'Moderate trust' and tech_adoption_posture == 'Technophobe/Avoider' + multiply: null + add: null + weight_overrides: + Fully credible: 0.08 + Mostly credible: 0.28 + Skeptical - likely overhyped: 0.26 + Conspiracy - staged or manipulated: 0.08 + Uninformed - unaware of events: 0.3 + probability_override: null + - when: institutional_trust == 'Low trust' and tech_adoption_posture in ['Innovator', + 'Early adopter'] + multiply: null + add: null + weight_overrides: + Fully credible: 0.1 + Mostly credible: 0.28 + Skeptical - likely overhyped: 0.4 + Conspiracy - staged or manipulated: 0.16 + Uninformed - unaware of events: 0.06 + probability_override: null + - when: institutional_trust == 'Low trust' and tech_adoption_posture in ['Early + majority adopter', 'Late majority adopter'] + multiply: null + add: null + weight_overrides: + Fully credible: 0.06 + Mostly credible: 0.22 + Skeptical - likely overhyped: 0.38 + Conspiracy - staged or manipulated: 0.18 + Uninformed - unaware of events: 0.16 + probability_override: null + - when: institutional_trust == 'Low trust' and tech_adoption_posture == 'Technophobe/Avoider' + multiply: null + add: null + weight_overrides: + Fully credible: 0.04 + Mostly credible: 0.14 + Skeptical - likely overhyped: 0.3 + Conspiracy - staged or manipulated: 0.2 + Uninformed - unaware of events: 0.32 + probability_override: null + - when: institutional_trust == 'Distrustful/Conspiratorial' and tech_adoption_posture + in ['Innovator', 'Early adopter'] + multiply: null + add: null + weight_overrides: + Fully credible: 0.04 + Mostly credible: 0.12 + Skeptical - likely overhyped: 0.32 + Conspiracy - staged or manipulated: 0.46 + Uninformed - unaware of events: 0.06 + probability_override: null + - when: institutional_trust == 'Distrustful/Conspiratorial' and tech_adoption_posture + in ['Early majority adopter', 'Late majority adopter'] + multiply: null + add: null + weight_overrides: + Fully credible: 0.02 + Mostly credible: 0.08 + Skeptical - likely overhyped: 0.26 + Conspiracy - staged or manipulated: 0.44 + Uninformed - unaware of events: 0.2 + probability_override: null + - when: institutional_trust == 'Distrustful/Conspiratorial' and tech_adoption_posture + == 'Technophobe/Avoider' + multiply: null + add: null + weight_overrides: + Fully credible: 0.02 + Mostly credible: 0.06 + Skeptical - likely overhyped: 0.18 + Conspiracy - staged or manipulated: 0.42 + Uninformed - unaware of events: 0.32 + probability_override: null + - when: primary_info_source == 'Avoids news' and institutional_trust in ['Low + trust', 'Distrustful/Conspiratorial'] + multiply: null + add: null + weight_overrides: + Fully credible: 0.03 + Mostly credible: 0.08 + Skeptical - likely overhyped: 0.2 + Conspiracy - staged or manipulated: 0.22 + Uninformed - unaware of events: 0.47 + probability_override: null + - when: primary_info_source == 'Social media' and institutional_trust == 'Distrustful/Conspiratorial' + multiply: null + add: null + weight_overrides: + Fully credible: 0.02 + Mostly credible: 0.08 + Skeptical - likely overhyped: 0.22 + Conspiracy - staged or manipulated: 0.58 + Uninformed - unaware of events: 0.1 + probability_override: null + grounding: + level: medium + method: extrapolated + source: 'Pew 2025: 95% of US adults have heard at least a little about AI — so + full unawareness is low but non-zero for this dramatic ASI claim. Edelman Trust + Barometer: US institutional trust very low (35%); 77% distrust businesses on + AI. Historical patterns on extraordinary technology claims: public splits between + credulous acceptance, healthy skepticism, and conspiratorial rejection. AIPI + polling: ~83% believe AI could cause catastrophic accident — indicating priming + for dramatic AI claims.' + note: '''Skeptical - likely overhyped'' is the modal response (~30%) consistent + with historically high media skepticism in the US and frequent ''AI hype'' narratives. + ''Mostly credible'' (~28%) reflects adults who trust mainstream scientific demonstrations. + ''Fully credible'' (~12%) are the attentive tech-engaged adults who trust institutional + demonstrations. ''Conspiracy/staged'' (~14%) aligns with the Distrustful/Conspiratorial + institutional_trust segment (~15-20% of population). ''Uninformed'' (~16%) captures + adults who primarily avoid news or are disengaged — larger than raw news-avoidance + data because ASI is novel enough that even news consumers may not have absorbed + the claims. institutional_trust, primary_info_source, and tech_adoption_posture + will be decisive modifiers.' + constraints: + - type: spec_expression + value: null + expression: abs(sum(weights) - 1.0) < 0.001 + reason: Weights must sum to 1 +household_config: + age_brackets: + - - 30 + - 18-29 + - - 45 + - 30-44 + - - 65 + - 45-64 + - - 81 + - 65-80 + household_type_weights: + 18-29: + single: 0.45 + couple: 0.22 + single_parent: 0.08 + couple_with_kids: 0.1 + multi_generational: 0.15 + 30-44: + single: 0.22 + couple: 0.2 + single_parent: 0.12 + couple_with_kids: 0.4 + multi_generational: 0.06 + 45-64: + single: 0.27 + couple: 0.38 + single_parent: 0.09 + couple_with_kids: 0.2 + multi_generational: 0.06 + 65-80: + single: 0.38 + couple: 0.47 + single_parent: 0.03 + couple_with_kids: 0.04 + multi_generational: 0.08 + same_group_rates: + white: 0.91 + black: 0.82 + hispanic: 0.74 + asian: 0.68 + native_american: 0.58 + multiracial: 0.42 + default_same_group_rate: 0.72 + same_country_rate: 0.95 + assortative_mating: + education_level: 0.63 + household_income: 0.6 + political_identity: 0.72 + religious_engagement: 0.68 + race_ethnicity: 0.85 + age: 0.78 + urban_rural: 0.65 + us_region: 0.7 + vote_2024_presidential: 0.74 + institutional_trust: 0.55 + tech_adoption_posture: 0.48 + risk_tolerance: 0.45 + consumer_identity: 0.42 + social_media_behavior: 0.4 + primary_info_source: 0.52 + economic_anxiety: 0.58 + financial_margin: 0.56 + investment_profile: 0.54 + employment_sector: 0.38 + openness: 0.43 + conscientiousness: 0.41 + neuroticism: 0.35 + agreeableness: 0.32 + extraversion: 0.3 + asi_adoption_intent: 0.5 + asi_credibility_assessment: 0.48 + asi_governance_stance: 0.55 + asi_threat_perception: 0.46 + geopolitical_competition_concern: 0.44 + human_purpose_anxiety: 0.4 + redistribution_support: 0.62 + job_displacement_vulnerability: 0.35 + top_issue_2026: 0.58 + midterm_turnout_propensity: 0.55 + information_verification_behavior: 0.47 + country_direction_approval: 0.7 + has_children: 0.82 + marital_status: 0.75 + personal_health_stake: 0.38 + economic_abundance_belief: 0.52 + partner_gender_mode: weighted + partner_gender_pair_weights: + - left: Male + right: Female + weight: 0.942 + - left: Male + right: Male + weight: 0.026 + - left: Female + right: Female + weight: 0.022 + - left: Male + right: Non-binary/Other + weight: 0.005 + - left: Female + right: Non-binary/Other + weight: 0.005 + partner_age_gap_mean: -2.3 + partner_age_gap_std: 4.8 + min_adult_age: 18 + min_agent_age: 13 + child_min_parent_offset: 18 + child_max_parent_offset: 45 + max_dependent_child_age: 17 + elderly_min_offset: 20 + elderly_max_offset: 45 + life_stages: + - max_age: 5 + label: preschool + - max_age: 11 + label: elementary + - max_age: 14 + label: middle_school + - max_age: 18 + label: high_school + adult_stage_label: adult + avg_household_size: 2.51 +agent_focus_mode: couples +identity_dimensions: +- dimension: professional_identity + relevance: ASI renders structurally redundant every profession built on information + asymmetry or cognitive scarcity — doctors, lawyers, engineers, scientists, financial + advisors, therapists, and software developers — directly threatening the core + of how hundreds of millions of people define their competence, purpose, and social + worth. +- dimension: socioeconomic_class + relevance: 'The scenario radically destabilizes class hierarchies: elite credentials + and access to expert knowledge — historically the primary mechanism of upper-class + reproduction — are suddenly free and universally available, while the marginal + cost of goods trends toward zero, dissolving the economic logic underpinning both + working-class labor and professional-class status simultaneously.' +- dimension: political_orientation + relevance: ASI forces an immediate ideological confrontation — conservatives face + the collapse of market-based scarcity economics and national sovereignty (China + already has it), while liberals face questions about who controls distribution + of abundance and whether democratic institutions can govern a self-improving system; + neither existing political framework was designed to process this transition. +- dimension: religious_affiliation + relevance: A self-improving system that exceeds all human cognition, solves mysteries + of the universe, and eliminates disease challenges core theological frameworks + about human exceptionalism, the meaning of suffering, divine providence, and eschatology + — activating existential threat across virtually every major faith tradition. +- dimension: generational_identity + relevance: 'Generational identity is acutely activated: older generations face the + obsolescence of lifetimes of accumulated expertise and the frameworks they used + to make sense of the world, while younger generations face a future where traditional + education, career-building, and life-stage milestones may be structurally meaningless + before they begin.' +- dimension: citizenship + relevance: National identity and sovereignty are destabilized by the revelation + that China achieved ASI first and that ASI-designed infrastructure is being deployed + selectively in early-adopter nations (Singapore, UAE, Estonia) — activating anxieties + about which nations, citizens, and governments will control or be left behind + by this transition. +- dimension: race_ethnicity + relevance: The scenario explicitly surfaces racial and ethnic equity dimensions + — the promise that a high school dropout in rural Arkansas has the same cognitive + resources as Harvard professors collides with the reality that deployment to developing + nations is 'the primary coordination challenge,' raising acute questions about + whether ASI-driven abundance will reproduce or finally dissolve existing racial + and global hierarchies of access. +- dimension: parental_status + relevance: 'Parents face a profound identity disruption: the entire framework of + raising children — education, career preparation, teaching values of effort and + expertise — is destabilized when cognitive scarcity no longer exists, forcing + a fundamental reckoning with what it means to prepare a child for a world no prior + generation has imagined.' +sampling_semantic_roles: + marital_roles: + attr: marital_status + partnered_values: + - Married/partnered + single_values: + - Single + - Divorced/separated + - Widowed + geo_roles: + country_attr: country + region_attr: us_region + urbanicity_attr: urban_rural + partner_correlation_roles: + race_ethnicity: same_group_rate + education_level: same_group_rate + religious_engagement: same_group_rate + political_identity: same_group_rate + school_parent_role: + dependents_attr: has_children + school_age_values: [] + religion_roles: + religion_attr: religious_engagement + secular_values: + - Secular/None + household_roles: null diff --git a/examples/study-02/spec_overlap_pair_audit.json b/examples/study-02/spec_overlap_pair_audit.json new file mode 100644 index 0000000..d4e9751 --- /dev/null +++ b/examples/study-02/spec_overlap_pair_audit.json @@ -0,0 +1,2863 @@ +[ + { + "attr": "education_level", + "i": 0, + "j": 1, + "left": "age < 25", + "right": "age >= 25 and age < 35", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "age" + ] + }, + { + "attr": "education_level", + "i": 0, + "j": 2, + "left": "age < 25", + "right": "age >= 35 and age < 50", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "age" + ] + }, + { + "attr": "education_level", + "i": 0, + "j": 3, + "left": "age < 25", + "right": "age >= 50 and age < 65", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "age" + ] + }, + { + "attr": "education_level", + "i": 0, + "j": 4, + "left": "age < 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+ ] + }, + { + "attr": "country_direction_approval", + "i": 0, + "j": 1, + "left": "political_identity == 'Strong Republican' and financial_margin in ['Comfortable savings buffer', 'Modest buffer']", + "right": "political_identity == 'Strong Republican' and financial_margin in ['Paycheck-to-paycheck', 'In debt/deficit']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "financial_margin", + "political_identity" + ] + }, + { + "attr": "country_direction_approval", + "i": 0, + "j": 2, + "left": "political_identity == 'Strong Republican' and financial_margin in ['Comfortable savings buffer', 'Modest buffer']", + "right": "political_identity == 'Lean Republican' and financial_margin in ['Comfortable savings buffer', 'Modest buffer']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "financial_margin", + "political_identity" + ] + }, + { + "attr": "country_direction_approval", + "i": 0, + "j": 3, + "left": "political_identity == 'Strong Republican' and financial_margin in ['Comfortable savings buffer', 'Modest buffer']", + "right": "political_identity == 'True Independent' and financial_margin in ['Comfortable savings buffer', 'Modest buffer']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "financial_margin", + "political_identity" + ] + }, + { + "attr": "country_direction_approval", + "i": 0, + "j": 7, + "left": "political_identity == 'Strong Republican' and financial_margin in ['Comfortable savings buffer', 'Modest buffer']", + "right": "political_identity == 'Strong Democrat' and financial_margin in ['Comfortable savings buffer', 'Modest buffer']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "financial_margin", + "political_identity" + ] + }, + { + "attr": "country_direction_approval", + "i": 1, + "j": 4, + "left": "political_identity == 'Strong Republican' and financial_margin in ['Paycheck-to-paycheck', 'In debt/deficit']", + "right": "political_identity == 'True Independent' and financial_margin in ['Paycheck-to-paycheck', 'In debt/deficit']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "financial_margin", + "political_identity" + ] + }, + { + "attr": "country_direction_approval", + "i": 1, + "j": 5, + "left": "political_identity == 'Strong Republican' and financial_margin in ['Paycheck-to-paycheck', 'In debt/deficit']", + "right": "political_identity == 'Lean Democrat' and financial_margin in ['Paycheck-to-paycheck', 'In debt/deficit']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "financial_margin", + "political_identity" + ] + }, + { + "attr": "country_direction_approval", + "i": 1, + "j": 6, + "left": "political_identity == 'Strong Republican' and financial_margin in ['Paycheck-to-paycheck', 'In debt/deficit']", + "right": "political_identity == 'Strong Democrat' and financial_margin in ['Paycheck-to-paycheck', 'In debt/deficit']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "financial_margin", + "political_identity" + ] + }, + { + "attr": "country_direction_approval", + "i": 2, + "j": 3, + "left": "political_identity == 'Lean Republican' and financial_margin in ['Comfortable savings buffer', 'Modest buffer']", + "right": "political_identity == 'True Independent' and financial_margin in ['Comfortable savings buffer', 'Modest buffer']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "financial_margin", + "political_identity" + ] + }, + { + "attr": "country_direction_approval", + "i": 2, + "j": 7, + "left": "political_identity == 'Lean Republican' and financial_margin in ['Comfortable savings buffer', 'Modest buffer']", + "right": "political_identity == 'Strong Democrat' and financial_margin in ['Comfortable savings buffer', 'Modest buffer']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "financial_margin", + "political_identity" + ] + }, + { + "attr": "country_direction_approval", + "i": 3, + "j": 4, + "left": "political_identity == 'True Independent' and financial_margin in ['Comfortable savings buffer', 'Modest buffer']", + "right": "political_identity == 'True Independent' and financial_margin in ['Paycheck-to-paycheck', 'In debt/deficit']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "financial_margin", + "political_identity" + ] + }, + { + "attr": "country_direction_approval", + "i": 3, + "j": 7, + "left": "political_identity == 'True Independent' and financial_margin in ['Comfortable savings buffer', 'Modest buffer']", + "right": "political_identity == 'Strong Democrat' and financial_margin in ['Comfortable savings buffer', 'Modest buffer']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "financial_margin", + "political_identity" + ] + }, + { + "attr": "country_direction_approval", + "i": 4, + "j": 5, + "left": "political_identity == 'True Independent' and financial_margin in ['Paycheck-to-paycheck', 'In debt/deficit']", + "right": "political_identity == 'Lean Democrat' and financial_margin in ['Paycheck-to-paycheck', 'In debt/deficit']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "financial_margin", + "political_identity" + ] + }, + { + "attr": "country_direction_approval", + "i": 4, + "j": 6, + "left": "political_identity == 'True Independent' and financial_margin in ['Paycheck-to-paycheck', 'In debt/deficit']", + "right": "political_identity == 'Strong Democrat' and financial_margin in ['Paycheck-to-paycheck', 'In debt/deficit']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "financial_margin", + "political_identity" + ] + }, + { + "attr": "country_direction_approval", + "i": 5, + "j": 6, + "left": "political_identity == 'Lean Democrat' and financial_margin in ['Paycheck-to-paycheck', 'In debt/deficit']", + "right": "political_identity == 'Strong Democrat' and financial_margin in ['Paycheck-to-paycheck', 'In debt/deficit']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "financial_margin", + "political_identity" + ] + }, + { + "attr": "country_direction_approval", + "i": 6, + "j": 7, + "left": "political_identity == 'Strong Democrat' and financial_margin in ['Paycheck-to-paycheck', 'In debt/deficit']", + "right": "political_identity == 'Strong Democrat' and financial_margin in ['Comfortable savings buffer', 'Modest buffer']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "financial_margin", + "political_identity" + ] + }, + { + "attr": "consumer_identity", + "i": 2, + "j": 3, + "left": "household_income >= 100000 and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "right": "household_income >= 150000 and education_level == 'Graduate degree'", + "bruteforce_overlap": true, + "witness": { + "education_level": "Graduate degree", + "household_income": 150000 + }, + "vars": [ + "education_level", + "household_income" + ] + }, + { + "attr": "institutional_trust", + "i": 0, + "j": 1, + "left": "political_identity == 'Strong Democrat' and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "right": "political_identity == 'Strong Democrat' and education_level in ['Less than HS', 'HS diploma/GED', 'Some college']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "education_level", + "political_identity" + ] + }, + { + "attr": "institutional_trust", + "i": 0, + "j": 3, + "left": "political_identity == 'Strong Democrat' and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "right": "political_identity == 'Strong Republican' and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "education_level", + "political_identity" + ] + }, + { + "attr": "institutional_trust", + "i": 0, + "j": 5, + "left": "political_identity == 'Strong Democrat' and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "right": "political_identity == 'True Independent' and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "education_level", + "political_identity" + ] + }, + { + "attr": "institutional_trust", + "i": 0, + "j": 6, + "left": "political_identity == 'Strong Democrat' and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "right": "political_identity in ['Lean Democrat'] and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "education_level", + "political_identity" + ] + }, + { + "attr": "institutional_trust", + "i": 0, + "j": 7, + "left": "political_identity == 'Strong Democrat' and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "right": "political_identity in ['Lean Republican'] and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "education_level", + "political_identity" + ] + }, + { + "attr": "institutional_trust", + "i": 1, + "j": 2, + "left": "political_identity == 'Strong Democrat' and education_level in ['Less than HS', 'HS diploma/GED', 'Some college']", + "right": "political_identity == 'Strong Republican' and education_level in ['Less than HS', 'HS diploma/GED', 'Some college']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "education_level", + "political_identity" + ] + }, + { + "attr": "institutional_trust", + "i": 1, + "j": 4, + "left": "political_identity == 'Strong Democrat' and education_level in ['Less than HS', 'HS diploma/GED', 'Some college']", + "right": "political_identity == 'True Independent' and education_level in ['Less than HS', 'HS diploma/GED']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "education_level", + "political_identity" + ] + }, + { + "attr": "institutional_trust", + "i": 2, + "j": 3, + "left": "political_identity == 'Strong Republican' and education_level in ['Less than HS', 'HS diploma/GED', 'Some college']", + "right": "political_identity == 'Strong Republican' and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "education_level", + "political_identity" + ] + }, + { + "attr": "institutional_trust", + "i": 2, + "j": 4, + "left": "political_identity == 'Strong Republican' and education_level in ['Less than HS', 'HS diploma/GED', 'Some college']", + "right": "political_identity == 'True Independent' and education_level in ['Less than HS', 'HS diploma/GED']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "education_level", + "political_identity" + ] + }, + { + "attr": "institutional_trust", + "i": 3, + "j": 5, + "left": "political_identity == 'Strong Republican' and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "right": "political_identity == 'True Independent' and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "education_level", + "political_identity" + ] + }, + { + "attr": "institutional_trust", + "i": 3, + "j": 6, + "left": "political_identity == 'Strong Republican' and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "right": "political_identity in ['Lean Democrat'] and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "education_level", + "political_identity" + ] + }, + { + "attr": "institutional_trust", + "i": 3, + "j": 7, + "left": "political_identity == 'Strong Republican' and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "right": "political_identity in ['Lean Republican'] and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "education_level", + "political_identity" + ] + }, + { + "attr": "institutional_trust", + "i": 4, + "j": 5, + "left": "political_identity == 'True Independent' and education_level in ['Less than HS', 'HS diploma/GED']", + "right": "political_identity == 'True Independent' and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "education_level", + "political_identity" + ] + }, + { + "attr": "institutional_trust", + "i": 5, + "j": 6, + "left": "political_identity == 'True Independent' and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "right": "political_identity in ['Lean Democrat'] and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "education_level", + "political_identity" + ] + }, + { + "attr": "institutional_trust", + "i": 5, + "j": 7, + "left": "political_identity == 'True Independent' and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "right": "political_identity in ['Lean Republican'] and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "education_level", + "political_identity" + ] + }, + { + "attr": "institutional_trust", + "i": 6, + "j": 7, + "left": "political_identity in ['Lean Democrat'] and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "right": "political_identity in ['Lean Republican'] and education_level in ['Bachelor\\'s degree', 'Graduate degree']", + "bruteforce_overlap": false, + "witness": null, + "vars": [ + "education_level", + "political_identity" + ] + } +] \ No newline at end of file diff --git a/examples/study-02/spec_warnings_by_attribute.txt b/examples/study-02/spec_warnings_by_attribute.txt new file mode 100644 index 0000000..fd64b49 --- /dev/null +++ b/examples/study-02/spec_warnings_by_attribute.txt @@ -0,0 +1,256 @@ +## congressional_district_competitiveness (45) +- modifiers 0 and 1 may overlap under last-wins semantics +- modifiers 0 and 2 may overlap under last-wins semantics +- modifiers 0 and 3 may overlap under last-wins semantics +- modifiers 0 and 6 may overlap under last-wins semantics +- modifiers 0 and 9 may overlap under last-wins semantics +- modifiers 0 and 12 may overlap under last-wins semantics +- modifiers 1 and 2 may overlap under last-wins semantics +- modifiers 1 and 4 may overlap under last-wins semantics +- modifiers 1 and 7 may overlap under last-wins semantics +- modifiers 1 and 10 may overlap under last-wins semantics +- modifiers 1 and 13 may overlap under last-wins semantics +- modifiers 2 and 5 may overlap under last-wins semantics +- modifiers 2 and 8 may overlap under last-wins semantics +- modifiers 2 and 11 may overlap under last-wins semantics +- modifiers 2 and 14 may overlap under last-wins semantics +- modifiers 3 and 4 may overlap under last-wins semantics +- modifiers 3 and 5 may overlap under last-wins semantics +- modifiers 3 and 6 may overlap under last-wins semantics +- modifiers 3 and 9 may overlap under last-wins semantics +- modifiers 3 and 12 may overlap under last-wins semantics +- modifiers 4 and 5 may overlap under last-wins semantics +- modifiers 4 and 7 may overlap under last-wins semantics +- modifiers 4 and 10 may overlap under last-wins semantics +- modifiers 4 and 13 may overlap under last-wins semantics +- modifiers 5 and 8 may overlap under last-wins semantics +- modifiers 5 and 11 may overlap under last-wins semantics +- modifiers 5 and 14 may overlap under last-wins semantics +- modifiers 6 and 7 may overlap under last-wins semantics +- modifiers 6 and 8 may overlap under last-wins semantics +- modifiers 6 and 9 may overlap under last-wins semantics +- modifiers 6 and 12 may overlap under last-wins semantics +- modifiers 7 and 8 may overlap under last-wins semantics +- modifiers 7 and 10 may overlap under last-wins semantics +- modifiers 7 and 13 may overlap under last-wins semantics +- modifiers 8 and 11 may overlap under last-wins semantics +- modifiers 8 and 14 may overlap under last-wins semantics +- modifiers 9 and 10 may overlap under last-wins semantics +- modifiers 9 and 11 may overlap under last-wins semantics +- modifiers 9 and 12 may overlap under last-wins semantics +- modifiers 10 and 11 may overlap under last-wins semantics +- modifiers 10 and 13 may overlap under last-wins semantics +- modifiers 11 and 14 may overlap under last-wins semantics +- modifiers 12 and 13 may overlap under last-wins semantics +- modifiers 12 and 14 may overlap under last-wins semantics +- modifiers 13 and 14 may overlap under last-wins semantics + +## institutional_trust (16) +- modifiers 0 and 1 may overlap under last-wins semantics +- modifiers 0 and 3 may overlap under last-wins semantics +- modifiers 0 and 5 may overlap under last-wins semantics +- modifiers 0 and 6 may overlap under last-wins semantics +- modifiers 0 and 7 may overlap under last-wins semantics +- modifiers 1 and 2 may overlap under last-wins semantics +- modifiers 1 and 4 may overlap under last-wins semantics +- modifiers 2 and 3 may overlap under last-wins semantics +- modifiers 2 and 4 may overlap under last-wins semantics +- modifiers 3 and 5 may overlap under last-wins semantics +- modifiers 3 and 6 may overlap under last-wins semantics +- modifiers 3 and 7 may overlap under last-wins semantics +- modifiers 4 and 5 may overlap under last-wins semantics +- modifiers 5 and 6 may overlap under last-wins semantics +- modifiers 5 and 7 may overlap under last-wins semantics +- modifiers 6 and 7 may overlap under last-wins semantics + +## social_media_behavior (15) +- modifiers 0 and 1 may overlap under last-wins semantics +- modifiers 0 and 2 may overlap under last-wins semantics +- modifiers 0 and 3 may overlap under last-wins semantics +- modifiers 0 and 4 may overlap under last-wins semantics +- modifiers 0 and 5 may overlap under last-wins semantics +- modifiers 1 and 2 may overlap under last-wins semantics +- modifiers 1 and 3 may overlap under last-wins semantics +- modifiers 1 and 4 may overlap under last-wins semantics +- modifiers 1 and 5 may overlap under last-wins semantics +- modifiers 2 and 3 may overlap under last-wins semantics +- modifiers 2 and 4 may overlap under last-wins semantics +- modifiers 2 and 5 may overlap under last-wins semantics +- modifiers 3 and 4 may overlap under last-wins semantics +- modifiers 3 and 5 may overlap under last-wins semantics +- modifiers 4 and 5 may overlap under last-wins semantics + +## political_identity (15) +- modifiers 0 and 1 may overlap under last-wins semantics +- modifiers 0 and 5 may overlap under last-wins semantics +- modifiers 0 and 6 may overlap under last-wins semantics +- modifiers 0 and 7 may overlap under last-wins semantics +- modifiers 0 and 8 may overlap under last-wins semantics +- modifiers 1 and 2 may overlap under last-wins semantics +- modifiers 1 and 6 may overlap under last-wins semantics +- modifiers 1 and 7 may overlap under last-wins semantics +- modifiers 1 and 8 may overlap under last-wins semantics +- modifiers 3 and 5 may overlap under last-wins semantics +- modifiers 3 and 7 may overlap under last-wins semantics +- modifiers 5 and 8 may overlap under last-wins semantics +- modifiers 6 and 7 may overlap under last-wins semantics +- modifiers 6 and 8 may overlap under last-wins semantics +- modifiers 7 and 8 may overlap under last-wins semantics + +## country_direction_approval (15) +- modifiers 0 and 1 may overlap under last-wins semantics +- modifiers 0 and 2 may overlap under last-wins semantics +- modifiers 0 and 3 may overlap under last-wins semantics +- modifiers 0 and 7 may overlap under last-wins semantics +- modifiers 1 and 4 may overlap under last-wins semantics +- modifiers 1 and 5 may overlap under last-wins semantics +- modifiers 1 and 6 may overlap under last-wins semantics +- modifiers 2 and 3 may overlap under last-wins semantics +- modifiers 2 and 7 may overlap under last-wins semantics +- modifiers 3 and 4 may overlap under last-wins semantics +- modifiers 3 and 7 may overlap under last-wins semantics +- modifiers 4 and 5 may overlap under last-wins semantics +- modifiers 4 and 6 may overlap under last-wins semantics +- modifiers 5 and 6 may overlap under last-wins semantics +- modifiers 6 and 7 may overlap under last-wins semantics + +## tech_adoption_posture (12) +- modifiers 0 and 2 may overlap under last-wins semantics +- modifiers 0 and 4 may overlap under last-wins semantics +- modifiers 0 and 6 may overlap under last-wins semantics +- modifiers 1 and 3 may overlap under last-wins semantics +- modifiers 1 and 5 may overlap under last-wins semantics +- modifiers 1 and 6 may overlap under last-wins semantics +- modifiers 2 and 4 may overlap under last-wins semantics +- modifiers 2 and 6 may overlap under last-wins semantics +- modifiers 3 and 5 may overlap under last-wins semantics +- modifiers 3 and 6 may overlap under last-wins semantics +- modifiers 4 and 6 may overlap under last-wins semantics +- modifiers 5 and 6 may overlap under last-wins semantics + +## employment_sector (11) +- modifiers 0 and 2 may overlap under last-wins semantics +- modifiers 0 and 4 may overlap under last-wins semantics +- modifiers 0 and 5 may overlap under last-wins semantics +- modifiers 1 and 2 may overlap under last-wins semantics +- modifiers 1 and 4 may overlap under last-wins semantics +- modifiers 1 and 5 may overlap under last-wins semantics +- modifiers 2 and 4 may overlap under last-wins semantics +- modifiers 2 and 5 may overlap under last-wins semantics +- modifiers 3 and 4 may overlap under last-wins semantics +- modifiers 3 and 5 may overlap under last-wins semantics +- modifiers 4 and 5 may overlap under last-wins semantics + +## top_issue_2026 (11) +- modifiers 0 and 2 may overlap under last-wins semantics +- modifiers 0 and 3 may overlap under last-wins semantics +- modifiers 0 and 4 may overlap under last-wins semantics +- modifiers 0 and 6 may overlap under last-wins semantics +- modifiers 1 and 2 may overlap under last-wins semantics +- modifiers 1 and 5 may overlap under last-wins semantics +- modifiers 1 and 6 may overlap under last-wins semantics +- modifiers 2 and 3 may overlap under last-wins semantics +- modifiers 2 and 5 may overlap under last-wins semantics +- modifiers 4 and 5 may overlap under last-wins semantics +- modifiers 4 and 6 may overlap under last-wins semantics + +## education_level (10) +- modifiers 0 and 1 may overlap under last-wins semantics +- modifiers 0 and 2 may overlap under last-wins semantics +- modifiers 0 and 3 may overlap under last-wins semantics +- modifiers 0 and 4 may overlap under last-wins semantics +- modifiers 1 and 2 may overlap under last-wins semantics +- modifiers 1 and 3 may overlap under last-wins semantics +- modifiers 1 and 4 may overlap under last-wins semantics +- modifiers 2 and 3 may overlap under last-wins semantics +- modifiers 2 and 4 may overlap under last-wins semantics +- modifiers 3 and 4 may overlap under last-wins semantics + +## marital_status (10) +- modifiers 0 and 1 may overlap under last-wins semantics +- modifiers 0 and 2 may overlap under last-wins semantics +- modifiers 0 and 3 may overlap under last-wins semantics +- modifiers 0 and 4 may overlap under last-wins semantics +- modifiers 1 and 2 may overlap under last-wins semantics +- modifiers 1 and 3 may overlap under last-wins semantics +- modifiers 1 and 4 may overlap under last-wins semantics +- modifiers 2 and 3 may overlap under last-wins semantics +- modifiers 2 and 4 may overlap under last-wins semantics +- modifiers 3 and 4 may overlap under last-wins semantics + +## financial_margin (10) +- modifiers 0 and 1 may overlap under last-wins semantics +- modifiers 0 and 2 may overlap under last-wins semantics +- modifiers 0 and 3 may overlap under last-wins semantics +- modifiers 0 and 4 may overlap under last-wins semantics +- modifiers 1 and 2 may overlap under last-wins semantics +- modifiers 1 and 3 may overlap under last-wins semantics +- modifiers 1 and 4 may overlap under last-wins semantics +- modifiers 2 and 3 may overlap under last-wins semantics +- modifiers 2 and 4 may overlap under last-wins semantics +- modifiers 3 and 4 may overlap under last-wins semantics + +## religious_engagement (10) +- modifiers 0 and 1 may overlap under last-wins semantics +- modifiers 0 and 2 may overlap under last-wins semantics +- modifiers 1 and 2 may overlap under last-wins semantics +- modifiers 1 and 3 may overlap under last-wins semantics +- modifiers 1 and 5 may overlap under last-wins semantics +- modifiers 1 and 6 may overlap under last-wins semantics +- modifiers 3 and 5 may overlap under last-wins semantics +- modifiers 3 and 6 may overlap under last-wins semantics +- modifiers 5 and 6 may overlap under last-wins semantics +- modifiers 5 and 7 may overlap under last-wins semantics + +## has_children (9) +- modifiers 0 and 4 may overlap under last-wins semantics +- modifiers 0 and 5 may overlap under last-wins semantics +- modifiers 1 and 2 may overlap under last-wins semantics +- modifiers 1 and 3 may overlap under last-wins semantics +- modifiers 1 and 5 may overlap under last-wins semantics +- modifiers 2 and 3 may overlap under last-wins semantics +- modifiers 2 and 5 may overlap under last-wins semantics +- modifiers 3 and 5 may overlap under last-wins semantics +- modifiers 4 and 5 may overlap under last-wins semantics + +## primary_info_source (9) +- modifiers 0 and 2 may overlap under last-wins semantics +- modifiers 0 and 4 may overlap under last-wins semantics +- modifiers 0 and 5 may overlap under last-wins semantics +- modifiers 1 and 3 may overlap under last-wins semantics +- modifiers 1 and 5 may overlap under last-wins semantics +- modifiers 2 and 4 may overlap under last-wins semantics +- modifiers 2 and 5 may overlap under last-wins semantics +- modifiers 3 and 5 may overlap under last-wins semantics +- modifiers 4 and 5 may overlap under last-wins semantics + +## vote_2024_presidential (9) +- modifiers 0 and 1 may overlap under last-wins semantics +- modifiers 0 and 6 may overlap under last-wins semantics +- modifiers 1 and 7 may overlap under last-wins semantics +- modifiers 4 and 5 may overlap under last-wins semantics +- modifiers 4 and 6 may overlap under last-wins semantics +- modifiers 4 and 7 may overlap under last-wins semantics +- modifiers 5 and 6 may overlap under last-wins semantics +- modifiers 5 and 7 may overlap under last-wins semantics +- modifiers 6 and 7 may overlap under last-wins semantics + +## investment_profile (5) +- modifiers 0 and 1 may overlap under last-wins semantics +- modifiers 0 and 4 may overlap under last-wins semantics +- modifiers 1 and 4 may overlap under last-wins semantics +- modifiers 2 and 4 may overlap under last-wins semantics +- modifiers 3 and 4 may overlap under last-wins semantics + +## political_engagement_level (3) +- modifiers 0 and 1 may overlap under last-wins semantics +- modifiers 2 and 3 may overlap under last-wins semantics +- modifiers 4 and 5 may overlap under last-wins semantics + +## midterm_turnout_propensity (3) +- modifiers 0 and 1 may overlap under last-wins semantics +- modifiers 2 and 3 may overlap under last-wins semantics +- modifiers 5 and 6 may overlap under last-wins semantics + +## consumer_identity (1) +- modifiers 2 and 3 may overlap under last-wins semantics diff --git a/examples/study-02/spec_warnings_full.json b/examples/study-02/spec_warnings_full.json new file mode 100644 index 0000000..79bd4d4 --- /dev/null +++ b/examples/study-02/spec_warnings_full.json @@ -0,0 +1,1973 @@ +[ + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "education_level", + "message": "modifiers 0 and 1 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "education_level", + "message": "modifiers 0 and 2 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "education_level", + "message": "modifiers 0 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "education_level", + "message": "modifiers 0 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "education_level", + "message": "modifiers 1 and 2 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "education_level", + "message": "modifiers 1 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "education_level", + "message": "modifiers 1 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "education_level", + "message": "modifiers 2 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "education_level", + "message": "modifiers 2 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "education_level", + "message": "modifiers 3 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "marital_status", + "message": "modifiers 0 and 1 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "marital_status", + "message": "modifiers 0 and 2 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "marital_status", + "message": "modifiers 0 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "marital_status", + "message": "modifiers 0 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "marital_status", + "message": "modifiers 1 and 2 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "marital_status", + "message": "modifiers 1 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "marital_status", + "message": "modifiers 1 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "marital_status", + "message": "modifiers 2 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "marital_status", + "message": "modifiers 2 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "marital_status", + "message": "modifiers 3 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "has_children", + "message": "modifiers 0 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "has_children", + "message": "modifiers 0 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "has_children", + "message": "modifiers 1 and 2 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "has_children", + "message": "modifiers 1 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "has_children", + "message": "modifiers 1 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "has_children", + "message": "modifiers 2 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "has_children", + "message": "modifiers 2 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "has_children", + "message": "modifiers 3 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "has_children", + "message": "modifiers 4 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "employment_sector", + "message": "modifiers 0 and 2 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "employment_sector", + "message": "modifiers 0 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "employment_sector", + "message": "modifiers 0 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "employment_sector", + "message": "modifiers 1 and 2 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "employment_sector", + "message": "modifiers 1 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "employment_sector", + "message": "modifiers 1 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "employment_sector", + "message": "modifiers 2 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "employment_sector", + "message": "modifiers 2 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "employment_sector", + "message": "modifiers 3 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "employment_sector", + "message": "modifiers 3 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "employment_sector", + "message": "modifiers 4 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "financial_margin", + "message": "modifiers 0 and 1 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "financial_margin", + "message": "modifiers 0 and 2 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "financial_margin", + "message": "modifiers 0 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "financial_margin", + "message": "modifiers 0 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "financial_margin", + "message": "modifiers 1 and 2 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "financial_margin", + "message": "modifiers 1 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "financial_margin", + "message": "modifiers 1 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "financial_margin", + "message": "modifiers 2 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "financial_margin", + "message": "modifiers 2 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "financial_margin", + "message": "modifiers 3 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "investment_profile", + "message": "modifiers 0 and 1 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "investment_profile", + "message": "modifiers 0 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "investment_profile", + "message": "modifiers 1 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "investment_profile", + "message": "modifiers 2 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "investment_profile", + "message": "modifiers 3 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "primary_info_source", + "message": "modifiers 0 and 2 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "primary_info_source", + "message": "modifiers 0 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "primary_info_source", + "message": "modifiers 0 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "primary_info_source", + "message": "modifiers 1 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "primary_info_source", + "message": "modifiers 1 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "primary_info_source", + "message": "modifiers 2 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "primary_info_source", + "message": "modifiers 2 and 5 may overlap under last-wins 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"modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "social_media_behavior", + "message": "modifiers 0 and 2 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "social_media_behavior", + "message": "modifiers 0 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "social_media_behavior", + "message": "modifiers 0 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "social_media_behavior", + "message": "modifiers 0 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "social_media_behavior", + "message": "modifiers 1 and 2 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "social_media_behavior", + "message": "modifiers 1 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "social_media_behavior", + "message": "modifiers 1 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "social_media_behavior", + "message": "modifiers 1 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "social_media_behavior", + "message": "modifiers 2 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "social_media_behavior", + "message": "modifiers 2 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "social_media_behavior", + "message": "modifiers 2 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "social_media_behavior", + "message": "modifiers 3 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "social_media_behavior", + "message": "modifiers 3 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + 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"congressional_district_competitiveness", + "message": "modifiers 7 and 13 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "congressional_district_competitiveness", + "message": "modifiers 8 and 11 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "congressional_district_competitiveness", + "message": "modifiers 8 and 14 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "congressional_district_competitiveness", + "message": "modifiers 9 and 10 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "congressional_district_competitiveness", + "message": "modifiers 9 and 11 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "congressional_district_competitiveness", + "message": "modifiers 9 and 12 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "congressional_district_competitiveness", + "message": "modifiers 10 and 11 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "congressional_district_competitiveness", + "message": "modifiers 10 and 13 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "congressional_district_competitiveness", + "message": "modifiers 11 and 14 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "congressional_district_competitiveness", + "message": "modifiers 12 and 13 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "congressional_district_competitiveness", + "message": "modifiers 12 and 14 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "congressional_district_competitiveness", + "message": "modifiers 13 and 14 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "top_issue_2026", + "message": "modifiers 0 and 2 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "top_issue_2026", + "message": "modifiers 0 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "top_issue_2026", + "message": "modifiers 0 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "top_issue_2026", + "message": "modifiers 0 and 6 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "top_issue_2026", + "message": "modifiers 1 and 2 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "top_issue_2026", + "message": "modifiers 1 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "top_issue_2026", + "message": "modifiers 1 and 6 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "top_issue_2026", + "message": "modifiers 2 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "top_issue_2026", + "message": "modifiers 2 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "top_issue_2026", + "message": "modifiers 4 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "top_issue_2026", + "message": "modifiers 4 and 6 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "country_direction_approval", + "message": "modifiers 0 and 1 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "country_direction_approval", + "message": "modifiers 0 and 2 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "country_direction_approval", + "message": "modifiers 0 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "country_direction_approval", + "message": "modifiers 0 and 7 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "country_direction_approval", + "message": "modifiers 1 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "country_direction_approval", + "message": "modifiers 1 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "country_direction_approval", + "message": "modifiers 1 and 6 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "country_direction_approval", + "message": "modifiers 2 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "country_direction_approval", + "message": "modifiers 2 and 7 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "country_direction_approval", + "message": "modifiers 3 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "country_direction_approval", + "message": "modifiers 3 and 7 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "country_direction_approval", + "message": "modifiers 4 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "country_direction_approval", + "message": "modifiers 4 and 6 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "country_direction_approval", + "message": "modifiers 5 and 6 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "country_direction_approval", + "message": "modifiers 6 and 7 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "consumer_identity", + "message": "modifiers 2 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "institutional_trust", + "message": "modifiers 0 and 1 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "institutional_trust", + "message": "modifiers 0 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "institutional_trust", + "message": "modifiers 0 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "institutional_trust", + "message": "modifiers 0 and 6 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "institutional_trust", + "message": "modifiers 0 and 7 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "institutional_trust", + "message": "modifiers 1 and 2 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "institutional_trust", + "message": "modifiers 1 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "institutional_trust", + "message": "modifiers 2 and 3 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "institutional_trust", + "message": "modifiers 2 and 4 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "institutional_trust", + "message": "modifiers 3 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "institutional_trust", + "message": "modifiers 3 and 6 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "institutional_trust", + "message": "modifiers 3 and 7 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "institutional_trust", + "message": "modifiers 4 and 5 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "institutional_trust", + "message": "modifiers 5 and 6 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "institutional_trust", + "message": "modifiers 5 and 7 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + }, + { + "severity": "warning", + "category": "MODIFIER_OVERLAP", + "location": "institutional_trust", + "message": "modifiers 6 and 7 may overlap under last-wins semantics", + "suggestion": "Make conditions mutually exclusive or merge the modifier logic", + "modifier_index": null, + "value": null + } +] \ No newline at end of file diff --git a/examples/study-02/spec_warnings_full.txt b/examples/study-02/spec_warnings_full.txt new file mode 100644 index 0000000..028c0a4 --- /dev/null +++ b/examples/study-02/spec_warnings_full.txt @@ -0,0 +1,219 @@ +001. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 1 may overlap under last-wins semantics +002. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 2 may overlap under last-wins semantics +003. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 3 may overlap under last-wins semantics +004. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 4 may overlap under last-wins semantics +005. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 2 may overlap under last-wins semantics +006. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 3 may overlap under last-wins semantics +007. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 4 may overlap under last-wins semantics +008. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 3 may overlap under last-wins semantics +009. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 4 may overlap under last-wins semantics +010. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 4 may overlap under last-wins semantics +011. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 1 may overlap under last-wins semantics +012. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 2 may overlap under last-wins semantics +013. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 3 may overlap under last-wins semantics +014. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 4 may overlap under last-wins semantics +015. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 2 may overlap under last-wins semantics +016. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 3 may overlap under last-wins semantics +017. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 4 may overlap under last-wins semantics +018. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 3 may overlap under last-wins semantics +019. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 4 may overlap under last-wins semantics +020. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 4 may overlap under last-wins semantics +021. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 4 may overlap under last-wins semantics +022. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 5 may overlap under last-wins semantics +023. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 2 may overlap under last-wins semantics +024. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 3 may overlap under last-wins semantics +025. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 5 may overlap under last-wins semantics +026. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 3 may overlap under last-wins semantics +027. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 5 may overlap under last-wins semantics +028. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 5 may overlap under last-wins semantics +029. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 4 and 5 may overlap under last-wins semantics +030. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 2 may overlap under last-wins semantics +031. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 4 may overlap under last-wins semantics +032. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 5 may overlap under last-wins semantics +033. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 2 may overlap under last-wins semantics +034. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 4 may overlap under last-wins semantics +035. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 5 may overlap under last-wins semantics +036. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 4 may overlap under last-wins semantics +037. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 5 may overlap under last-wins semantics +038. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 4 may overlap under last-wins semantics +039. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 5 may overlap under last-wins semantics +040. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 4 and 5 may overlap under last-wins semantics +041. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 1 may overlap under last-wins semantics +042. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 2 may overlap under last-wins semantics +043. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 3 may overlap under last-wins semantics +044. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 4 may overlap under last-wins semantics +045. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 2 may overlap under last-wins semantics +046. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 3 may overlap under last-wins semantics +047. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 4 may overlap under last-wins semantics +048. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 3 may overlap under last-wins semantics +049. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 4 may overlap under last-wins semantics +050. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 4 may overlap under last-wins semantics +051. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 1 may overlap under last-wins semantics +052. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 4 may overlap under last-wins semantics +053. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 4 may overlap under last-wins semantics +054. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 4 may overlap under last-wins semantics +055. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 4 may overlap under last-wins semantics +056. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 2 may overlap under last-wins semantics +057. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 4 may overlap under last-wins semantics +058. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 5 may overlap under last-wins semantics +059. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 3 may overlap under last-wins semantics +060. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 5 may overlap under last-wins semantics +061. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 4 may overlap under last-wins semantics +062. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 5 may overlap under last-wins semantics +063. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 5 may overlap under last-wins semantics +064. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 4 and 5 may overlap under last-wins semantics +065. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 1 may overlap under last-wins semantics +066. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 2 may overlap under last-wins semantics +067. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 3 may overlap under last-wins semantics +068. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 4 may overlap under last-wins semantics +069. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 5 may overlap under last-wins semantics +070. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 2 may overlap under last-wins semantics +071. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 3 may overlap under last-wins semantics +072. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 4 may overlap under last-wins semantics +073. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 5 may overlap under last-wins semantics +074. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 3 may overlap under last-wins semantics +075. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 4 may overlap under last-wins semantics +076. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 5 may overlap under last-wins semantics +077. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 4 may overlap under last-wins semantics +078. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 5 may overlap under last-wins semantics +079. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 4 and 5 may overlap under last-wins semantics +080. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 2 may overlap under last-wins semantics +081. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 4 may overlap under last-wins semantics +082. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 6 may overlap under last-wins semantics +083. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 3 may overlap under last-wins semantics +084. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 5 may overlap under last-wins semantics +085. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 6 may overlap under last-wins semantics +086. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 4 may overlap under last-wins semantics +087. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 6 may overlap under last-wins semantics +088. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 5 may overlap under last-wins semantics +089. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 6 may overlap under last-wins semantics +090. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 4 and 6 may overlap under last-wins semantics +091. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 5 and 6 may overlap under last-wins semantics +092. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 1 may overlap under last-wins semantics +093. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 2 may overlap under last-wins semantics +094. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 2 may overlap under last-wins semantics +095. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 3 may overlap under last-wins semantics +096. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 5 may overlap under last-wins semantics +097. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 6 may overlap under last-wins semantics +098. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 5 may overlap under last-wins semantics +099. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 6 may overlap under last-wins semantics +100. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 5 and 6 may overlap under last-wins semantics +101. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 5 and 7 may overlap under last-wins semantics +102. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 1 may overlap under last-wins semantics +103. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 5 may overlap under last-wins semantics +104. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 6 may overlap under last-wins semantics +105. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 7 may overlap under last-wins semantics +106. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 8 may overlap under last-wins semantics +107. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 2 may overlap under last-wins semantics +108. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 6 may overlap under last-wins semantics +109. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 7 may overlap under last-wins semantics +110. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 8 may overlap under last-wins semantics +111. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 5 may overlap under last-wins semantics +112. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 7 may overlap under last-wins semantics +113. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 5 and 8 may overlap under last-wins semantics +114. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 6 and 7 may overlap under last-wins semantics +115. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 6 and 8 may overlap under last-wins semantics +116. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 7 and 8 may overlap under last-wins semantics +117. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 1 may overlap under last-wins semantics +118. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 3 may overlap under last-wins semantics +119. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 4 and 5 may overlap under last-wins semantics +120. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 1 may overlap under last-wins semantics +121. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 6 may overlap under last-wins semantics +122. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 7 may overlap under last-wins semantics +123. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 4 and 5 may overlap under last-wins semantics +124. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 4 and 6 may overlap under last-wins semantics +125. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 4 and 7 may overlap under last-wins semantics +126. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 5 and 6 may overlap under last-wins semantics +127. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 5 and 7 may overlap under last-wins semantics +128. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 6 and 7 may overlap under last-wins semantics +129. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 1 may overlap under last-wins semantics +130. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 3 may overlap under last-wins semantics +131. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 5 and 6 may overlap under last-wins semantics +132. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 1 may overlap under last-wins semantics +133. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 2 may overlap under last-wins semantics +134. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 3 may overlap under last-wins semantics +135. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 6 may overlap under last-wins semantics +136. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 9 may overlap under last-wins semantics +137. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 12 may overlap under last-wins semantics +138. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 2 may overlap under last-wins semantics +139. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 4 may overlap under last-wins semantics +140. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 7 may overlap under last-wins semantics +141. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 10 may overlap under last-wins semantics +142. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 13 may overlap under last-wins semantics +143. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 5 may overlap under last-wins semantics +144. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 8 may overlap under last-wins semantics +145. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 11 may overlap under last-wins semantics +146. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 14 may overlap under last-wins semantics +147. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 4 may overlap under last-wins semantics +148. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 5 may overlap under last-wins semantics +149. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 6 may overlap under last-wins semantics +150. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 9 may overlap under last-wins semantics +151. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 12 may overlap under last-wins semantics +152. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 4 and 5 may overlap under last-wins semantics +153. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 4 and 7 may overlap under last-wins semantics +154. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 4 and 10 may overlap under last-wins semantics +155. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 4 and 13 may overlap under last-wins semantics +156. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 5 and 8 may overlap under last-wins semantics +157. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 5 and 11 may overlap under last-wins semantics +158. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 5 and 14 may overlap under last-wins semantics +159. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 6 and 7 may overlap under last-wins semantics +160. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 6 and 8 may overlap under last-wins semantics +161. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 6 and 9 may overlap under last-wins semantics +162. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 6 and 12 may overlap under last-wins semantics +163. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 7 and 8 may overlap under last-wins semantics +164. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 7 and 10 may overlap under last-wins semantics +165. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 7 and 13 may overlap under last-wins semantics +166. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 8 and 11 may overlap under last-wins semantics +167. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 8 and 14 may overlap under last-wins semantics +168. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 9 and 10 may overlap under last-wins semantics +169. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 9 and 11 may overlap under last-wins semantics +170. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 9 and 12 may overlap under last-wins semantics +171. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 10 and 11 may overlap under last-wins semantics +172. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 10 and 13 may overlap under last-wins semantics +173. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 11 and 14 may overlap under last-wins semantics +174. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 12 and 13 may overlap under last-wins semantics +175. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 12 and 14 may overlap under last-wins semantics +176. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 13 and 14 may overlap under last-wins semantics +177. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 2 may overlap under last-wins semantics +178. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 3 may overlap under last-wins semantics +179. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 4 may overlap under last-wins semantics +180. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 6 may overlap under last-wins semantics +181. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 2 may overlap under last-wins semantics +182. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 5 may overlap under last-wins semantics +183. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 6 may overlap under last-wins semantics +184. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 3 may overlap under last-wins semantics +185. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 5 may overlap under last-wins semantics +186. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 4 and 5 may overlap under last-wins semantics +187. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 4 and 6 may overlap under last-wins semantics +188. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 1 may overlap under last-wins semantics +189. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 2 may overlap under last-wins semantics +190. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 3 may overlap under last-wins semantics +191. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 7 may overlap under last-wins semantics +192. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 4 may overlap under last-wins semantics +193. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 5 may overlap under last-wins semantics +194. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 6 may overlap under last-wins semantics +195. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 3 may overlap under last-wins semantics +196. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 7 may overlap under last-wins semantics +197. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 4 may overlap under last-wins semantics +198. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 7 may overlap under last-wins semantics +199. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 4 and 5 may overlap under last-wins semantics +200. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 4 and 6 may overlap under last-wins semantics +201. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 5 and 6 may overlap under last-wins semantics +202. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 6 and 7 may overlap under last-wins semantics +203. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 3 may overlap under last-wins semantics +204. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 1 may overlap under last-wins semantics +205. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 3 may overlap under last-wins semantics +206. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 5 may overlap under last-wins semantics +207. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 6 may overlap under last-wins semantics +208. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 0 and 7 may overlap under last-wins semantics +209. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 2 may overlap under last-wins semantics +210. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 1 and 4 may overlap under last-wins semantics +211. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 3 may overlap under last-wins semantics +212. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 2 and 4 may overlap under last-wins semantics +213. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 5 may overlap under last-wins semantics +214. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 6 may overlap under last-wins semantics +215. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 3 and 7 may overlap under last-wins semantics +216. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 4 and 5 may overlap under last-wins semantics +217. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 5 and 6 may overlap under last-wins semantics +218. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 5 and 7 may overlap under last-wins semantics +219. [Severity.WARNING] NO_CODE | NO_ATTR | modifiers 6 and 7 may overlap under last-wins semantics \ No newline at end of file diff --git a/examples/study-02/spec_warnings_grouped.txt b/examples/study-02/spec_warnings_grouped.txt new file mode 100644 index 0000000..b72d762 --- /dev/null +++ b/examples/study-02/spec_warnings_grouped.txt @@ -0,0 +1,65 @@ +Total warnings: 219 +Unique message patterns: 61 + +Top exact warning patterns: +- (13) modifiers 0 and 1 may overlap under last-wins semantics +- (11) modifiers 0 and 2 may overlap under last-wins semantics +- (11) modifiers 1 and 2 may overlap under last-wins semantics +- (11) modifiers 2 and 3 may overlap under last-wins semantics +- (10) modifiers 0 and 4 may overlap under last-wins semantics +- (10) modifiers 4 and 5 may overlap under last-wins semantics +- (9) modifiers 1 and 4 may overlap under last-wins semantics +- (9) modifiers 2 and 4 may overlap under last-wins semantics +- (9) modifiers 3 and 5 may overlap under last-wins semantics +- (8) modifiers 0 and 3 may overlap under last-wins semantics +- (8) modifiers 1 and 3 may overlap under last-wins semantics +- (8) modifiers 3 and 4 may overlap under last-wins semantics +- (8) modifiers 1 and 5 may overlap under last-wins semantics +- (6) modifiers 0 and 5 may overlap under last-wins semantics +- (6) modifiers 2 and 5 may overlap under last-wins semantics +- (6) modifiers 0 and 6 may overlap under last-wins semantics +- (6) modifiers 5 and 6 may overlap under last-wins semantics +- (5) modifiers 1 and 6 may overlap under last-wins semantics +- (5) modifiers 6 and 7 may overlap under last-wins semantics +- (4) modifiers 3 and 6 may overlap under last-wins semantics +- (4) modifiers 4 and 6 may overlap under last-wins semantics +- (3) modifiers 5 and 7 may overlap under last-wins semantics +- (3) modifiers 0 and 7 may overlap under last-wins semantics +- (3) modifiers 1 and 7 may overlap under last-wins semantics +- (3) modifiers 3 and 7 may overlap under last-wins semantics +- (2) modifiers 5 and 8 may overlap under last-wins semantics +- (2) modifiers 6 and 8 may overlap under last-wins semantics +- (2) modifiers 7 and 8 may overlap under last-wins semantics +- (2) modifiers 4 and 7 may overlap under last-wins semantics +- (1) modifiers 2 and 6 may overlap under last-wins semantics +- (1) modifiers 0 and 8 may overlap under last-wins semantics +- (1) modifiers 1 and 8 may overlap under last-wins semantics +- (1) modifiers 0 and 9 may overlap under last-wins semantics +- (1) modifiers 0 and 12 may overlap under last-wins semantics +- (1) modifiers 1 and 10 may overlap under last-wins semantics +- (1) modifiers 1 and 13 may overlap under last-wins semantics +- (1) modifiers 2 and 8 may overlap under last-wins semantics +- (1) modifiers 2 and 11 may overlap under last-wins semantics +- (1) modifiers 2 and 14 may overlap under last-wins semantics +- (1) modifiers 3 and 9 may overlap under last-wins semantics +- (1) modifiers 3 and 12 may overlap under last-wins semantics +- (1) modifiers 4 and 10 may overlap under last-wins semantics +- (1) modifiers 4 and 13 may overlap under last-wins semantics +- (1) modifiers 5 and 11 may overlap under last-wins semantics +- (1) modifiers 5 and 14 may overlap under last-wins semantics +- (1) modifiers 6 and 9 may overlap under last-wins semantics +- (1) modifiers 6 and 12 may overlap under last-wins semantics +- (1) modifiers 7 and 10 may overlap under last-wins semantics +- (1) modifiers 7 and 13 may overlap under last-wins semantics +- (1) modifiers 8 and 11 may overlap under last-wins semantics +- (1) modifiers 8 and 14 may overlap under last-wins semantics +- (1) modifiers 9 and 10 may overlap under last-wins semantics +- (1) modifiers 9 and 11 may overlap under last-wins semantics +- (1) modifiers 9 and 12 may overlap under last-wins semantics +- (1) modifiers 10 and 11 may overlap under last-wins semantics +- (1) modifiers 10 and 13 may overlap under last-wins semantics +- (1) modifiers 11 and 14 may overlap under last-wins semantics +- (1) modifiers 12 and 13 may overlap under last-wins semantics +- (1) modifiers 12 and 14 may overlap under last-wins semantics +- (1) modifiers 13 and 14 may overlap under last-wins semantics +- (1) modifiers 2 and 7 may overlap under last-wins semantics \ No newline at end of file diff --git a/examples/study-02/study.db b/examples/study-02/study.db new file mode 100644 index 0000000000000000000000000000000000000000..89bfeca9245b3dfeb833a4264e0dcbb38a7ab72e GIT binary patch literal 23523328 zcmeF43w#^ZmH#DM(&()uk|P3yOcFwDVkeR%TUJ5{PGW+|izE&qBteX&u|2V*Q8c3j zlLi=#^M1bw6evxBQc5YMlv3JK%C@wnlmg4rmQuX#;GQVJW$M}H$S;IGU zE`5L1sOm>G&p19PlV0Vh^m$IoJ$tsc-y$-dQ8vzsoA_jJNZ?{zlu2@8Uns$FNnxmP z*SzIztsQN2N9){0ZS+uHbgh{l^s}Dt3~>><`VudWZeP;TwxDf! z8K3pT@PX>kP|!tXlDTAg`{LH+tLcSptLfTI>FQ~&o?c&9RH{09ReQ%pOILQ#%a^Wd zpD#}zO}XdH(QfS>3SYP<74KE!mx<31G(Ugb6CAhXj#Hio@WP@n++ZVfaFtVoIpg 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sOdc6}-21WYA=iWHX1r4Mwy-i;KJwd&e*gX? z{ugs~C$FB|z~lxdH!!&Y>IOb69}VJvp4o$A<9|hcPAQxz6|{m{n8g2+_ bool: @@ -276,17 +277,21 @@ def _resolve_merged_population_for_persona(persona_file: Path) -> PopulationSpec from ...core.models.scenario import ScenarioSpec scenario_dir = persona_file.parent - scenario_files = sorted( - scenario_dir.glob("scenario.v*.yaml"), - key=lambda p: int(re.search(r"scenario\.v(\d+)\.yaml$", p.name).group(1)), # type: ignore[union-attr] - ) - if not scenario_files: + scenario_files_with_versions: list[tuple[int, Path]] = [] + for path in scenario_dir.glob("scenario.v*.yaml"): + match = re.match(r"^scenario\.v(\d+)\.yaml$", path.name) + if not match: + continue + scenario_files_with_versions.append((int(match.group(1)), path)) + + scenario_files_with_versions.sort(key=lambda item: item[0]) + if not scenario_files_with_versions: raise FileNotFoundError( f"No scenario.vN.yaml found next to persona file: {persona_file}" ) # Choose latest scenario version in this directory. - scenario_path = scenario_files[-1] + scenario_path = scenario_files_with_versions[-1][1] scenario_spec = ScenarioSpec.from_yaml(scenario_path) pop_name, pop_version = scenario_spec.meta.get_population_ref() @@ -304,7 +309,15 @@ def _resolve_merged_population_for_persona(persona_file: Path) -> PopulationSpec pop_spec = PopulationSpec.from_yaml(pop_path) ext_attrs = scenario_spec.extended_attributes or [] merged_attrs = list(pop_spec.attributes) + ext_attrs - merged_order = pop_spec.merged_sampling_order(ext_attrs) + merged_deps: dict[str, list[str]] = { + attr.name: list(attr.sampling.depends_on or []) for attr in merged_attrs + } + merged_names = set(merged_deps.keys()) + merged_deps = { + name: [dep for dep in deps if dep in merged_names] + for name, deps in merged_deps.items() + } + merged_order = topological_sort(merged_deps) return PopulationSpec( meta=pop_spec.meta, grounding=pop_spec.grounding, diff --git a/extropy/core/models/__init__.py b/extropy/core/models/__init__.py index 18cb21f..abf1290 100644 --- a/extropy/core/models/__init__.py +++ b/extropy/core/models/__init__.py @@ -39,6 +39,7 @@ HouseholdType, Dependent, LifeStageThreshold, + PartnerGenderPairWeight, HouseholdConfig, STANDARD_PERSONALITY_ATTRIBUTES, ) @@ -160,6 +161,7 @@ "HouseholdType", "Dependent", "LifeStageThreshold", + "PartnerGenderPairWeight", "HouseholdConfig", "STANDARD_PERSONALITY_ATTRIBUTES", # Scenario - Event diff --git a/extropy/core/models/population.py b/extropy/core/models/population.py index 24db747..9f52c5b 100644 --- a/extropy/core/models/population.py +++ b/extropy/core/models/population.py @@ -51,6 +51,14 @@ class LifeStageThreshold(BaseModel): label: str = Field(description="Stage label, e.g. 'elementary', 'high_school'") +class PartnerGenderPairWeight(BaseModel): + """Weighted probability for an unordered partner gender pair.""" + + left: str = Field(description="One gender value in the pair") + right: str = Field(description="Other gender value in the pair") + weight: float = Field(ge=0, description="Non-negative pair weight") + + class HouseholdConfig(BaseModel): """Household composition and dependent generation parameters. @@ -115,6 +123,20 @@ class HouseholdConfig(BaseModel): "political_orientation": 0.5, } ) + partner_gender_mode: Literal["independent", "weighted"] = Field( + default="independent", + description=( + "independent: sample partner gender from base distribution; " + "weighted: apply partner_gender_pair_weights to bias pair composition" + ), + ) + partner_gender_pair_weights: list[PartnerGenderPairWeight] = Field( + default_factory=list, + description=( + "Optional unordered partner gender pair weights " + "(e.g., male/female, female/female)." + ), + ) partner_age_gap_mean: float = -2.0 partner_age_gap_std: float = 3.0 min_adult_age: int = 18 diff --git a/extropy/core/models/scenario.py b/extropy/core/models/scenario.py index 8da9446..aa7acdb 100644 --- a/extropy/core/models/scenario.py +++ b/extropy/core/models/scenario.py @@ -449,6 +449,15 @@ class ReligionRoles(BaseModel): ) +class HouseholdRoles(BaseModel): + """Role mapping for household-coupled structural attributes.""" + + household_size_attr: str | None = Field( + default=None, + description="Attribute name representing total realized household size", + ) + + class SamplingSemanticRoles(BaseModel): """Scenario-owned semantic role mapping for deterministic sample/runtime logic.""" @@ -472,6 +481,10 @@ class SamplingSemanticRoles(BaseModel): default=None, description="Religion role mapping", ) + household_roles: HouseholdRoles | None = Field( + default=None, + description="Household structural role mapping", + ) class ScenarioSpec(BaseModel): diff --git a/extropy/population/modifier_precedence.py b/extropy/population/modifier_precedence.py new file mode 100644 index 0000000..7fbfe97 --- /dev/null +++ b/extropy/population/modifier_precedence.py @@ -0,0 +1,557 @@ +"""Deterministic precedence rules for overlapping conditional modifiers. + +Precedence policy: +1. Subset wins (stricter condition wins) +2. Higher specificity wins +3. Declaration order wins (higher index) +""" + +from __future__ import annotations + +import ast +import re +from dataclasses import dataclass, field +from typing import Any, Literal + + +PrecedenceReason = Literal["single", "subset", "specificity", "order"] + + +@dataclass(frozen=True) +class ModifierPrecedenceDecision: + """Winner selection result for overlapping modifiers.""" + + winner_index: int + reason: PrecedenceReason + + +@dataclass +class _VarConstraint: + """Normalized constraints for one variable in a condition expression.""" + + allowed: set[Any] | None = None + disallowed: set[Any] = field(default_factory=set) + lower: tuple[float, bool] | None = None # (value, inclusive) + upper: tuple[float, bool] | None = None # (value, inclusive) + + def apply_eq(self, value: Any) -> None: + values = {value} + self.allowed = values if self.allowed is None else self.allowed & values + + def apply_in(self, values: set[Any]) -> None: + self.allowed = values if self.allowed is None else self.allowed & values + + def apply_neq(self, value: Any) -> None: + self.disallowed.add(value) + + def apply_not_in(self, values: set[Any]) -> None: + self.disallowed.update(values) + + def apply_lower(self, value: float, inclusive: bool) -> None: + if self.lower is None: + self.lower = (value, inclusive) + return + current_value, current_inclusive = self.lower + if value > current_value: + self.lower = (value, inclusive) + elif value == current_value and not inclusive and current_inclusive: + self.lower = (value, inclusive) + + def apply_upper(self, value: float, inclusive: bool) -> None: + if self.upper is None: + self.upper = (value, inclusive) + return + current_value, current_inclusive = self.upper + if value < current_value: + self.upper = (value, inclusive) + elif value == current_value and not inclusive and current_inclusive: + self.upper = (value, inclusive) + + def effective_allowed(self) -> set[Any] | None: + if self.allowed is None: + return None + return {value for value in self.allowed if value not in self.disallowed} + + def numeric_range_width(self) -> float | None: + if self.lower is None or self.upper is None: + return None + return self.upper[0] - self.lower[0] + + +@dataclass +class _WhenSummary: + raw: str + parseable: bool + unconditional: bool + always_false: bool + clause_count: int + constraints: dict[str, _VarConstraint] + + +def choose_modifier_precedence( + conditions: list[tuple[int, str]], +) -> ModifierPrecedenceDecision | None: + """Choose one winning modifier index from matching conditions.""" + if not conditions: + return None + if len(conditions) == 1: + return ModifierPrecedenceDecision( + winner_index=conditions[0][0], reason="single" + ) + + summaries = {idx: _parse_when(expr) for idx, expr in conditions} + subset_balance: dict[int, int] = {idx: 0 for idx, _ in conditions} + + for a, b in _iter_pairs([idx for idx, _ in conditions]): + a_implies_b = _when_implies(summaries[a], summaries[b]) + b_implies_a = _when_implies(summaries[b], summaries[a]) + if a_implies_b and not b_implies_a: + subset_balance[a] += 1 + subset_balance[b] -= 1 + elif b_implies_a and not a_implies_b: + subset_balance[b] += 1 + subset_balance[a] -= 1 + + specificity_scores = { + idx: _specificity_score(summaries[idx]) for idx, _ in conditions + } + ranked = sorted( + [idx for idx, _ in conditions], + key=lambda idx: ( + subset_balance[idx], + specificity_scores[idx], + idx, + ), + ) + winner = ranked[-1] + runner_up = ranked[-2] + + if subset_balance[winner] > subset_balance[runner_up]: + reason: PrecedenceReason = "subset" + elif specificity_scores[winner] > specificity_scores[runner_up]: + reason = "specificity" + else: + reason = "order" + + return ModifierPrecedenceDecision(winner_index=winner, reason=reason) + + +def _iter_pairs(indices: list[int]): + for i in range(len(indices)): + for j in range(i + 1, len(indices)): + yield indices[i], indices[j] + + +def _parse_when(expr: str) -> _WhenSummary: + text = (expr or "").strip() + if not text: + return _WhenSummary( + raw=text, + parseable=False, + unconditional=False, + always_false=False, + clause_count=0, + constraints={}, + ) + + try: + tree = ast.parse(text, mode="eval") + except SyntaxError: + return _WhenSummary( + raw=text, + parseable=False, + unconditional=False, + always_false=False, + clause_count=0, + constraints={}, + ) + + constraints: dict[str, _VarConstraint] = {} + clause_count = 0 + parseable = True + always_false = False + + def add_clause() -> None: + nonlocal clause_count + clause_count += 1 + + def get_constraint(name: str) -> _VarConstraint: + return constraints.setdefault(name, _VarConstraint()) + + def visit(node: ast.AST) -> bool: + nonlocal always_false + + if isinstance(node, ast.BoolOp): + if not isinstance(node.op, ast.And): + return False + return all(visit(value) for value in node.values) + + if isinstance(node, ast.Constant) and isinstance(node.value, bool): + if node.value is False: + always_false = True + return True + + if isinstance(node, ast.Compare): + operands = [node.left, *node.comparators] + for op, left_node, right_node in zip( + node.ops, operands[:-1], operands[1:], strict=False + ): + if not _apply_comparison( + left_node, + op, + right_node, + get_constraint, + add_clause, + ): + return False + return True + + return False + + parseable = visit(tree.body) + if not parseable: + constraints = {} + clause_count = 0 + + unconditional = parseable and clause_count == 0 and not always_false + + return _WhenSummary( + raw=text, + parseable=parseable, + unconditional=unconditional, + always_false=always_false, + clause_count=clause_count, + constraints=constraints, + ) + + +def _apply_comparison( + left_node: ast.AST, + op: ast.cmpop, + right_node: ast.AST, + get_constraint, + add_clause, +) -> bool: + left_name = _name_from_node(left_node) + right_name = _name_from_node(right_node) + + if left_name and not right_name: + return _apply_name_to_value_comparison( + left_name, op, right_node, get_constraint, add_clause + ) + + if right_name and not left_name: + reversed_op = _reverse_compare_op(op) + if reversed_op is None: + return False + return _apply_name_to_value_comparison( + right_name, + reversed_op, + left_node, + get_constraint, + add_clause, + ) + + return False + + +def _apply_name_to_value_comparison( + name: str, + op: ast.cmpop, + value_node: ast.AST, + get_constraint, + add_clause, +) -> bool: + constraint = get_constraint(name) + + if isinstance(op, ast.In): + values = _literal_set_from_node(value_node) + if values is None: + return False + constraint.apply_in(values) + add_clause() + return True + + if isinstance(op, ast.NotIn): + values = _literal_set_from_node(value_node) + if values is None: + return False + constraint.apply_not_in(values) + add_clause() + return True + + value = _literal_from_node(value_node) + if value is None: + return False + + if isinstance(op, ast.Eq): + constraint.apply_eq(value) + add_clause() + return True + if isinstance(op, ast.NotEq): + constraint.apply_neq(value) + add_clause() + return True + + if isinstance(value, bool) or not isinstance(value, (int, float)): + return False + + numeric = float(value) + if isinstance(op, ast.Gt): + constraint.apply_lower(numeric, inclusive=False) + add_clause() + return True + if isinstance(op, ast.GtE): + constraint.apply_lower(numeric, inclusive=True) + add_clause() + return True + if isinstance(op, ast.Lt): + constraint.apply_upper(numeric, inclusive=False) + add_clause() + return True + if isinstance(op, ast.LtE): + constraint.apply_upper(numeric, inclusive=True) + add_clause() + return True + + return False + + +def _name_from_node(node: ast.AST) -> str | None: + if isinstance(node, ast.Name): + return node.id + return None + + +def _literal_from_node(node: ast.AST) -> Any | None: + if isinstance(node, ast.Constant): + return node.value + return None + + +def _literal_set_from_node(node: ast.AST) -> set[Any] | None: + if isinstance(node, ast.Constant): + return {node.value} + + if not isinstance(node, (ast.List, ast.Tuple, ast.Set)): + return None + + values: set[Any] = set() + for elt in node.elts: + if not isinstance(elt, ast.Constant): + return None + values.add(elt.value) + return values + + +def _reverse_compare_op(op: ast.cmpop) -> ast.cmpop | None: + mapping: dict[type[ast.cmpop], ast.cmpop] = { + ast.Lt: ast.Gt(), + ast.LtE: ast.GtE(), + ast.Gt: ast.Lt(), + ast.GtE: ast.LtE(), + ast.Eq: ast.Eq(), + ast.NotEq: ast.NotEq(), + ast.In: ast.In(), + ast.NotIn: ast.NotIn(), + } + return mapping.get(type(op)) + + +def _when_implies(left: _WhenSummary, right: _WhenSummary) -> bool: + if right.unconditional: + return True + if left.always_false: + return True + if right.always_false: + return left.always_false + if left.unconditional: + return right.unconditional + if not left.parseable or not right.parseable: + return False + + for var_name, right_constraint in right.constraints.items(): + left_constraint = left.constraints.get(var_name) + if left_constraint is None: + return False + if not _constraint_implies(left_constraint, right_constraint): + return False + + return True + + +def _constraint_implies(left: _VarConstraint, right: _VarConstraint) -> bool: + left_allowed = left.effective_allowed() + right_allowed = right.effective_allowed() + + if right_allowed is not None: + if left_allowed is None: + return False + if not left_allowed.issubset(right_allowed): + return False + + if right.lower is not None or right.upper is not None: + if left_allowed is not None: + if not left_allowed: + return True + for value in left_allowed: + if not _value_satisfies_bounds(value, right.lower, right.upper): + return False + elif not _bounds_imply( + left.lower, + left.upper, + right.lower, + right.upper, + ): + return False + + for disallowed in right.disallowed: + if _value_possible_under_constraint(disallowed, left): + return False + + return True + + +def _value_possible_under_constraint(value: Any, constraint: _VarConstraint) -> bool: + allowed = constraint.effective_allowed() + if allowed is not None and value not in allowed: + return False + if value in constraint.disallowed: + return False + if not _value_satisfies_bounds(value, constraint.lower, constraint.upper): + return False + return True + + +def _value_satisfies_bounds( + value: Any, + lower: tuple[float, bool] | None, + upper: tuple[float, bool] | None, +) -> bool: + if isinstance(value, bool) or not isinstance(value, (int, float)): + return lower is None and upper is None + + numeric = float(value) + if lower is not None: + lower_value, lower_inclusive = lower + if lower_inclusive: + if numeric < lower_value: + return False + elif numeric <= lower_value: + return False + if upper is not None: + upper_value, upper_inclusive = upper + if upper_inclusive: + if numeric > upper_value: + return False + elif numeric >= upper_value: + return False + return True + + +def _bounds_imply( + left_lower: tuple[float, bool] | None, + left_upper: tuple[float, bool] | None, + right_lower: tuple[float, bool] | None, + right_upper: tuple[float, bool] | None, +) -> bool: + if right_lower is not None: + if left_lower is None: + return False + if not _lower_bound_implies(left_lower, right_lower): + return False + if right_upper is not None: + if left_upper is None: + return False + if not _upper_bound_implies(left_upper, right_upper): + return False + return True + + +def _lower_bound_implies( + left: tuple[float, bool], + right: tuple[float, bool], +) -> bool: + left_value, left_inclusive = left + right_value, right_inclusive = right + + if left_value > right_value: + return True + if left_value < right_value: + return False + + if right_inclusive: + return True + return not left_inclusive + + +def _upper_bound_implies( + left: tuple[float, bool], + right: tuple[float, bool], +) -> bool: + left_value, left_inclusive = left + right_value, right_inclusive = right + + if left_value < right_value: + return True + if left_value > right_value: + return False + + if right_inclusive: + return True + return not left_inclusive + + +def _specificity_score(summary: _WhenSummary) -> int: + if summary.always_false: + return 1_000_000 + + if not summary.parseable: + return _fallback_specificity_score(summary.raw) + + score = summary.clause_count * 20 + for constraint in summary.constraints.values(): + allowed = constraint.effective_allowed() + if allowed is not None: + size = max(1, len(allowed)) + score += max(0, 60 - min(size, 10) * 6) + score += len(constraint.disallowed) * 4 + if constraint.lower is not None: + score += 9 + if constraint.upper is not None: + score += 9 + if constraint.lower is not None and constraint.upper is not None: + width = constraint.numeric_range_width() + if width is not None: + if width <= 0: + score += 30 + elif width <= 1: + score += 20 + elif width <= 5: + score += 14 + elif width <= 20: + score += 9 + elif width <= 100: + score += 5 + return score + + +def _fallback_specificity_score(expr: str) -> int: + text = expr.strip().lower() + if not text: + return 0 + + score = 0 + score += text.count(" and ") * 6 + score += text.count("==") * 4 + score += text.count(" in ") * 4 + score += text.count("!=") * 3 + score += text.count(">=") * 3 + score += text.count("<=") * 3 + score += text.count(">") * 2 + score += text.count("<") * 2 + + # Light lexical fallback so longer, denser expressions rank as more specific. + token_count = len(re.findall(r"[a-z_][a-z0-9_]*", text)) + score += token_count + return score diff --git a/extropy/population/persona/renderer.py b/extropy/population/persona/renderer.py index 48db220..1238e76 100644 --- a/extropy/population/persona/renderer.py +++ b/extropy/population/persona/renderer.py @@ -311,6 +311,86 @@ def _ensure_period(phrase: str) -> str: return phrase +def _join_with_and(items: list[str]) -> str: + """Join items with commas and final 'and'.""" + if not items: + return "" + if len(items) == 1: + return items[0] + if len(items) == 2: + return f"{items[0]} and {items[1]}" + return ", ".join(items[:-1]) + f", and {items[-1]}" + + +def _extract_average_descriptor(phrase: str, attr_name: str) -> str: + """Extract readable descriptor from repetitive average-relative phrase.""" + text = phrase.strip() + + patterns = ( + r"(?i)about as (.+?) as most people", + r"(?i)^(.+?) is about the same as most people", + ) + for pattern in patterns: + match = re.search(pattern, text) + if match: + descriptor = match.group(1).strip(" .") + if descriptor: + # Keep descriptor as a neutral noun/phrase. + if descriptor.lower().startswith("i "): + descriptor = descriptor[2:].strip() + if descriptor.lower().startswith("my "): + descriptor = descriptor[3:].strip() + if descriptor.lower().startswith("much "): + descriptor = descriptor[5:].strip() + return descriptor + + return attr_name.replace("_", " ") + + +def _compact_repetitive_average_relative_phrases( + entries: list[tuple[str, str, str | None]], +) -> list[tuple[str, str, str | None]]: + """Collapse repetitive 'about ... as most people' relative phrases. + + Keeps semantics but avoids copy-paste sounding sections when many traits land + in the average bucket. + """ + avg_idx: list[int] = [] + descriptors: list[str] = [] + + for idx, (attr_name, phrase, treatment) in enumerate(entries): + if treatment != "relative": + continue + low = phrase.lower() + if "as most people" not in low and "same as most people" not in low: + continue + avg_idx.append(idx) + desc = _extract_average_descriptor(phrase, attr_name) + if desc and desc not in descriptors: + descriptors.append(desc) + + # Only compact when repetition is clearly noisy. + if len(avg_idx) < 3 or len(descriptors) < 3: + return entries + + summary = ( + "On these traits, I'm pretty middle-of-the-road: " + f"{_join_with_and(descriptors)}." + ) + + compacted: list[tuple[str, str, str | None]] = [] + inserted = False + for idx, entry in enumerate(entries): + if idx in avg_idx: + if not inserted: + compacted.append(("relative_average_summary", summary, "relative")) + inserted = True + continue + compacted.append(entry) + + return compacted + + def render_intro( agent: dict[str, Any], config: PersonaConfig, @@ -626,12 +706,21 @@ def render_persona( continue lines = [f"## {group_obj.label}", ""] - phrases = [] + phrase_entries: list[tuple[str, str, str | None]] = [] for attr_name in remaining_attrs: value = agent.get(attr_name) phrase = render_attribute(attr_name, value, config) if phrase: - phrases.append(_ensure_period(phrase)) + treatment = config.get_treatment(attr_name) + treatment_type = ( + treatment.treatment.value if treatment is not None else None + ) + phrase_entries.append( + (attr_name, _ensure_period(phrase), treatment_type) + ) + + phrase_entries = _compact_repetitive_average_relative_phrases(phrase_entries) + phrases = [phrase for _, phrase, _ in phrase_entries] if not phrases: continue diff --git a/extropy/population/persona/validator.py b/extropy/population/persona/validator.py index 26046cd..7d6e3c5 100644 --- a/extropy/population/persona/validator.py +++ b/extropy/population/persona/validator.py @@ -180,16 +180,4 @@ def validate_persona_config( message=f"missing concrete phrasing for '{attr_name}'", ) - # Float treatment should support mixed rendering quality. - if ( - attr.type == "float" - and treatment == "relative" - and attr_name not in conc_map - ): - result.add_warning( - category="PERSONA_PHRASING", - location=f"phrasings.concrete.{attr_name}", - message="relative float has no concrete fallback template", - ) - return result diff --git a/extropy/population/sampler/core.py b/extropy/population/sampler/core.py index f16573d..00cd279 100644 --- a/extropy/population/sampler/core.py +++ b/extropy/population/sampler/core.py @@ -23,6 +23,7 @@ SamplingStats, SamplingResult, HouseholdConfig, + HouseholdType, SamplingSemanticRoles, MaritalRoles, ) @@ -34,6 +35,7 @@ household_needs_kids, correlate_partner_attribute, generate_dependents, + choose_partner_gender, ) from .modifiers import apply_modifiers_and_sample from ...utils.eval_safe import eval_formula, FormulaError @@ -457,6 +459,7 @@ def _generate_npc_partner( primary: dict[str, Any], attr_map: dict[str, AttributeSpec], categorical_options: dict[str, list[str]], + gender_attr: str | None, rng: random.Random, config: HouseholdConfig, geography_hint: str | None = None, @@ -469,8 +472,25 @@ def _generate_npc_partner( """ partner: dict[str, Any] = {} - # Always include gender - partner["gender"] = rng.choice(["male", "female"]) + if gender_attr: + partner_gender = choose_partner_gender( + primary_gender=( + str(primary.get(gender_attr)) + if primary.get(gender_attr) is not None + else None + ), + available_genders=categorical_options.get(gender_attr), + rng=rng, + config=config, + ) + if partner_gender: + partner[gender_attr] = partner_gender + partner["gender"] = partner_gender + else: + partner["gender"] = rng.choice(["male", "female"]) + else: + # Legacy fallback when no gender attribute exists in spec. + partner["gender"] = rng.choice(["male", "female"]) # Always correlate age if present (essential for NPC identity, regardless of scope) if "age" in primary: @@ -624,6 +644,10 @@ def _sample_population_households( household_attrs = { attr.name for attr in spec.attributes if attr.scope == "household" } + marital_attr = _resolve_marital_attr_name(attr_map, semantic_roles) + household_size_attr = _resolve_household_size_attr_name(attr_map, semantic_roles) + gender_attr = _resolve_gender_attr_name(attr_map) + marital_spec = attr_map.get(marital_attr) if marital_attr else None categorical_options: dict[str, list[str]] = {} for attr in spec.attributes: if attr.type == "categorical" and attr.sampling.distribution: @@ -654,16 +678,33 @@ def _sample_population_households( has_partner = household_needs_partner(htype) has_kids = household_needs_kids(htype) + if has_partner and focus_mode in ("couples", "all") and agent_index >= target_n: + # In couples/all mode, avoid creating fallback NPC partners when there is + # no capacity left for a second agent. + has_partner = False num_adults = 2 if has_partner else 1 - # Determine household_size from agent if present, else estimate - household_size = adult1.get( + # Determine sampled household_size from agent if present, else estimate. + # Then enforce household-type semantics deterministically (kids/no-kids). + sampled_household_size = adult1.get( "household_size", num_adults + (1 if has_kids else 0) ) - if isinstance(household_size, (int, float)): - household_size = max(num_adults, int(household_size)) + if isinstance(sampled_household_size, (int, float)): + sampled_household_size = max(num_adults, int(sampled_household_size)) else: - household_size = num_adults + (1 if has_kids else 0) + sampled_household_size = num_adults + (1 if has_kids else 0) + + target_dependents = max(0, sampled_household_size - num_adults) + if htype in (HouseholdType.SINGLE, HouseholdType.COUPLE): + target_dependents = 0 + elif htype in ( + HouseholdType.SINGLE_PARENT, + HouseholdType.COUPLE_WITH_KIDS, + HouseholdType.MULTI_GENERATIONAL, + ): + target_dependents = max(1, target_dependents) + + household_size = num_adults + target_dependents # Annotate Adult 1 with household fields adult1["household_id"] = household_id @@ -687,6 +728,7 @@ def _sample_population_households( household_attrs, categorical_options, config, + gender_attr, semantic_roles=semantic_roles, ) adult2["household_id"] = household_id @@ -704,6 +746,7 @@ def _sample_population_households( adult1, attr_map, categorical_options, + gender_attr, rng, config, geography_hint=spec.meta.geography, @@ -734,6 +777,7 @@ def _sample_population_households( if has_kids and focus_mode == "all": # Kids old enough become full agents; younger ones stay as NPCs dep_dicts = [] + promoted_dependents: list[dict[str, Any]] = [] for dep in dependents: if agent_index >= target_n or dep.age < config.min_agent_age: # Too young or at target — stays as NPC data @@ -752,7 +796,7 @@ def _sample_population_households( household_id, semantic_roles=semantic_roles, ) - agents.append(kid_agent) + promoted_dependents.append(kid_agent) adult_ids.append(kid_agent["_id"]) agent_index += 1 # Any overflow dependents attached as NPC data @@ -761,6 +805,37 @@ def _sample_population_households( # Kids are NPCs dep_dicts = [d.model_dump() for d in dependents] adult1["dependents"] = dep_dicts + promoted_dependents = [] + + household_members = [adult1] + if adult2_added: + household_members.append(adult2) + household_members.extend(promoted_dependents) + + # In household-mode, household-coupled fields should reflect realized structure + # before post-sampling reconciliation/gating runs. + if household_size_attr: + npc_partner_count = 1 if has_partner and not adult2_added else 0 + realized_size = len(household_members) + npc_partner_count + len(dep_dicts) + for member in household_members: + member[household_size_attr] = realized_size + + if marital_spec and marital_attr: + for member in household_members: + has_partner_member = bool( + member.get("partner_id") or member.get("partner_npc") + ) + desired = _pick_marital_value( + marital_spec, + has_partner_member, + marital_roles=( + semantic_roles.marital_roles if semantic_roles else None + ), + ) + if desired is not None: + member[marital_attr] = desired + + agents.extend(promoted_dependents) agents.append(adult1) @@ -812,6 +887,7 @@ def _sample_partner_agent( household_attrs: set[str], categorical_options: dict[str, list[str]], config: HouseholdConfig | None = None, + gender_attr: str | None = None, semantic_roles: SamplingSemanticRoles | None = None, ) -> dict[str, Any]: """Sample a partner agent with correlated demographics. @@ -833,8 +909,18 @@ def _sample_partner_agent( # Household-scoped: copy from primary if attr.scope == "household" and attr_name in primary: value = primary[attr_name] - # Partner-correlated: use assortative mating - elif attr.scope == "partner_correlated" and attr_name in primary: + # Partner-correlated: use assortative mating. + # Age-like fields and scenario policy overrides are correlated even when + # scope metadata is incomplete, to prevent unrealistic partner drift. + elif ( + attr_name in primary + and ( + attr.scope == "partner_correlated" + or attr.semantic_type == "age" + or _resolve_partner_policy_override(attr_name, semantic_roles) + is not None + ) + ): value = correlate_partner_attribute( attr_name, attr.type, @@ -862,8 +948,26 @@ def _sample_partner_agent( agent[attr_name] = value _update_stats(attr, value, stats, numeric_values) + if gender_attr and gender_attr in agent: + partner_gender = choose_partner_gender( + primary_gender=( + str(primary.get(gender_attr)) + if primary.get(gender_attr) is not None + else None + ), + available_genders=categorical_options.get(gender_attr), + rng=rng, + config=config, + ) + if partner_gender is not None: + agent[gender_attr] = partner_gender + # Generate first name for partner agent - gender = agent.get("gender") or agent.get("sex") + gender = None + if gender_attr: + gender = agent.get(gender_attr) + if gender is None: + gender = agent.get("gender") or agent.get("sex") ethnicity = ( agent.get("race_ethnicity") or agent.get("ethnicity") or agent.get("race") ) @@ -1121,6 +1225,65 @@ def _find_attr_name( return None +def _resolve_marital_attr_name( + attr_map: dict[str, AttributeSpec], + semantic_roles: SamplingSemanticRoles | None = None, +) -> str | None: + """Resolve marital attribute via scenario semantic roles, then legacy fallback.""" + if ( + semantic_roles + and semantic_roles.marital_roles + and semantic_roles.marital_roles.attr + ): + candidate = semantic_roles.marital_roles.attr + if candidate in attr_map: + return candidate + + return _find_attr_name( + attr_map, + {"marital_status", "marital", "relationship_status"}, + ) + + +def _resolve_household_size_attr_name( + attr_map: dict[str, AttributeSpec], + semantic_roles: SamplingSemanticRoles | None = None, +) -> str | None: + """Resolve household-size attribute via scenario semantic roles, then fallback.""" + if ( + semantic_roles + and semantic_roles.household_roles + and semantic_roles.household_roles.household_size_attr + ): + candidate = semantic_roles.household_roles.household_size_attr + attr = attr_map.get(candidate) + if attr is not None and attr.type in ("int", "float"): + return candidate + + fallback = _find_attr_name(attr_map, {"household_size"}) + if fallback is None: + return None + attr = attr_map.get(fallback) + if attr is not None and attr.type in ("int", "float"): + return fallback + return None + + +def _resolve_gender_attr_name(attr_map: dict[str, AttributeSpec]) -> str | None: + """Resolve gender attribute by metadata first, then legacy alias fallback.""" + for name, attr in attr_map.items(): + if attr.identity_type == "gender_identity": + return name + + fallback = _find_attr_name(attr_map, {"gender", "sex", "gender_identity"}) + if fallback is None: + return None + attr = attr_map.get(fallback) + if attr is None or attr.type != "categorical": + return None + return fallback + + def _resolve_partner_policy_override( attr_name: str, semantic_roles: SamplingSemanticRoles | None, @@ -1240,11 +1403,8 @@ def _reconcile_household_coherence( if not by_household: return - marital_attr = _find_attr_name( - attr_map, - {"marital_status", "marital", "relationship_status"}, - ) - household_size_attr = _find_attr_name(attr_map, {"household_size"}) + marital_attr = _resolve_marital_attr_name(attr_map, semantic_roles) + household_size_attr = _resolve_household_size_attr_name(attr_map, semantic_roles) marital_updates = 0 household_size_updates = 0 diff --git a/extropy/population/sampler/households.py b/extropy/population/sampler/households.py index 40aa51c..19f7f9d 100644 --- a/extropy/population/sampler/households.py +++ b/extropy/population/sampler/households.py @@ -20,6 +20,50 @@ from ..names.generator import generate_name +def _normalize_label(value: str) -> str: + return " ".join(value.strip().lower().split()) + + +def _pair_key(left: str, right: str) -> tuple[str, str]: + a = _normalize_label(left) + b = _normalize_label(right) + return (a, b) if a <= b else (b, a) + + +def choose_partner_gender( + primary_gender: str | None, + available_genders: list[str] | None, + rng: random.Random, + config: HouseholdConfig, +) -> str | None: + """Choose partner gender using scenario-owned pairing policy.""" + options = [g for g in (available_genders or []) if isinstance(g, str) and g.strip()] + if not options: + return None + + if config.partner_gender_mode != "weighted" or not config.partner_gender_pair_weights: + return rng.choice(options) + + primary = primary_gender if isinstance(primary_gender, str) else None + if not primary: + return rng.choice(options) + + weights_by_pair: dict[tuple[str, str], float] = {} + for row in config.partner_gender_pair_weights: + key = _pair_key(row.left, row.right) + weights_by_pair[key] = max(0.0, weights_by_pair.get(key, 0.0) + row.weight) + + candidate_weights: list[float] = [] + for option in options: + key = _pair_key(primary, option) + candidate_weights.append(max(0.0, weights_by_pair.get(key, 0.0))) + + if sum(candidate_weights) <= 0: + return rng.choice(options) + + return rng.choices(options, weights=candidate_weights, k=1)[0] + + def _age_bracket(age: int, config: HouseholdConfig) -> str: """Map age to bracket key using config age brackets.""" for upper_bound, label in config.age_brackets: diff --git a/extropy/population/sampler/modifiers.py b/extropy/population/sampler/modifiers.py index 7ca6b2b..54062c1 100644 --- a/extropy/population/sampler/modifiers.py +++ b/extropy/population/sampler/modifiers.py @@ -2,8 +2,8 @@ Modifiers adjust distributions based on agent attributes: - Numeric (multiply/add): All matching modifiers stack -- Categorical (weight_overrides): Last matching modifier wins -- Boolean (probability_override): Last matching modifier wins +- Categorical (weight_overrides): One deterministic winner +- Boolean (probability_override): One deterministic winner """ import logging @@ -20,6 +20,7 @@ CategoricalDistribution, BooleanDistribution, ) +from ..modifier_precedence import choose_modifier_precedence from ...utils.eval_safe import eval_condition from .distributions import ( _sample_normal, @@ -61,7 +62,6 @@ def apply_modifiers_and_sample( try: if eval_condition(mod.when, agent, raise_on_error=True): matching_modifiers.append((i, mod)) - triggered_indices.append(i) except Exception as e: warning = f"modifier[{i}] when='{mod.when}' eval failed: {e}" condition_warnings.append(warning) @@ -70,14 +70,21 @@ def apply_modifiers_and_sample( # Route to type-specific handler if isinstance(dist, (NormalDistribution, LognormalDistribution)): value = _apply_numeric_modifiers(dist, matching_modifiers, rng, agent) + triggered_indices = [idx for idx, _ in matching_modifiers] elif isinstance(dist, UniformDistribution): value = _apply_uniform_modifiers(dist, matching_modifiers, rng) + triggered_indices = [idx for idx, _ in matching_modifiers] elif isinstance(dist, BetaDistribution): value = _apply_beta_modifiers(dist, matching_modifiers, rng) + triggered_indices = [idx for idx, _ in matching_modifiers] elif isinstance(dist, CategoricalDistribution): - value = _apply_categorical_modifiers(dist, matching_modifiers, rng) + value, winner_index = _apply_categorical_modifiers( + dist, matching_modifiers, rng + ) + triggered_indices = [winner_index] if winner_index is not None else [] elif isinstance(dist, BooleanDistribution): - value = _apply_boolean_modifiers(dist, matching_modifiers, rng) + value, winner_index = _apply_boolean_modifiers(dist, matching_modifiers, rng) + triggered_indices = [winner_index] if winner_index is not None else [] else: raise ValueError(f"Unknown distribution type: {type(dist)}") @@ -192,57 +199,81 @@ def _apply_categorical_modifiers( dist: CategoricalDistribution, matching: list[tuple[int, Modifier]], rng: random.Random, -) -> str: +) -> tuple[str, int | None]: """ - Apply categorical modifiers (last weight_override wins). + Apply categorical modifiers with deterministic winner selection. Note: If modifiers only have multiply/add (legacy numeric modifiers on categorical), - we ignore them and use base weights. + they are ignored and base weights are used. """ - # Find the last modifier with weight_overrides - override_weights: list[float] | None = None + weighted_modifiers = [(idx, mod) for idx, mod in matching if mod.weight_overrides] + winner_index, winner_modifier = _select_winner_modifier(weighted_modifiers) - for _, mod in matching: - if mod.weight_overrides: - # Build new weights list from overrides - new_weights = [] - for option in dist.options: - if option in mod.weight_overrides: - new_weights.append(mod.weight_overrides[option]) - else: - # Keep original weight for options not in override - idx = dist.options.index(option) - new_weights.append(dist.weights[idx]) - override_weights = new_weights + if winner_modifier is None or winner_modifier.weight_overrides is None: + return _sample_categorical(dist, rng, None), None - return _sample_categorical(dist, rng, override_weights) + base_weights = dict(zip(dist.options, dist.weights, strict=False)) + override_weights = [ + winner_modifier.weight_overrides.get(option, base_weights[option]) + for option in dist.options + ] + return _sample_categorical(dist, rng, override_weights), winner_index def _apply_boolean_modifiers( dist: BooleanDistribution, matching: list[tuple[int, Modifier]], rng: random.Random, -) -> bool: +) -> tuple[bool, int | None]: """ - Apply boolean modifiers (last probability_override wins). + Apply boolean modifiers with deterministic winner selection. If modifiers use multiply/add instead of probability_override, - apply to probability: new_prob = (base_prob * multiply) + add, clamped to [0,1]. + apply to probability using the selected winning modifier: + new_prob = (base_prob * multiply) + add, clamped to [0,1]. """ probability = dist.probability_true - for _, mod in matching: - if mod.probability_override is not None: - # Direct override wins - probability = mod.probability_override + effective = [] + for idx, mod in matching: + has_effect = ( + mod.probability_override is not None + or (mod.multiply is not None and mod.multiply != 1.0) + or (mod.add is not None and mod.add != 0) + ) + if has_effect: + effective.append((idx, mod)) + + winner_index, winner_modifier = _select_winner_modifier(effective) + if winner_modifier is not None: + if winner_modifier.probability_override is not None: + probability = winner_modifier.probability_override else: - # Apply multiply/add to probability - if mod.multiply is not None: - probability *= mod.multiply - if mod.add is not None: - probability += mod.add + if winner_modifier.multiply is not None: + probability *= winner_modifier.multiply + if winner_modifier.add is not None: + probability += winner_modifier.add # Clamp probability to [0, 1] probability = max(0.0, min(1.0, probability)) - return _sample_boolean(dist, rng, probability) + return _sample_boolean(dist, rng, probability), winner_index + + +def _select_winner_modifier( + matching: list[tuple[int, Modifier]], +) -> tuple[int | None, Modifier | None]: + """Pick one deterministic winner from matching modifiers.""" + if not matching: + return None, None + + decision = choose_modifier_precedence([(idx, mod.when) for idx, mod in matching]) + if decision is None: + return None, None + + by_index = {idx: mod for idx, mod in matching} + winner = by_index.get(decision.winner_index) + if winner is None: + return None, None + + return decision.winner_index, winner diff --git a/extropy/population/spec_builder/hydrator.py b/extropy/population/spec_builder/hydrator.py index 6898f71..e5e390d 100644 --- a/extropy/population/spec_builder/hydrator.py +++ b/extropy/population/spec_builder/hydrator.py @@ -110,6 +110,55 @@ def _should_hydrate_household_config( return False +def _extract_gender_options_for_household( + context: list[AttributeSpec] | None, + hydrated_attrs: list[HydratedAttribute], +) -> list[str]: + """Extract explicit gender-category options for household partner pairing.""" + + def options_for(attr: AttributeSpec) -> list[str]: + if attr.type != "categorical" or attr.sampling.distribution is None: + return [] + dist = attr.sampling.distribution + if not hasattr(dist, "options"): + return [] + opts = getattr(dist, "options", None) or [] + return [str(o) for o in opts if str(o).strip()] + + # Prefer semantic metadata over name heuristics. + candidates: list[AttributeSpec] = [] + for attr in (context or []): + if attr.identity_type == "gender_identity": + candidates.append(attr) + for attr in hydrated_attrs: + if attr.identity_type == "gender_identity": + candidates.append(attr) + + # Legacy fallback for older specs lacking identity_type metadata. + if not candidates: + fallback_names = {"gender", "sex", "gender_identity"} + for attr in (context or []): + if attr.name in fallback_names: + candidates.append(attr) + for attr in hydrated_attrs: + if attr.name in fallback_names: + candidates.append(attr) + + for attr in candidates: + opts = options_for(attr) + if opts: + # Preserve original option casing/order from spec. + seen: set[str] = set() + ordered: list[str] = [] + for value in opts: + if value not in seen: + seen.add(value) + ordered.append(value) + return ordered + + return [] + + # ============================================================================= # Main Orchestrator # ============================================================================= @@ -303,9 +352,21 @@ def on_retry(attempt: int, max_retries: int, error_summary: str): # Step 2e: Household config (optional; enabled by scenario stage) if include_household and _should_hydrate_household_config(population, attributes): report("2e", "Researching household composition...") + merged_attribute_names = sorted( + { + *(attr.name for attr in attributes), + *(attr.name for attr in context or []), + } + ) + gender_options = _extract_gender_options_for_household( + context=context, + hydrated_attrs=independent_attrs + derived_attrs + conditional_attrs, + ) household_config, hh_sources = hydrate_household_config( population=population, geography=geography, + allowed_attributes=merged_attribute_names, + allowed_gender_values=gender_options, model=model, reasoning_effort=reasoning_effort, on_retry=make_retry_callback("2e"), diff --git a/extropy/population/spec_builder/hydrators/household.py b/extropy/population/spec_builder/hydrators/household.py index 8442182..5ecede7 100644 --- a/extropy/population/spec_builder/hydrators/household.py +++ b/extropy/population/spec_builder/hydrators/household.py @@ -15,6 +15,8 @@ def hydrate_household_config( population: str, geography: str | None = None, + allowed_attributes: list[str] | None = None, + allowed_gender_values: list[str] | None = None, model: str | None = None, reasoning_effort: str = "low", on_retry: RetryCallback | None = None, @@ -29,6 +31,8 @@ def hydrate_household_config( Args: population: Population description (e.g., "Japanese retirees in Osaka") geography: Geographic scope (e.g., "Japan") + allowed_attributes: Exact merged population attribute names. When provided, + assortative_mating.attribute is constrained to this set. model: Model to use reasoning_effort: "low", "medium", or "high" on_retry: Callback for retry notifications @@ -38,6 +42,26 @@ def hydrate_household_config( """ geo_context = f" in {geography}" if geography else "" + allowed_text = "" + if allowed_attributes: + allowed_list = ", ".join(sorted(set(allowed_attributes))) + allowed_text = f""" +14. **Assortative-mating attribute constraint**: + - `assortative_mating[].attribute` MUST be one of these exact attribute names: + {allowed_list} + - Do not invent aliases or alternate labels. +""" + + gender_text = "" + if allowed_gender_values: + gender_list = ", ".join(sorted(set(allowed_gender_values))) + gender_text = f""" +15. **Partner gender pair constraint**: + - `partner_gender_pair_weights[].left/right` MUST use these exact values: + {gender_list} + - Treat pairs as unordered (male/female same as female/male). +""" + prompt = f"""Research household composition data for {population}{geo_context}. For this population, provide statistically grounded values for: @@ -77,7 +101,17 @@ def hydrate_household_config( 12. **Average household size**: Mean number of persons per household. -13. **Sources**: List of URLs or citations used. +13. **Partner gender pairing policy**: + - partner_gender_mode: "independent" or "weighted" + - partner_gender_pair_weights: Array of objects {{left, right, weight}} where: + - left/right are gender-category values from the population + - weight is non-negative + - weights should sum to ~1.0 across listed pairs + - Use "weighted" only when good regional evidence exists; otherwise "independent". + +14. **Sources**: List of URLs or citations used. +{allowed_text} +{gender_text} ## Research Guidelines @@ -92,7 +126,10 @@ def hydrate_household_config( try: data, sources = agentic_research( prompt=prompt, - response_schema=build_household_config_schema(), + response_schema=build_household_config_schema( + allowed_assortative_attributes=allowed_attributes, + allowed_gender_values=allowed_gender_values, + ), schema_name="household_config", model=model, reasoning_effort=reasoning_effort, @@ -105,7 +142,11 @@ def hydrate_household_config( return HouseholdConfig(), [] try: - config = _parse_household_config(data) + config = _parse_household_config( + data, + allowed_attributes=allowed_attributes, + allowed_gender_values=allowed_gender_values, + ) # Merge any sources from the response into the source list response_sources = data.get("sources", []) all_sources = list(set(sources + [s for s in response_sources if s])) @@ -118,7 +159,11 @@ def hydrate_household_config( return HouseholdConfig(), sources -def _parse_household_config(data: dict) -> HouseholdConfig: +def _parse_household_config( + data: dict, + allowed_attributes: list[str] | None = None, + allowed_gender_values: list[str] | None = None, +) -> HouseholdConfig: """Parse LLM response into a HouseholdConfig, falling back to defaults for bad fields. Converts array-of-objects LLM output back to the dict/tuple structures that @@ -188,14 +233,52 @@ def _parse_household_config(data: dict) -> HouseholdConfig: kwargs[field] = int(data[field]) # Assortative mating: [{attribute, correlation}] -> {str: float} + allowed_attr_set = set(allowed_attributes or []) if "assortative_mating" in data and isinstance(data["assortative_mating"], list): mating = {} for item in data["assortative_mating"]: if isinstance(item, dict) and "attribute" in item and "correlation" in item: - mating[str(item["attribute"])] = float(item["correlation"]) + attr_name = str(item["attribute"]) + if allowed_attr_set and attr_name not in allowed_attr_set: + continue + mating[attr_name] = float(item["correlation"]) if mating: kwargs["assortative_mating"] = mating + if "partner_gender_mode" in data and isinstance(data["partner_gender_mode"], str): + mode = data["partner_gender_mode"].strip().lower() + if mode in {"independent", "weighted"}: + kwargs["partner_gender_mode"] = mode + + allowed_gender_set = set(allowed_gender_values or []) + if "partner_gender_pair_weights" in data and isinstance( + data["partner_gender_pair_weights"], list + ): + pair_weights = [] + for item in data["partner_gender_pair_weights"]: + if not ( + isinstance(item, dict) + and "left" in item + and "right" in item + and "weight" in item + ): + continue + left = str(item["left"]) + right = str(item["right"]) + if allowed_gender_set and ( + left not in allowed_gender_set or right not in allowed_gender_set + ): + continue + try: + weight = float(item["weight"]) + except Exception: + continue + if weight < 0: + continue + pair_weights.append({"left": left, "right": right, "weight": weight}) + if pair_weights: + kwargs["partner_gender_pair_weights"] = pair_weights + # Life stages if "life_stages" in data and isinstance(data["life_stages"], list): stages = [] diff --git a/extropy/population/spec_builder/schemas.py b/extropy/population/spec_builder/schemas.py index 6fc89a0..bb79ae4 100644 --- a/extropy/population/spec_builder/schemas.py +++ b/extropy/population/spec_builder/schemas.py @@ -245,13 +245,30 @@ def build_conditional_base_schema() -> dict: } -def build_household_config_schema() -> dict: +def build_household_config_schema( + allowed_assortative_attributes: list[str] | None = None, + allowed_gender_values: list[str] | None = None, +) -> dict: """Build JSON schema for household config hydration. Uses array-of-objects patterns instead of dict/tuple schemas for LLM compatibility. Both Anthropic and OpenAI structured outputs require additionalProperties: false (not a schema) and don't support tuple-style array items. """ + attribute_schema: dict = {"type": "string"} + if allowed_assortative_attributes: + # Constrain to exact merged attribute names to avoid silent runtime ignores. + attribute_schema = { + "type": "string", + "enum": sorted(set(allowed_assortative_attributes)), + } + gender_value_schema: dict = {"type": "string"} + if allowed_gender_values: + gender_value_schema = { + "type": "string", + "enum": sorted(set(allowed_gender_values)), + } + return { "type": "object", "properties": { @@ -312,13 +329,30 @@ def build_household_config_schema() -> dict: "items": { "type": "object", "properties": { - "attribute": {"type": "string"}, + "attribute": attribute_schema, "correlation": {"type": "number"}, }, "required": ["attribute", "correlation"], "additionalProperties": False, }, }, + "partner_gender_mode": { + "type": "string", + "enum": ["independent", "weighted"], + }, + "partner_gender_pair_weights": { + "type": "array", + "items": { + "type": "object", + "properties": { + "left": gender_value_schema, + "right": gender_value_schema, + "weight": {"type": "number"}, + }, + "required": ["left", "right", "weight"], + "additionalProperties": False, + }, + }, "partner_age_gap_mean": {"type": "number"}, "partner_age_gap_std": {"type": "number"}, "min_adult_age": {"type": "integer"}, @@ -353,6 +387,8 @@ def build_household_config_schema() -> dict: "same_group_rates", "default_same_group_rate", "assortative_mating", + "partner_gender_mode", + "partner_gender_pair_weights", "partner_age_gap_mean", "partner_age_gap_std", "min_adult_age", diff --git a/extropy/population/validator/semantic.py b/extropy/population/validator/semantic.py index 234fc38..e23511b 100644 --- a/extropy/population/validator/semantic.py +++ b/extropy/population/validator/semantic.py @@ -4,6 +4,10 @@ They help identify potential issues but don't indicate structural problems. """ +import ast +import itertools +import math + from ...core.models.validation import Severity, ValidationIssue from ...core.models import ( PopulationSpec, @@ -15,7 +19,8 @@ CategoricalDistribution, BooleanDistribution, ) -from ...utils import extract_comparisons_from_expression +from ..modifier_precedence import choose_modifier_precedence +from ...utils import extract_comparisons_from_expression, extract_names_from_expression # ============================================================================= @@ -48,7 +53,7 @@ def run_semantic_checks(spec: PopulationSpec) -> list[ValidationIssue]: issues.extend(_check_condition_values(attr, attr_lookup)) # Category 13: Last-Wins overlap detection for categorical/boolean - issues.extend(_check_last_wins_overlaps(attr)) + issues.extend(_check_last_wins_overlaps(attr, attr_lookup)) return issues @@ -260,8 +265,16 @@ def _check_condition_values( # ============================================================================= -def _check_last_wins_overlaps(attr: AttributeSpec) -> list[ValidationIssue]: - """Warn when categorical/boolean modifiers may overlap under last-wins semantics.""" +def _check_last_wins_overlaps( + attr: AttributeSpec, + attr_lookup: dict[str, AttributeSpec], +) -> list[ValidationIssue]: + """Warn when categorical/boolean modifiers overlap ambiguously. + + Overlap itself is not always problematic when precedence is deterministic + by condition specificity/subset. We only warn when overlap would still + resolve by declaration order. + """ issues: list[ValidationIssue] = [] dist = attr.sampling.distribution modifiers = attr.sampling.modifiers @@ -280,13 +293,6 @@ def is_unconditional(expr: str) -> bool: normalized = normalize_when(expr) return normalized in {"true", "1==1", "(true)"} - def compared_pairs(expr: str) -> set[tuple[str, str]]: - pairs: set[tuple[str, str]] = set() - for attr_name, values in extract_comparisons_from_expression(expr): - for value in values: - pairs.add((attr_name, str(value))) - return pairs - for i in range(len(modifiers)): for j in range(i + 1, len(modifiers)): left = modifiers[i].when @@ -300,24 +306,211 @@ def compared_pairs(expr: str) -> set[tuple[str, str]]: elif normalize_when(left) == normalize_when(right): may_overlap = True else: - left_pairs = compared_pairs(left) - right_pairs = compared_pairs(right) - if not left_pairs or not right_pairs: - may_overlap = True - elif left_pairs.intersection(right_pairs): - may_overlap = True + may_overlap = _conditions_can_both_be_true( + left, + right, + attr_lookup, + ) if may_overlap: + decision = choose_modifier_precedence([(i, left), (j, right)]) + if decision and decision.reason in {"subset", "specificity"}: + continue + issues.append( ValidationIssue( severity=Severity.WARNING, category="MODIFIER_OVERLAP", location=attr.name, message=( - f"modifiers {i} and {j} may overlap under last-wins semantics" + f"modifiers {i} and {j} overlap and currently rely on declaration order" + ), + suggestion=( + "Make conditions mutually exclusive or add specificity so one " + "rule deterministically dominates" ), - suggestion="Make conditions mutually exclusive or merge the modifier logic", ) ) return issues + + +def _conditions_can_both_be_true( + left: str, + right: str, + attr_lookup: dict[str, AttributeSpec], +) -> bool: + """Return True if both condition expressions appear jointly satisfiable.""" + referenced = sorted( + ( + extract_names_from_expression(left) | extract_names_from_expression(right) + ).intersection(set(attr_lookup.keys())) + ) + + # If no referenced attrs, evaluate directly. + if not referenced: + return _eval_condition_bool(left, {}) and _eval_condition_bool(right, {}) + + # If expressions reference unknown names, keep conservative warning behavior. + all_referenced = extract_names_from_expression( + left + ) | extract_names_from_expression(right) + unknown = [name for name in all_referenced if name not in attr_lookup] + if unknown: + return True + + domains: dict[str, list[object]] = {} + for name in referenced: + domains[name] = _candidate_values_for_attr(attr_lookup[name], [left, right]) + if not domains[name]: + return True + + domain_lists = [domains[name] for name in referenced] + total = 1 + for values in domain_lists: + total *= max(1, len(values)) + + max_combinations = 120_000 + if total > max_combinations: + trimmed = [] + for values in domain_lists: + if len(values) <= 18: + trimmed.append(values) + continue + step = max(1, len(values) // 18) + trimmed.append(values[::step][:18]) + domain_lists = trimmed + + for combo in itertools.product(*domain_lists): + env = dict(zip(referenced, combo, strict=False)) + if not _eval_condition_bool(left, env): + continue + if _eval_condition_bool(right, env): + return True + + return False + + +def _eval_condition_bool(expr: str, env: dict[str, object]) -> bool: + """Evaluate condition expression; return False on evaluation errors.""" + try: + return bool(eval(expr, {"__builtins__": {}}, env)) + except Exception: + return False + + +def _candidate_values_for_attr( + attr: AttributeSpec, + expressions: list[str], +) -> list[object]: + """Build a bounded candidate set for condition satisfiability checks.""" + dist = attr.sampling.distribution + + if attr.type == "boolean": + return [True, False] + + if attr.type == "categorical" and isinstance(dist, CategoricalDistribution): + return list(dist.options or []) + + if attr.type in {"int", "float"}: + values: set[float] = set() + min_val, max_val = _numeric_bounds(attr) + + if min_val is not None: + values.add(min_val) + if max_val is not None: + values.add(max_val) + if min_val is not None and max_val is not None and min_val <= max_val: + values.add((min_val + max_val) / 2.0) + + for expr in expressions: + for constant in _extract_numeric_constants(expr): + values.add(constant) + values.add(constant - 1) + values.add(constant + 1) + values.add(constant - 0.5) + values.add(constant + 0.5) + + if not values: + values = {0.0, 1.0, 10.0} + + bounded: list[float] = [] + for value in values: + if min_val is not None and value < min_val: + continue + if max_val is not None and value > max_val: + continue + bounded.append(value) + + if attr.type == "int": + ints: list[int] = [] + for value in bounded: + ints.extend( + [ + int(math.floor(value)), + int(round(value)), + int(math.ceil(value)), + ] + ) + ints = sorted(set(ints)) + if len(ints) > 80: + step = max(1, len(ints) // 80) + ints = ints[::step] + return ints + + floats = sorted(set(round(value, 6) for value in bounded)) + if len(floats) > 80: + step = max(1, len(floats) // 80) + floats = floats[::step] + return floats + + return [] + + +def _numeric_bounds(attr: AttributeSpec) -> tuple[float | None, float | None]: + """Infer numeric min/max from distribution bounds and hard constraints.""" + min_val = None + max_val = None + + dist = attr.sampling.distribution + if isinstance( + dist, + ( + NormalDistribution, + LognormalDistribution, + UniformDistribution, + BetaDistribution, + ), + ): + min_val = dist.min + max_val = dist.max + + for constraint in attr.constraints: + if constraint.type == "hard_min" and constraint.value is not None: + if min_val is None: + min_val = float(constraint.value) + else: + min_val = max(min_val, float(constraint.value)) + elif constraint.type == "hard_max" and constraint.value is not None: + if max_val is None: + max_val = float(constraint.value) + else: + max_val = min(max_val, float(constraint.value)) + + return min_val, max_val + + +def _extract_numeric_constants(expr: str) -> set[float]: + """Extract numeric constants from an expression AST.""" + out: set[float] = set() + try: + tree = ast.parse(expr, mode="eval") + except SyntaxError: + return out + + for node in ast.walk(tree): + if isinstance(node, ast.Constant) and isinstance(node.value, (int, float)): + if isinstance(node.value, bool): + continue + out.add(float(node.value)) + return out diff --git a/extropy/scenario/compiler.py b/extropy/scenario/compiler.py index 6918df2..4df8fbb 100644 --- a/extropy/scenario/compiler.py +++ b/extropy/scenario/compiler.py @@ -30,6 +30,7 @@ from .timeline import generate_timeline_and_outcomes from .sampling_semantics import generate_sampling_semantic_roles from ..utils.callbacks import StepProgressCallback +from ..utils import topological_sort from .validator import validate_scenario from ..storage import open_study_db @@ -413,11 +414,20 @@ def progress(step: str, status: str): ext_attrs = list(extended_attributes or []) merged_population = population_spec if ext_attrs: + merged_attributes = list(population_spec.attributes) + ext_attrs + merged_deps: dict[str, list[str]] = { + attr.name: list(attr.sampling.depends_on or []) for attr in merged_attributes + } + merged_names = set(merged_deps.keys()) + merged_deps = { + name: [dep for dep in deps if dep in merged_names] + for name, deps in merged_deps.items() + } merged_population = PopulationSpec( meta=population_spec.meta.model_copy(), grounding=population_spec.grounding, - attributes=list(population_spec.attributes) + ext_attrs, - sampling_order=population_spec.merged_sampling_order(ext_attrs), + attributes=merged_attributes, + sampling_order=topological_sort(merged_deps), ) # Step 1: Parse scenario description diff --git a/extropy/scenario/interaction.py b/extropy/scenario/interaction.py index d6d969c..319e7a9 100644 --- a/extropy/scenario/interaction.py +++ b/extropy/scenario/interaction.py @@ -4,6 +4,8 @@ to the event and configures how information spreads through the network. """ +import json + from ..core.llm import reasoning_call from ..core.models import ( PopulationSpec, @@ -109,9 +111,22 @@ def determine_interaction_model( 0.35 """ # Build attribute list for spread modifiers - attribute_info = "\n".join( - f"- {attr.name} ({attr.type})" for attr in population_spec.attributes - ) + attr_lines: list[str] = [] + for attr in population_spec.attributes: + line = f"- {attr.name} ({attr.type})" + dist = getattr(attr.sampling, "distribution", None) + if ( + attr.type == "categorical" + and dist is not None + and hasattr(dist, "options") + and dist.options + ): + line += f" options={json.dumps(list(dist.options), ensure_ascii=False)}" + elif attr.type == "boolean": + line += " options=[true, false]" + attr_lines.append(line) + + attribute_info = "\n".join(attr_lines) # Build edge type info if available edge_type_info = "" @@ -134,7 +149,8 @@ def determine_interaction_model( ## Event Type: {event.type.value} -Content: "{event.content[:200]}..." +Content: +\"\"\"{event.content}\"\"\" Source: {event.source} Credibility: {event.credibility:.2f} Emotional valence: {event.emotional_valence:.2f} @@ -171,6 +187,11 @@ def determine_interaction_model( - Professionals share more on work topics - Close relationships share more +Strict literal contract for conditions: +- For categorical/boolean attributes, use values exactly as listed in `options` (same case and punctuation). +- Do NOT use snake_case aliases, paraphrases, abbreviations, or inferred synonyms. +- If you cannot map a concept to an exact listed option, do not emit that categorical condition. + Format: {{"when": "expression", "multiply": 1.5, "add": 0.0}} Examples: diff --git a/extropy/scenario/sampling_semantics.py b/extropy/scenario/sampling_semantics.py index 13ba275..abd465d 100644 --- a/extropy/scenario/sampling_semantics.py +++ b/extropy/scenario/sampling_semantics.py @@ -69,6 +69,14 @@ "required": ["religion_attr", "secular_values"], "additionalProperties": False, }, + "household_roles": { + "type": "object", + "properties": { + "household_size_attr": {"type": ["string", "null"]}, + }, + "required": ["household_size_attr"], + "additionalProperties": False, + }, }, "required": [ "marital_roles", @@ -76,6 +84,7 @@ "partner_correlation_roles", "school_parent_role", "religion_roles", + "household_roles", ], "additionalProperties": False, } @@ -151,6 +160,9 @@ def _build_prompt(population_spec: PopulationSpec) -> str: - religion_attr: religion/faith affiliation attribute - secular_values: values indicating no religion +6) household_roles: +- household_size_attr: attribute representing realized household size + Rules: - Never invent attribute names. - If a role is unavailable, use null and empty arrays/maps. @@ -228,6 +240,9 @@ def keep_values(attr_name: str | None, values: Any) -> list[str]: if isinstance(v, str) and v.strip() ] + household_raw = data.get("household_roles") or {} + household_size_attr = keep_attr(household_raw.get("household_size_attr")) + return SamplingSemanticRoles.model_validate( { "marital_roles": { @@ -245,6 +260,9 @@ def keep_values(attr_name: str | None, values: Any) -> list[str]: "religion_attr": religion_attr, "secular_values": secular_values, }, + "household_roles": { + "household_size_attr": household_size_attr, + }, } ) diff --git a/extropy/scenario/validator.py b/extropy/scenario/validator.py index 16e6d67..3414826 100644 --- a/extropy/scenario/validator.py +++ b/extropy/scenario/validator.py @@ -293,6 +293,36 @@ def _validate_sampling_semantic_roles( ) ) + household = roles.household_roles + if household and household.household_size_attr: + attr = attr_lookup.get(household.household_size_attr) + if attr is None: + issues.append( + ValidationError( + category="sampling_semantics", + location="sampling_semantic_roles.household_roles.household_size_attr", + message=( + "Unknown household_size_attr: " + f"'{household.household_size_attr}'" + ), + suggestion="Use an attribute name from base or extended attributes", + ) + ) + else: + attr_type = getattr(attr, "type", None) + if attr_type not in ("int", "float"): + issues.append( + ValidationError( + category="sampling_semantics", + location="sampling_semantic_roles.household_roles.household_size_attr", + message=( + "household_size_attr must reference numeric attribute, " + f"got type '{attr_type}'" + ), + suggestion="Map household_size_attr to an int/float attribute", + ) + ) + return issues diff --git a/tests/test_household_hydrator.py b/tests/test_household_hydrator.py new file mode 100644 index 0000000..5eada1f --- /dev/null +++ b/tests/test_household_hydrator.py @@ -0,0 +1,27 @@ +from extropy.population.spec_builder.schemas import build_household_config_schema +from extropy.population.spec_builder.hydrators.household import _parse_household_config + + +def test_household_schema_constrains_assortative_attribute_enum(): + schema = build_household_config_schema( + allowed_assortative_attributes=["education_level", "political_identity"] + ) + + attribute_schema = schema["properties"]["assortative_mating"]["items"]["properties"][ + "attribute" + ] + assert attribute_schema["type"] == "string" + assert attribute_schema["enum"] == ["education_level", "political_identity"] + + +def test_parse_household_config_filters_unknown_assortative_attributes(): + data = { + "assortative_mating": [ + {"attribute": "education_level", "correlation": 0.65}, + {"attribute": "technology_adoption_posture", "correlation": 0.45}, + ] + } + + config = _parse_household_config(data, allowed_attributes=["education_level"]) + + assert config.assortative_mating == {"education_level": 0.65} diff --git a/tests/test_modifier_precedence.py b/tests/test_modifier_precedence.py new file mode 100644 index 0000000..a1f80ec --- /dev/null +++ b/tests/test_modifier_precedence.py @@ -0,0 +1,211 @@ +"""Tests for deterministic modifier precedence and overlap warnings.""" + +import random +from datetime import datetime + +from extropy.core.models.population import ( + AttributeSpec, + CategoricalDistribution, + GroundingInfo, + GroundingSummary, + Modifier, + PopulationSpec, + SamplingConfig, + SpecMeta, + UniformDistribution, + BooleanDistribution, +) +from extropy.population.modifier_precedence import choose_modifier_precedence +from extropy.population.sampler.modifiers import apply_modifiers_and_sample +from extropy.population.validator.semantic import run_semantic_checks + + +def _categorical_attr(name: str, options: list[str]) -> AttributeSpec: + return AttributeSpec( + name=name, + type="categorical", + category="population_specific", + description=name, + sampling=SamplingConfig( + strategy="independent", + distribution=CategoricalDistribution( + type="categorical", + options=options, + weights=[1.0 / len(options)] * len(options), + ), + ), + grounding=GroundingInfo(level="low", method="estimated"), + ) + + +def _int_attr(name: str, low: float = 0.0, high: float = 100.0) -> AttributeSpec: + return AttributeSpec( + name=name, + type="int", + category="universal", + description=name, + sampling=SamplingConfig( + strategy="independent", + distribution=UniformDistribution(type="uniform", min=low, max=high), + ), + grounding=GroundingInfo(level="low", method="estimated"), + ) + + +def _spec(attrs: list[AttributeSpec], order: list[str]) -> PopulationSpec: + return PopulationSpec( + meta=SpecMeta( + description="modifier precedence test", + geography="test", + created_at=datetime(2024, 1, 1), + version="1.0", + ), + grounding=GroundingSummary( + overall="low", + sources_count=0, + strong_count=0, + medium_count=0, + low_count=1, + sources=[], + ), + attributes=attrs, + sampling_order=order, + ) + + +def test_choose_modifier_precedence_subset_wins(): + decision = choose_modifier_precedence( + [ + (3, "age >= 50"), + (7, "age >= 65"), + ] + ) + assert decision is not None + assert decision.winner_index == 7 + assert decision.reason == "subset" + + +def test_choose_modifier_precedence_specificity_wins(): + decision = choose_modifier_precedence( + [ + (2, "education_level == 'Graduate degree'"), + ( + 4, + "education_level == 'Graduate degree' and race_ethnicity == 'Asian'", + ), + ] + ) + assert decision is not None + assert decision.winner_index == 4 + assert decision.reason in {"subset", "specificity"} + + +def test_choose_modifier_precedence_order_wins_on_tie(): + decision = choose_modifier_precedence( + [ + (1, "us_region == 'West' and race_ethnicity == 'Asian'"), + (6, "us_region == 'West' and education_level == 'Graduate degree'"), + ] + ) + assert decision is not None + assert decision.winner_index == 6 + assert decision.reason == "order" + + +def test_boolean_modifiers_use_single_deterministic_winner(): + distribution = BooleanDistribution(type="boolean", probability_true=0.2) + modifiers = [ + Modifier(when="age >= 18", add=0.2), + Modifier(when="age >= 18 and household_income >= 100000", add=0.5), + ] + _, triggered, warnings = apply_modifiers_and_sample( + dist=distribution, + modifiers=modifiers, + rng=random.Random(42), + agent={"age": 42, "household_income": 120000}, + ) + assert warnings == [] + assert triggered == [1] + + +def test_semantic_overlap_suppressed_when_subset_or_specificity_resolves(): + status = AttributeSpec( + name="status", + type="categorical", + category="population_specific", + description="status", + sampling=SamplingConfig( + strategy="conditional", + distribution=CategoricalDistribution( + type="categorical", + options=["A", "B"], + weights=[0.6, 0.4], + ), + depends_on=["age", "education_level"], + modifiers=[ + Modifier( + when="age >= 50", + weight_overrides={"A": 0.2, "B": 0.8}, + ), + Modifier( + when="age >= 50 and education_level == 'Graduate degree'", + weight_overrides={"A": 0.8, "B": 0.2}, + ), + ], + ), + grounding=GroundingInfo(level="low", method="estimated"), + ) + spec = _spec( + [ + _int_attr("age", low=18, high=90), + _categorical_attr("education_level", ["HS", "Graduate degree"]), + status, + ], + ["age", "education_level", "status"], + ) + + issues = run_semantic_checks(spec) + overlap_issues = [issue for issue in issues if issue.category == "MODIFIER_OVERLAP"] + assert overlap_issues == [] + + +def test_semantic_overlap_warns_when_only_order_can_resolve(): + identity = AttributeSpec( + name="identity_segment", + type="categorical", + category="population_specific", + description="identity_segment", + sampling=SamplingConfig( + strategy="conditional", + distribution=CategoricalDistribution( + type="categorical", + options=["X", "Y"], + weights=[0.5, 0.5], + ), + depends_on=["us_region", "race_ethnicity", "education_level"], + modifiers=[ + Modifier( + when="us_region == 'West' and race_ethnicity == 'Asian'", + weight_overrides={"X": 0.8, "Y": 0.2}, + ), + Modifier( + when="us_region == 'West' and education_level == 'Graduate degree'", + weight_overrides={"X": 0.2, "Y": 0.8}, + ), + ], + ), + grounding=GroundingInfo(level="low", method="estimated"), + ) + spec = _spec( + [ + _categorical_attr("us_region", ["West", "South"]), + _categorical_attr("race_ethnicity", ["Asian", "White"]), + _categorical_attr("education_level", ["HS", "Graduate degree"]), + identity, + ], + ["us_region", "race_ethnicity", "education_level", "identity_segment"], + ) + + issues = run_semantic_checks(spec) + overlap_issues = [issue for issue in issues if issue.category == "MODIFIER_OVERLAP"] + assert len(overlap_issues) == 1 diff --git a/tests/test_scenario_interaction.py b/tests/test_scenario_interaction.py new file mode 100644 index 0000000..2d1538b --- /dev/null +++ b/tests/test_scenario_interaction.py @@ -0,0 +1,45 @@ +"""Tests for scenario interaction prompt grounding.""" + +from extropy.core.models import Event, EventType +from extropy.scenario import interaction as interaction_module + + +def test_interaction_prompt_uses_full_event_content_and_option_vocab( + minimal_population_spec, monkeypatch +): + captured: dict[str, str] = {} + + def fake_reasoning_call(*, prompt, **kwargs): + captured["prompt"] = prompt + return { + "interaction_description": "Test dynamics", + "share_probability": 0.35, + "share_modifiers": [ + {"when": "gender == 'female'", "multiply": 1.1, "add": 0.0}, + ], + "decay_per_hop": 0.1, + "max_hops": 4, + "reasoning": "test", + } + + monkeypatch.setattr(interaction_module, "reasoning_call", fake_reasoning_call) + + long_content = "x" * 240 + "TAIL_MARKER_FOR_FULL_CONTENT" + event = Event( + type=EventType.ANNOUNCEMENT, + content=long_content, + source="Test Source", + credibility=0.9, + ambiguity=0.2, + emotional_valence=0.1, + ) + + interaction_module.determine_interaction_model( + event=event, + population_spec=minimal_population_spec, + ) + + prompt = captured.get("prompt", "") + assert "TAIL_MARKER_FOR_FULL_CONTENT" in prompt + assert "gender (categorical) options=" in prompt + assert "Strict literal contract for conditions" in prompt From 8c3960fa097b616dd8393e95d0b401ca12c1d7e4 Mon Sep 17 00:00:00 2001 From: RandomOscillations Date: Sun, 22 Feb 2026 00:31:20 -0500 Subject: [PATCH 07/20] Harden sampler household coherence and lifecycle normalization --- extropy/core/models/population.py | 1 + extropy/population/sampler/core.py | 339 ++++++++++++++++++++++- extropy/population/sampler/households.py | 9 +- tests/test_household_sampling.py | 216 +++++++++++++++ 4 files changed, 558 insertions(+), 7 deletions(-) diff --git a/extropy/core/models/population.py b/extropy/core/models/population.py index 9f52c5b..94b085e 100644 --- a/extropy/core/models/population.py +++ b/extropy/core/models/population.py @@ -148,6 +148,7 @@ class HouseholdConfig(BaseModel): max_dependent_child_age: int = 17 elderly_min_offset: int = 22 elderly_max_offset: int = 35 + max_elderly_dependent_age: int = 105 # Life stages (replaces hardcoded _school_status) life_stages: list[LifeStageThreshold] = Field( diff --git a/extropy/population/sampler/core.py b/extropy/population/sampler/core.py index 00cd279..4c7027d 100644 --- a/extropy/population/sampler/core.py +++ b/extropy/population/sampler/core.py @@ -116,6 +116,108 @@ def _coerce_minor_income(value: Any, options: list[str] | None) -> Any: return 0 +def _coerce_young_adult_education(value: Any, age: int, options: list[str]) -> Any: + """Coerce implausible early education stages for young adults.""" + if age >= 22: + return value + if not isinstance(value, str): + return value + + def stage(option: str) -> int: + token = _normalize_attr_token(option) + if any(k in token for k in ("phd", "doctorate", "doctoral", "graduate", "masters")): + return 5 + if any(k in token for k in ("bachelor", "ba", "bs")): + return 4 + if "associate" in token: + return 3 + if "some_college" in token or "college" in token: + return 2 + if "high_school" in token or "hs" in token or "ged" in token: + return 1 + return 0 + + current_stage = stage(value) + if age < 20 and current_stage >= 4: + allowed_max = 2 + elif age < 22 and current_stage >= 5: + allowed_max = 4 + else: + return value + + candidates = [opt for opt in options if stage(opt) <= allowed_max] + if not candidates: + return value + return max(candidates, key=stage) + + +def _coerce_early_retirement(value: Any, age: int, options: list[str]) -> Any: + """Coerce retirement status for very young adults.""" + if age >= 30: + return value + if not isinstance(value, str): + return value + + value_token = _normalize_attr_token(value) + if "retired" not in value_token: + return value + + preferred_tokens = ( + "student", + "intern", + "part_time", + "gig", + "self", + "private", + "public", + "government", + "unemployed", + "labor_force", + "employed", + ) + for token in preferred_tokens: + for opt in options: + norm = _normalize_attr_token(opt) + if "retired" in norm: + continue + if token in norm: + return opt + + for opt in options: + if "retired" not in _normalize_attr_token(opt): + return opt + return value + + +def _normalize_young_adult_attributes( + agent: dict[str, Any], + spec: PopulationSpec, +) -> None: + """Normalize rare but implausible lifecycle combinations for young adults.""" + age = agent.get("age") + if not isinstance(age, (int, float)): + return + age_int = int(age) + if age_int >= 30: + return + + for attr in spec.attributes: + if attr.name not in agent: + continue + if ( + attr.sampling.distribution is None + or not hasattr(attr.sampling.distribution, "options") + or not attr.sampling.distribution.options + ): + continue + options = list(attr.sampling.distribution.options) + value = agent[attr.name] + if attr.semantic_type == "education": + agent[attr.name] = _coerce_young_adult_education(value, age_int, options) + elif attr.semantic_type == "employment": + agent[attr.name] = _coerce_early_retirement(value, age_int, options) + + def _normalize_minor_attributes( agent: dict[str, Any], spec: PopulationSpec, @@ -379,7 +481,11 @@ def sample_population( semantic_roles=semantic_roles, ) _reconcile_household_coherence( - agents, attr_map, stats, semantic_roles=semantic_roles + agents, + attr_map, + stats, + semantic_roles=semantic_roles, + household_config=hh_config, ) else: agents = _sample_population_independent( @@ -647,6 +753,7 @@ def _sample_population_households( marital_attr = _resolve_marital_attr_name(attr_map, semantic_roles) household_size_attr = _resolve_household_size_attr_name(attr_map, semantic_roles) gender_attr = _resolve_gender_attr_name(attr_map) + parental_status_attrs = _resolve_parental_status_attrs(attr_map) marital_spec = attr_map.get(marital_attr) if marital_attr else None categorical_options: dict[str, list[str]] = {} for attr in spec.attributes: @@ -811,17 +918,56 @@ def _sample_population_households( if adult2_added: household_members.append(adult2) household_members.extend(promoted_dependents) + adult_members = [ + member + for member in household_members + if str(member.get("household_role", "")).startswith("adult_") + ] # In household-mode, household-coupled fields should reflect realized structure # before post-sampling reconciliation/gating runs. if household_size_attr: npc_partner_count = 1 if has_partner and not adult2_added else 0 realized_size = len(household_members) + npc_partner_count + len(dep_dicts) - for member in household_members: + for member in adult_members: member[household_size_attr] = realized_size + if parental_status_attrs: + child_dependents_present = any( + isinstance(dep, dict) + and _dependent_is_child(dep, child_age_max=config.max_dependent_child_age) + for dep in dep_dicts + ) + if not child_dependents_present: + child_dependents_present = any( + str(member.get("household_role", "")).startswith("dependent_") + and _dependent_is_child( + { + "relationship": str(member.get("household_role", ""))[ + len("dependent_") : + ], + "age": member.get("age"), + }, + child_age_max=config.max_dependent_child_age, + ) + for member in household_members + ) + + for member in adult_members: + for attr_name in parental_status_attrs: + attr_spec = attr_map.get(attr_name) + if attr_spec is None: + continue + desired = _pick_parental_status_value( + attr_spec, + child_dependents_present, + current_value=member.get(attr_name), + ) + if desired is not None: + member[attr_name] = desired + if marital_spec and marital_attr: - for member in household_members: + for member in adult_members: has_partner_member = bool( member.get("partner_id") or member.get("partner_npc") ) @@ -831,6 +977,7 @@ def _sample_population_households( marital_roles=( semantic_roles.marital_roles if semantic_roles else None ), + current_value=member.get(marital_attr), ) if desired is not None: member[marital_attr] = desired @@ -948,6 +1095,8 @@ def _sample_partner_agent( agent[attr_name] = value _update_stats(attr, value, stats, numeric_values) + _normalize_young_adult_attributes(agent, spec) + if gender_attr and gender_attr in agent: partner_gender = choose_partner_gender( primary_gender=( @@ -1026,6 +1175,8 @@ def _sample_single_agent( # Update stats _update_stats(attr, value, stats, numeric_values) + _normalize_young_adult_attributes(agent, spec) + # Generate demographically-plausible name gender = agent.get("gender") or agent.get("sex") ethnicity = ( @@ -1269,6 +1420,117 @@ def _resolve_household_size_attr_name( return None +def _resolve_parental_status_attrs( + attr_map: dict[str, AttributeSpec], +) -> list[str]: + """Resolve parental-status attributes with semantic role first, aliases second.""" + names: list[str] = [] + for name, attr in attr_map.items(): + if attr.identity_type == "parental_status": + names.append(name) + + if names: + return names + + for alias in ( + "has_children", + "is_parent", + "parental_status", + "has_kids", + "children_at_home", + ): + candidate = _find_attr_name(attr_map, {alias}) + if candidate and candidate not in names: + names.append(candidate) + return names + + +def _dependent_is_child(dep: dict[str, Any], child_age_max: int = 17) -> bool: + """Detect child dependents from relationship/age/school metadata.""" + rel = _normalize_attr_token(str(dep.get("relationship", ""))) + if any( + token in rel + for token in ("son", "daughter", "child", "stepchild", "foster_child") + ): + return True + + age_raw = dep.get("age") + if isinstance(age_raw, str) and age_raw.strip().isdigit(): + age_raw = int(age_raw.strip()) + if isinstance(age_raw, (int, float)) and 0 <= int(age_raw) <= child_age_max: + return True + + school_status = _normalize_attr_token(str(dep.get("school_status", ""))) + school_tokens = { + "home", + "elementary", + "middle_school", + "high_school", + "primary", + "secondary", + "k12", + } + return school_status in school_tokens + + +def _pick_parental_status_value( + attr: AttributeSpec, + has_child_dependents: bool, + current_value: Any | None = None, +) -> Any | None: + """Pick a coherent parental-status value based on realized household children.""" + if attr.type == "boolean": + return has_child_dependents + + if ( + attr.type != "categorical" + or attr.sampling.distribution is None + or not hasattr(attr.sampling.distribution, "options") + ): + return None + + options = list(getattr(attr.sampling.distribution, "options", []) or []) + if not options: + return None + + if current_value in options: + norm_current = _normalize_attr_token(str(current_value)) + if has_child_dependents and any( + token in norm_current for token in ("child", "children", "parent") + ): + return current_value + if not has_child_dependents and any( + token in norm_current + for token in ( + "no_child", + "no_children", + "non_parent", + "not_parent", + "childfree", + ) + ): + return current_value + + yes_tokens = ("child", "children", "parent", "has_kid", "has_child", "yes", "true") + no_tokens = ( + "no_child", + "no_children", + "non_parent", + "not_parent", + "childfree", + "none", + "no", + "false", + ) + + token_set = yes_tokens if has_child_dependents else no_tokens + for opt in options: + norm = _normalize_attr_token(str(opt)) + if any(token in norm for token in token_set): + return opt + return None + + def _resolve_gender_attr_name(attr_map: dict[str, AttributeSpec]) -> str | None: """Resolve gender attribute by metadata first, then legacy alias fallback.""" for name, attr in attr_map.items(): @@ -1299,6 +1561,7 @@ def _pick_marital_value( attr: AttributeSpec, has_partner: bool, marital_roles: MaritalRoles | None = None, + current_value: Any | None = None, ) -> Any | None: """Pick a coherent marital value based on realized partner status.""" if attr.type == "boolean": @@ -1319,6 +1582,11 @@ def _pick_marital_value( if _normalize_attr_token(attr.name) == _normalize_attr_token( marital_roles.attr ): + if current_value in options: + if has_partner and current_value in marital_roles.partnered_values: + return current_value + if not has_partner and current_value in marital_roles.single_values: + return current_value preferred = ( marital_roles.partnered_values if has_partner @@ -1328,7 +1596,6 @@ def _pick_marital_value( if value in options: return value - normalized = {opt: _normalize_attr_token(opt) for opt in options} partnered_tokens = ( "married", "partner", @@ -1338,6 +1605,14 @@ def _pick_marital_value( ) single_tokens = ("single", "unmarried", "not_married", "divorced", "widowed") + if current_value in options: + norm_current = _normalize_attr_token(str(current_value)) + if has_partner and any(token in norm_current for token in partnered_tokens): + return current_value + if not has_partner and any(token in norm_current for token in single_tokens): + return current_value + + normalized = {opt: _normalize_attr_token(opt) for opt in options} token_set = partnered_tokens if has_partner else single_tokens for opt, norm in normalized.items(): if any(token in norm for token in token_set): @@ -1391,6 +1666,7 @@ def _reconcile_household_coherence( attr_map: dict[str, AttributeSpec], stats: SamplingStats, semantic_roles: SamplingSemanticRoles | None = None, + household_config: HouseholdConfig | None = None, ) -> None: """Deterministically reconcile household coherence after realization.""" by_household: dict[str, list[dict[str, Any]]] = {} @@ -1405,16 +1681,28 @@ def _reconcile_household_coherence( marital_attr = _resolve_marital_attr_name(attr_map, semantic_roles) household_size_attr = _resolve_household_size_attr_name(attr_map, semantic_roles) + parental_status_attrs = _resolve_parental_status_attrs(attr_map) + child_age_max = ( + household_config.max_dependent_child_age if household_config else 17 + ) marital_updates = 0 household_size_updates = 0 surname_updates = 0 + parental_status_updates = 0 for members in by_household.values(): primary = next( (m for m in members if m.get("household_role") == "adult_primary"), members[0], ) + adult_members = [ + member + for member in members + if str(member.get("household_role", "")).startswith("adult_") + ] + if not adult_members: + adult_members = members dependents = primary.get("dependents", []) npc_partner_count = 1 if primary.get("partner_npc") else 0 npc_dependents_count = len(dependents) if isinstance(dependents, list) else 0 @@ -1454,9 +1742,47 @@ def _reconcile_household_coherence( dep["name"] = corrected surname_updates += 1 + child_dependents_present = False + if isinstance(dependents, list): + child_dependents_present = any( + isinstance(dep, dict) and _dependent_is_child(dep, child_age_max) + for dep in dependents + ) + if not child_dependents_present: + child_dependents_present = any( + str(member.get("household_role", "")).startswith("dependent_") + and _dependent_is_child( + { + "relationship": str(member.get("household_role", ""))[ + len("dependent_") : + ], + "age": member.get("age"), + }, + child_age_max, + ) + for member in members + ) + + if parental_status_attrs: + for member in adult_members: + for attr_name in parental_status_attrs: + attr_spec = attr_map.get(attr_name) + if attr_spec is None: + continue + desired = _pick_parental_status_value( + attr_spec, + child_dependents_present, + current_value=member.get(attr_name), + ) + if desired is None: + continue + if member.get(attr_name) != desired: + member[attr_name] = desired + parental_status_updates += 1 + if marital_attr: marital_spec = attr_map[marital_attr] - for member in members: + for member in adult_members: has_partner = bool( member.get("partner_id") or member.get("partner_npc") ) @@ -1466,6 +1792,7 @@ def _reconcile_household_coherence( marital_roles=( semantic_roles.marital_roles if semantic_roles else None ), + current_value=member.get(marital_attr), ) if desired is None: continue @@ -1480,6 +1807,8 @@ def _reconcile_household_coherence( reconciliation["household_size_updates"] = household_size_updates if surname_updates: reconciliation["household_surname_updates"] = surname_updates + if parental_status_updates: + reconciliation["parental_status_updates"] = parental_status_updates if reconciliation: stats.reconciliation_counts.update(reconciliation) diff --git a/extropy/population/sampler/households.py b/extropy/population/sampler/households.py index 19f7f9d..f18986d 100644 --- a/extropy/population/sampler/households.py +++ b/extropy/population/sampler/households.py @@ -295,9 +295,14 @@ def generate_dependents( elderly_count = 0 if household_type == HouseholdType.MULTI_GENERATIONAL and num_dependents > 0: elderly_count = 1 - elderly_age = primary_age + rng.randint( - config.elderly_min_offset, config.elderly_max_offset + elderly_min_age = primary_age + config.elderly_min_offset + elderly_max_age = min( + primary_age + config.elderly_max_offset, + config.max_elderly_dependent_age, ) + if elderly_max_age < elderly_min_age: + elderly_min_age = elderly_max_age + elderly_age = rng.randint(elderly_min_age, elderly_max_age) elderly_gender = rng.choice(["male", "female"]) relationship = "father" if elderly_gender == "male" else "mother" dep_first, _ = generate_name( diff --git a/tests/test_household_sampling.py b/tests/test_household_sampling.py index 4b89190..4a5183e 100644 --- a/tests/test_household_sampling.py +++ b/tests/test_household_sampling.py @@ -9,6 +9,7 @@ GroundingInfo, NormalDistribution, CategoricalDistribution, + BooleanDistribution, HouseholdType, HouseholdConfig, Dependent, @@ -317,6 +318,25 @@ def test_generate_dependents_multi_generational(self): relationships = [d.relationship for d in deps] assert any(r in ("father", "mother") for r in relationships) + def test_generate_dependents_caps_elderly_age(self): + rng = random.Random(42) + config = HouseholdConfig( + elderly_min_offset=25, + elderly_max_offset=35, + max_elderly_dependent_age=100, + ) + deps = generate_dependents( + HouseholdType.MULTI_GENERATIONAL, + 4, + 2, + 80, + rng, + config, + ) + elder_ages = [d.age for d in deps if d.relationship in ("father", "mother")] + assert elder_ages + assert max(elder_ages) <= 100 + def test_estimate_household_count(self): assert estimate_household_count(100, _DEFAULT_CONFIG) == 40 assert estimate_household_count(1, _DEFAULT_CONFIG) == 1 @@ -485,3 +505,199 @@ def test_country_correlation(self): rate = same_country / total # Should be close to 0.95 (within statistical margin) assert 0.90 < rate < 0.99, f"Same-country rate {rate:.2%} out of expected range" + + +class TestHouseholdCoherenceNormalization: + def test_non_partnered_marital_categories_preserved(self): + spec = _make_household_spec(size=500) + spec.attributes.append( + AttributeSpec( + name="marital_status", + type="categorical", + category="universal", + description="Marital status", + scope="individual", + sampling=SamplingConfig( + strategy="independent", + distribution=CategoricalDistribution( + type="categorical", + options=[ + "Married/partnered", + "Single", + "Divorced/separated", + "Widowed", + ], + weights=[0.25, 0.35, 0.25, 0.15], + ), + ), + grounding=GroundingInfo(level="medium", method="estimated"), + ) + ) + spec.sampling_order.append("marital_status") + + result = sample_population(spec, count=500, seed=42, agent_focus_mode="couples") + adults = [ + a + for a in result.agents + if str(a.get("household_role", "")).startswith("adult_") + ] + non_partnered = [ + a + for a in adults + if not (a.get("partner_id") or a.get("partner_npc")) + ] + assert non_partnered + assert all(a.get("marital_status") != "Married/partnered" for a in non_partnered) + preserved = sum( + 1 + for a in non_partnered + if a.get("marital_status") in {"Divorced/separated", "Widowed"} + ) + assert preserved > 0 + + def test_has_children_matches_realized_dependents_for_adults(self): + spec = _make_household_spec(size=400) + spec.attributes.append( + AttributeSpec( + name="has_children", + type="boolean", + category="universal", + description="Children in household", + scope="individual", + identity_type="parental_status", + sampling=SamplingConfig( + strategy="independent", + distribution=BooleanDistribution(type="boolean", probability_true=0.5), + ), + grounding=GroundingInfo(level="medium", method="estimated"), + ) + ) + spec.attributes.append( + AttributeSpec( + name="marital_status", + type="categorical", + category="universal", + description="Marital status", + scope="individual", + sampling=SamplingConfig( + strategy="independent", + distribution=CategoricalDistribution( + type="categorical", + options=[ + "Married/partnered", + "Single", + "Divorced/separated", + "Widowed", + ], + weights=[0.25, 0.45, 0.2, 0.1], + ), + ), + grounding=GroundingInfo(level="medium", method="estimated"), + ) + ) + spec.sampling_order.extend(["has_children", "marital_status"]) + + result = sample_population(spec, count=400, seed=42, agent_focus_mode="couples") + adults = [ + a + for a in result.agents + if str(a.get("household_role", "")).startswith("adult_") + ] + assert adults + for agent in adults: + dependents = agent.get("dependents", []) or [] + has_child_dep = any( + isinstance(dep, dict) + and ( + str(dep.get("relationship", "")).lower() + in {"son", "daughter", "child", "stepchild"} + or ( + isinstance(dep.get("age"), (int, float)) + and 0 <= int(dep["age"]) <= 17 + ) + ) + for dep in dependents + ) + assert bool(agent.get("has_children")) == has_child_dep + + def test_young_adult_retired_values_are_normalized(self): + spec = _make_individual_spec(size=300) + spec.attributes.append( + AttributeSpec( + name="employment_sector", + type="categorical", + category="universal", + description="Employment sector", + semantic_type="employment", + sampling=SamplingConfig( + strategy="independent", + distribution=CategoricalDistribution( + type="categorical", + options=["Retired", "Private sector", "Unemployed"], + weights=[0.8, 0.1, 0.1], + ), + ), + grounding=GroundingInfo(level="low", method="estimated"), + ) + ) + spec.sampling_order.append("employment_sector") + for attr in spec.attributes: + if attr.name == "age": + attr.sampling.distribution = NormalDistribution( + type="normal", + mean=24, + std=2, + min=18, + max=29, + ) + + result = sample_population(spec, count=300, seed=42) + assert all(a["age"] < 30 for a in result.agents) + assert all(a.get("employment_sector") != "Retired" for a in result.agents) + + def test_education_normalization_for_young_adults(self): + spec = _make_individual_spec(size=400) + spec.attributes.append( + AttributeSpec( + name="education_level", + type="categorical", + category="universal", + description="Education level", + semantic_type="education", + sampling=SamplingConfig( + strategy="independent", + distribution=CategoricalDistribution( + type="categorical", + options=[ + "HS diploma/GED", + "Some college", + "Bachelor's degree", + "Graduate Degree", + ], + weights=[0.05, 0.05, 0.2, 0.7], + ), + ), + grounding=GroundingInfo(level="low", method="estimated"), + ) + ) + spec.sampling_order.append("education_level") + for attr in spec.attributes: + if attr.name == "age": + attr.sampling.distribution = NormalDistribution( + type="normal", + mean=19.5, + std=1.2, + min=18, + max=21, + ) + + result = sample_population(spec, count=400, seed=42) + assert all(a["age"] < 22 for a in result.agents) + assert all(a.get("education_level") != "Graduate Degree" for a in result.agents) + assert all( + not ( + a["age"] in (18, 19) + and a.get("education_level") in {"Bachelor's degree", "Graduate Degree"} + ) + for a in result.agents + ) From 116649fed40d315909ab6a7fd2f5819871de2907 Mon Sep 17 00:00:00 2001 From: RandomOscillations Date: Sun, 22 Feb 2026 00:56:34 -0500 Subject: [PATCH 08/20] Update study-02 sampled DB after coherence hardening --- examples/study-02/study.db | Bin 23523328 -> 23523328 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/examples/study-02/study.db b/examples/study-02/study.db index 89bfeca9245b3dfeb833a4264e0dcbb38a7ab72e..b40ea27274900cffbd8f4cec7ae6016a4e713ac1 100644 GIT binary patch delta 1423562 zcmc${2Ut{B`ajNrnYjZ@0b%GcgB1%fy`Yq#S}FDlOca%%TA2G`+#Q>;?(^NXnV^9x_@=jWH*oMe*aWp8)#9l)g22yJ}@a@0x>#lRzt-s 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zr&t2~Xie79CjV$24f_&Xgdj8S72yzEsmTEp3)?fE%$sbaNiXXambHojs+Bj8Ul~&N z8+Zd16b28q$OAgwD8wu`&2&;D63hsen9xxzM2MCBfP4zpBNl;@d+NaCCTenZU=kPH z#Lmc{fmTVUkSa%yJ&O;|`b+Y7N6?*=8n3y4m5r3F9Jsub4U+P798#p>hAQa`{N|K$ zj4NB(|<|nv!dBPPiA=VR9<@VT&nY%vGqiN?EcEJ(Ezv1|tJ9vlom$|-Z z?C1zw`hot@ewy8Ihqfs{)ETO(uc)i9s9cXm5Y@Heg=_KulJjc0<`=X&nz+9KRVB}O z14xzsg#Z76Y9<=A?zs<~w5o_!x*3eL5Gf%m+2?nH(5nlC?cxc@VV-{ND? zuVbFOKwr7cTSR53^_R~DAF0qTx&9yD9BD8vXl{78h4K8H7hhibMCeOiz2e--rE|X@ zZP0o$o@jb_@!a?OTC^+L+bJxt?)T2Q>SDK+PU)-C= zWe5%`qbx%Wh&yT));F2_yMCqN7nzyHA1iUsW@>+SDX1IJvrOX|-Yt;+O!gfi!I4y+ z2noRK4bzG<_WXGP=)-kNz>S}%XHN_=7v-GPrK?w&0>b4M(>2o79ju?hJRUlf6{RNOVymB;;u+LB4U@-61;pNtMRrnr z99#lpoxu~Avk$`MsVB{E5gl_{vC)ZeACQbnzxTw|`**}^E4*wg$D@=Bmq z*02P+a{PM8+c{C^4R@+Af9m pZ2NZR2C?FDp|gShF6fXdLs7*>n?7Liqu8B4dOoIo>wK>k`ahnP=>`A* From 90a3b6c71faa9df2509bb2d4ae731dc646282f10 Mon Sep 17 00:00:00 2001 From: RandomOscillations Date: Sun, 22 Feb 2026 17:13:58 -0500 Subject: [PATCH 09/20] Harden network gating and deterministic structural role handling --- extropy/cli/commands/network.py | 5 +- extropy/core/models/network.py | 41 +- .../population/network/config_generator.py | 270 ++++++++++- extropy/population/network/generator.py | 435 ++++++++++++++---- tests/test_network.py | 250 +++++++++- tests/test_network_config_generator.py | 140 +++++- 6 files changed, 1010 insertions(+), 131 deletions(-) diff --git a/extropy/cli/commands/network.py b/extropy/cli/commands/network.py index 379d984..7110a68 100644 --- a/extropy/cli/commands/network.py +++ b/extropy/cli/commands/network.py @@ -603,7 +603,10 @@ def do_generation(): console.print(f" Largest Component: {metrics.largest_component_ratio:.1%}") console.print(f" Degree Assortativity: {metrics.degree_assortativity:.3f}") - is_valid, warnings = metrics.is_valid() + quality_bounds = (result.meta.get("quality", {}) or {}).get("bounds", {}) + is_valid, warnings = metrics.is_valid( + bounds=quality_bounds if quality_bounds else None + ) console.print() if is_valid: console.print("[green]✓[/green] All metrics within expected ranges") diff --git a/extropy/core/models/network.py b/extropy/core/models/network.py index 9574015..1e72b22 100644 --- a/extropy/core/models/network.py +++ b/extropy/core/models/network.py @@ -97,7 +97,9 @@ class NetworkMetrics(BaseModel): degree_assortativity: float degree_distribution: dict[int, int] = Field(default_factory=dict) - def is_valid(self) -> tuple[bool, list[str]]: + def is_valid( + self, bounds: dict[str, float] | None = None + ) -> tuple[bool, list[str]]: """Check if metrics are within expected ranges. Returns: @@ -105,6 +107,43 @@ def is_valid(self) -> tuple[bool, list[str]]: """ warnings = [] + if bounds: + degree_min = bounds.get("degree_min") + degree_max = bounds.get("degree_max") + clustering_min = bounds.get("clustering_min") + modularity_min = bounds.get("modularity_min") + modularity_max = bounds.get("modularity_max") + lcc_min = bounds.get("largest_component_min") + + if degree_min is not None and self.avg_degree < degree_min: + warnings.append( + f"avg_degree {self.avg_degree:.1f} below target minimum ({degree_min:.2f})" + ) + if degree_max is not None and self.avg_degree > degree_max: + warnings.append( + f"avg_degree {self.avg_degree:.1f} above target maximum ({degree_max:.2f})" + ) + if ( + clustering_min is not None + and self.clustering_coefficient < clustering_min + ): + warnings.append( + f"clustering_coefficient {self.clustering_coefficient:.2f} below target minimum ({clustering_min:.2f})" + ) + if modularity_min is not None and self.modularity < modularity_min: + warnings.append( + f"modularity {self.modularity:.2f} below target minimum ({modularity_min:.2f})" + ) + if modularity_max is not None and self.modularity > modularity_max: + warnings.append( + f"modularity {self.modularity:.2f} above target maximum ({modularity_max:.2f})" + ) + if lcc_min is not None and self.largest_component_ratio < lcc_min: + warnings.append( + f"largest_component_ratio {self.largest_component_ratio:.2f} below target minimum ({lcc_min:.2f})" + ) + return len(warnings) == 0, warnings + if self.avg_degree < 15: warnings.append( f"avg_degree {self.avg_degree:.1f} below expected range (15-25)" diff --git a/extropy/population/network/config_generator.py b/extropy/population/network/config_generator.py index c4475d2..24c71e2 100644 --- a/extropy/population/network/config_generator.py +++ b/extropy/population/network/config_generator.py @@ -330,17 +330,209 @@ def _build_prompt( """ -def _build_structural_role_prompt(population_spec: PopulationSpec) -> str: - """Build minimal prompt for structural-role mapping only.""" - max_prompt_attrs = 50 - attribute_names = [ - attr.name for attr in population_spec.attributes[:max_prompt_attrs] +def _extract_sample_value( + agents_sample: list[dict] | None, attr_name: str +) -> object | None: + """Return a representative non-null sample value for an attribute.""" + if not agents_sample: + return None + for agent in agents_sample: + value = agent.get(attr_name) + if value is None: + continue + if isinstance(value, str) and not value.strip(): + continue + return value + return None + + +def _score_structural_role_candidate( + *, + role_name: str, + attr, + sample_value: object | None, +) -> float: + """Deterministic score for assigning an attribute to a structural role.""" + name = attr.name.lower() + desc = (attr.description or "").lower() + semantic = (attr.semantic_type or "").lower() + identity = (attr.identity_type or "").lower() + scope = (attr.scope or "").lower() + attr_type = (attr.type or "").lower() + text = f"{name} {desc}" + + def has_any(*tokens: str) -> bool: + return any(token in text for token in tokens) + + score = 0.0 + if role_name == "household_id": + if "household" in name: + score += 3.0 + if name.endswith("_id"): + score += 1.5 + if scope == "household": + score += 1.5 + if has_any("household", "co-resid", "home"): + score += 1.0 + if attr_type == "boolean": + score -= 2.0 + + elif role_name == "partner_id": + if has_any("partner", "spouse", "husband", "wife"): + score += 3.0 + if name.endswith("_id"): + score += 2.0 + if has_any("agent id", "spouse id", "partner id"): + score += 1.5 + if attr_type == "boolean": + score -= 2.0 + if isinstance(sample_value, str) and ( + sample_value.startswith("agent_") or sample_value.startswith("a") + ): + score += 1.0 + + elif role_name == "age": + if name == "age": + score += 6.0 + if semantic == "age": + score += 3.0 + if has_any("age", "years old", "years_old", "current age"): + score += 2.0 + if attr_type in {"int", "float"}: + score += 1.0 + + elif role_name == "sector": + if semantic in {"employment", "occupation"}: + score += 3.0 + if has_any("employment", "sector", "occupation", "industry", "profession"): + score += 2.5 + if identity == "professional_identity": + score += 1.5 + if attr_type == "boolean": + score -= 1.5 + + elif role_name == "region": + if has_any("region", "state", "province", "county", "city", "country"): + score += 3.0 + if has_any("geography", "location", "district", "zip", "postal"): + score += 1.5 + if scope == "household": + score += 0.5 + + elif role_name == "urbanicity": + if has_any("urban", "rural", "suburban", "metro", "urbanicity"): + score += 4.0 + if has_any("population density", "city type"): + score += 1.0 + + elif role_name == "religion": + if identity == "religious_affiliation": + score += 3.0 + if has_any("relig", "faith", "church", "denomination", "congregation"): + score += 2.5 + if semantic == "occupation": + score -= 1.0 + + elif role_name == "dependents": + if has_any("dependents", "children", "child", "household_members"): + score += 3.0 + if has_any("has_children", "child_present"): + score += 0.5 + if isinstance(sample_value, list): + score += 4.0 + elif isinstance(sample_value, bool): + # Boolean parental flags are not structural dependent records. + score -= 4.0 + if attr_type == "boolean": + score -= 2.0 + + return score + + +def _resolve_structural_roles_deterministic( + population_spec: PopulationSpec, + agents_sample: list[dict] | None = None, +) -> tuple[dict[str, str | None], dict[str, list[str]]]: + """Resolve role mapping deterministically; return ambiguous slots for tie-break.""" + roles = [ + "household_id", + "partner_id", + "age", + "sector", + "region", + "urbanicity", + "religion", + "dependents", ] + resolved: dict[str, str | None] = {role: None for role in roles} + ambiguous: dict[str, list[str]] = {} + + for role in roles: + scored: list[tuple[float, str]] = [] + for attr in population_spec.attributes: + sample_value = _extract_sample_value(agents_sample, attr.name) + score = _score_structural_role_candidate( + role_name=role, + attr=attr, + sample_value=sample_value, + ) + if score <= 0: + continue + scored.append((score, attr.name)) + + if not scored: + resolved[role] = None + continue + + scored.sort(key=lambda item: (-item[0], item[1])) + best_score, best_attr = scored[0] + second_score = scored[1][0] if len(scored) > 1 else -999.0 + margin = best_score - second_score + + # Confident deterministic pick. + if best_score >= 3.5 and margin >= 0.8: + resolved[role] = best_attr + continue + + # Ambiguous: keep deterministic suggestion but ask LLM to tie-break. + resolved[role] = best_attr + ambiguous[role] = [name for _, name in scored[:3]] + + return resolved, ambiguous + + +def _build_structural_role_prompt( + population_spec: PopulationSpec, + *, + locked_roles: dict[str, str | None] | None = None, + ambiguous_candidates: dict[str, list[str]] | None = None, +) -> str: + """Build structural-role prompt with full-schema visibility.""" + attribute_names = [attr.name for attr in population_spec.attributes] attr_lines = "\n".join(f"- {name}" for name in attribute_names) - omitted_note = "" - if len(population_spec.attributes) > max_prompt_attrs: - omitted = len(population_spec.attributes) - max_prompt_attrs - omitted_note = f"\nNote: {omitted} additional attributes are omitted for prompt compactness." + + locked_lines = "" + if locked_roles: + fixed = [ + f"- {role}: {value}" + for role, value in locked_roles.items() + if value is not None and role not in (ambiguous_candidates or {}) + ] + if fixed: + locked_lines = "\nLocked role assignments (do not change):\n" + "\n".join( + fixed + ) + + ambiguous_lines = "" + if ambiguous_candidates: + parts = [] + for role, candidates in ambiguous_candidates.items(): + options = ", ".join(candidates) if candidates else "null" + parts.append(f"- {role}: choose one of [{options}] or null") + ambiguous_lines = ( + "\nAmbiguous roles to decide (only choose from listed options):\n" + + "\n".join(parts) + ) return f"""Map structural network roles to exact attribute names from this population. @@ -349,7 +541,8 @@ def _build_structural_role_prompt(population_spec: PopulationSpec) -> str: Available attribute names: {attr_lines} -{omitted_note} +{locked_lines} +{ambiguous_lines} Roles to map (return exact attribute name or null): - household_id: household grouping identifier @@ -365,15 +558,41 @@ def _build_structural_role_prompt(population_spec: PopulationSpec) -> str: - NEVER invent names; choose only from Available attribute names. - If role is not represented, return null. - Keep this mapping pragmatic for structural edges: partner, household, coworker, neighbor, congregation, school_parent. +- For roles listed as ambiguous above, choose only from those candidate options or null. +- Keep locked roles unchanged. """ def _generate_structural_attribute_roles( population_spec: PopulationSpec, model: str | None = None, + agents_sample: list[dict] | None = None, ) -> StructuralAttributeRoles: - """Generate structural-role mapping in a focused low-load call.""" - prompt = _build_structural_role_prompt(population_spec) + """Resolve structural-role mapping deterministically, with LLM tie-break fallback.""" + role_names = ( + "household_id", + "partner_id", + "age", + "sector", + "region", + "urbanicity", + "religion", + "dependents", + ) + deterministic_roles, ambiguous = _resolve_structural_roles_deterministic( + population_spec=population_spec, + agents_sample=agents_sample, + ) + + # If deterministic resolution is confident, skip LLM entirely. + if not ambiguous: + return StructuralAttributeRoles.model_validate(deterministic_roles) + + prompt = _build_structural_role_prompt( + population_spec, + locked_roles=deterministic_roles, + ambiguous_candidates=ambiguous, + ) data = reasoning_call( prompt=prompt, response_schema=STRUCTURAL_ROLE_SCHEMA, @@ -385,21 +604,21 @@ def _generate_structural_attribute_roles( known_attrs = {attr.name for attr in population_spec.attributes} roles: dict[str, str | None] = {} - for role_name in ( - "household_id", - "partner_id", - "age", - "sector", - "region", - "urbanicity", - "religion", - "dependents", - ): + for role_name in role_names: + locked = deterministic_roles.get(role_name) value = data.get(role_name) - if isinstance(value, str) and value in known_attrs: - roles[role_name] = value + if role_name in ambiguous: + allowed = set(ambiguous[role_name]) + if ( + isinstance(value, str) + and value in known_attrs + and (not allowed or value in allowed) + ): + roles[role_name] = value + else: + roles[role_name] = locked else: - roles[role_name] = None + roles[role_name] = locked return StructuralAttributeRoles.model_validate(roles) @@ -662,6 +881,7 @@ def generate_network_config( "structural_attribute_roles": _generate_structural_attribute_roles( population_spec, model=model, + agents_sample=agents_sample, ) } ) diff --git a/extropy/population/network/generator.py b/extropy/population/network/generator.py index 3530c73..6ceddab 100644 --- a/extropy/population/network/generator.py +++ b/extropy/population/network/generator.py @@ -28,6 +28,14 @@ logger = logging.getLogger(__name__) _STATUS_HEARTBEAT_SECONDS = 5.0 +_GATE_NUMERIC_EPSILON = 1e-4 + +try: + import networkx as nx + + HAS_NETWORKX = True +except ImportError: + HAS_NETWORKX = False _SIM_WORKER_AGENTS: list[dict[str, Any]] | None = None _SIM_WORKER_ATTRIBUTE_WEIGHTS = None @@ -1058,13 +1066,15 @@ def _evaluate_topology_gate( mod_max: float, lcc_min: float, min_edge_floor: int, + epsilon: float = _GATE_NUMERIC_EPSILON, ) -> tuple[bool, dict[str, float]]: """Evaluate strict topology gate and return deltas vs configured bounds.""" + eps = max(0.0, float(epsilon)) in_range = ( - deg_min <= metrics["avg_degree"] <= deg_max - and metrics["clustering"] >= cluster_min - and mod_min <= metrics["modularity"] <= mod_max - and metrics["largest_component_ratio"] >= lcc_min + (deg_min - eps) <= metrics["avg_degree"] <= (deg_max + eps) + and metrics["clustering"] >= (cluster_min - eps) + and (mod_min - eps) <= metrics["modularity"] <= (mod_max + eps) + and metrics["largest_component_ratio"] >= (lcc_min - eps) and metrics["edge_count"] >= min_edge_floor ) deltas = { @@ -1079,6 +1089,81 @@ def _evaluate_topology_gate( return in_range, deltas +def _similarity_reserve_ratio(config: NetworkConfig) -> float: + """Reserve a minimum similarity budget so structural channels do not dominate.""" + if config.quality_profile == "strict": + return 0.45 + if config.quality_profile == "fast": + return 0.25 + return 0.35 + + +def _compute_modularity_louvain( + edges: list[Edge], agent_ids: list[str] +) -> float | None: + """Compute modularity via Louvain on the final graph (same method as metrics.py).""" + if not HAS_NETWORKX: + return None + try: + graph = nx.Graph() + graph.add_nodes_from(agent_ids) + for edge in edges: + graph.add_edge(edge.source, edge.target) + communities = nx.community.louvain_communities(graph, seed=42) + return float(nx.community.modularity(graph, communities)) + except Exception: + return None + + +def _merge_edges_with_structural( + similarity_edges: list[Edge], + structural_edges: list[Edge], +) -> tuple[list[Edge], set[tuple[str, str]], int]: + """Merge structural edges into similarity edges, deduplicating by undirected pair.""" + merged = list(similarity_edges) + edge_set: set[tuple[str, str]] = set() + for edge in merged: + edge_set.add((edge.source, edge.target)) + edge_set.add((edge.target, edge.source)) + + added = 0 + for structural in structural_edges: + if (structural.source, structural.target) in edge_set: + continue + merged.append(structural) + edge_set.add((structural.source, structural.target)) + edge_set.add((structural.target, structural.source)) + added += 1 + + return merged, edge_set, added + + +def _compute_gate_metrics( + *, + edges: list[Edge], + agent_ids: list[str], + communities: list[int] | None, + fallback_modularity: float = 0.0, +) -> dict[str, float]: + """Compute final gate metrics on the realized graph.""" + n = len(agent_ids) + adjacency = _build_adjacency_from_edges(edges, agent_ids, n) + modularity = _compute_modularity_louvain(edges, agent_ids) + if modularity is None: + if communities and len(communities) == n: + modularity = _compute_modularity_fast(edges, agent_ids, communities) + else: + modularity = fallback_modularity + + return { + "edge_count": len(edges), + "avg_degree": (2 * len(edges) / n) if n > 0 else 0.0, + "clustering": _compute_avg_clustering(adjacency, n), + "largest_component_ratio": _compute_largest_component_ratio(adjacency, n), + "modularity": modularity, + } + + def _apply_scale_target_caps( config: NetworkConfig, n: int, @@ -1872,6 +1957,10 @@ def _resolve_structural_keys_from_config( for slot, fallback_keys in defaults.items(): configured = getattr(roles, slot, None) + if configured and populated_counts.get(configured, 0) > 0: + # Config-selected role must win when populated; fallbacks are only backup. + resolved[slot] = configured + continue candidates: list[str] = [] if configured: candidates.append(configured) @@ -1946,6 +2035,7 @@ def _generate_structural_edges( agent_ids: list[str], rng: random.Random, config: NetworkConfig | None = None, + target_edge_budget: int | None = None, ) -> list[Edge]: """Generate deterministic structural edges from agent attributes. @@ -1956,10 +2046,10 @@ def _generate_structural_edges( edges: list[Edge] = [] added: set[tuple[int, int]] = set() - def _add(i: int, j: int, etype: str, weight: float, context: str) -> None: + def _add(i: int, j: int, etype: str, weight: float, context: str) -> bool: pair = (min(i, j), max(i, j)) if pair in added or i == j: - return + return False added.add(pair) edges.append( Edge( @@ -1971,6 +2061,7 @@ def _add(i: int, j: int, etype: str, weight: float, context: str) -> None: context=context, ) ) + return True effective_config = config or NetworkConfig() resolved_keys = _resolve_structural_keys_from_config(agents, effective_config) @@ -2026,48 +2117,111 @@ def _add(i: int, j: int, etype: str, weight: float, context: str) -> None: if has_school_kid and region: school_parent_map.setdefault((region, urban_bucket), []).append(idx) - # 1. Partner edges (weight 1.0, max 1 per agent) + # 1) Mandatory structural edges: partner + household. + # These are preserved even when they exceed target edge budgets. for idx, agent in enumerate(agents): partner_id = agent.get(partner_key) if partner_key else None if partner_id and partner_id in id_to_idx: j = id_to_idx[partner_id] _add(idx, j, "partner", 1.0, "household") - # 2. Household edges (weight 0.9, all members in same household) + # 2) Household edges (co-residence ties). for members in household_map.values(): for i_pos in range(len(members)): for j_pos in range(i_pos + 1, len(members)): _add(members[i_pos], members[j_pos], "household", 0.9, "household") - # 3. Coworker edges (weight 0.6, capped at ~8 per agent) + # Optional structural budget: keep headroom for similarity-based structure. + optional_budget: int | None = None + if target_edge_budget is not None: + optional_budget = max(0, int(target_edge_budget) - len(edges)) + + def _channel_pressure( + pools: dict[tuple[str, str], list[int]], weight: float + ) -> float: + opportunity = sum(max(0, len(pool) - 1) for pool in pools.values()) + if opportunity <= 0: + return 0.0 + # Square-root dampening avoids one giant channel monopolizing budget. + return weight * (opportunity**0.5) + + channel_order = ["coworker", "neighbor", "congregation", "school_parent"] + channel_scores = { + "coworker": _channel_pressure(sector_region_map, 1.00), + "neighbor": _channel_pressure(region_urban_map, 0.80), + "congregation": _channel_pressure(religion_region_map, 0.65), + "school_parent": _channel_pressure(school_parent_map, 0.60), + } + channel_quotas: dict[str, int] | None = None + if optional_budget is not None: + channel_quotas = {name: 0 for name in channel_order} + total_score = sum(channel_scores.values()) + if optional_budget > 0 and total_score > 0: + fractional: list[tuple[float, str]] = [] + allocated = 0 + for name in channel_order: + raw = optional_budget * (channel_scores[name] / total_score) + whole = int(raw) + channel_quotas[name] = whole + allocated += whole + fractional.append((raw - whole, name)) + remainder = max(0, optional_budget - allocated) + fractional.sort(key=lambda item: (-item[0], item[1])) + for _, name in fractional: + if remainder <= 0: + break + channel_quotas[name] += 1 + remainder -= 1 + + def _quota_for(channel: str) -> int | None: + if channel_quotas is None: + return None + return channel_quotas.get(channel, 0) + + # 3) Coworker edges (weight 0.6, locally capped). _MAX_COWORKER = 8 coworker_count: dict[int, int] = {} + coworker_added = 0 + coworker_quota = _quota_for("coworker") for pool in sector_region_map.values(): + if coworker_quota is not None and coworker_added >= coworker_quota: + break if len(pool) < 2: continue shuffled = list(pool) rng.shuffle(shuffled) for i_pos, i in enumerate(shuffled): + if coworker_quota is not None and coworker_added >= coworker_quota: + break if coworker_count.get(i, 0) >= _MAX_COWORKER: continue for j in shuffled[i_pos + 1 :]: + if coworker_quota is not None and coworker_added >= coworker_quota: + break if coworker_count.get(j, 0) >= _MAX_COWORKER: continue if coworker_count.get(i, 0) >= _MAX_COWORKER: break - _add(i, j, "coworker", 0.6, "workplace") - coworker_count[i] = coworker_count.get(i, 0) + 1 - coworker_count[j] = coworker_count.get(j, 0) + 1 + if _add(i, j, "coworker", 0.6, "workplace"): + coworker_count[i] = coworker_count.get(i, 0) + 1 + coworker_count[j] = coworker_count.get(j, 0) + 1 + coworker_added += 1 - # 4. Neighbor edges (weight 0.4, capped at ~4, age within 15yr) + # 4) Neighbor edges (weight 0.4, locally capped, age-bounded). _MAX_NEIGHBOR = 4 neighbor_count: dict[int, int] = {} + neighbor_added = 0 + neighbor_quota = _quota_for("neighbor") for pool in region_urban_map.values(): + if neighbor_quota is not None and neighbor_added >= neighbor_quota: + break if len(pool) < 2: continue shuffled = list(pool) rng.shuffle(shuffled) for i_pos, i in enumerate(shuffled): + if neighbor_quota is not None and neighbor_added >= neighbor_quota: + break if neighbor_count.get(i, 0) >= _MAX_NEIGHBOR: continue age_i_raw = agents[i].get(age_key) if age_key else None @@ -2075,6 +2229,8 @@ def _add(i: int, j: int, etype: str, weight: float, context: str) -> None: age_i_raw = int(age_i_raw.strip()) age_i = age_i_raw if isinstance(age_i_raw, (int, float)) else 40 for j in shuffled[i_pos + 1 :]: + if neighbor_quota is not None and neighbor_added >= neighbor_quota: + break if neighbor_count.get(j, 0) >= _MAX_NEIGHBOR: continue if neighbor_count.get(i, 0) >= _MAX_NEIGHBOR: @@ -2084,49 +2240,74 @@ def _add(i: int, j: int, etype: str, weight: float, context: str) -> None: age_j_raw = int(age_j_raw.strip()) age_j = age_j_raw if isinstance(age_j_raw, (int, float)) else 40 if abs(age_i - age_j) <= 15: - _add(i, j, "neighbor", 0.4, "neighborhood") - neighbor_count[i] = neighbor_count.get(i, 0) + 1 - neighbor_count[j] = neighbor_count.get(j, 0) + 1 + if _add(i, j, "neighbor", 0.4, "neighborhood"): + neighbor_count[i] = neighbor_count.get(i, 0) + 1 + neighbor_count[j] = neighbor_count.get(j, 0) + 1 + neighbor_added += 1 - # 5. Congregation edges (weight 0.4, capped at ~4) + # 5) Congregation edges (weight 0.4, locally capped). _MAX_CONGREGATION = 4 congregation_count: dict[int, int] = {} + congregation_added = 0 + congregation_quota = _quota_for("congregation") for pool in religion_region_map.values(): + if congregation_quota is not None and congregation_added >= congregation_quota: + break if len(pool) < 2: continue shuffled = list(pool) rng.shuffle(shuffled) for i_pos, i in enumerate(shuffled): + if ( + congregation_quota is not None + and congregation_added >= congregation_quota + ): + break if congregation_count.get(i, 0) >= _MAX_CONGREGATION: continue for j in shuffled[i_pos + 1 :]: + if ( + congregation_quota is not None + and congregation_added >= congregation_quota + ): + break if congregation_count.get(j, 0) >= _MAX_CONGREGATION: continue if congregation_count.get(i, 0) >= _MAX_CONGREGATION: break - _add(i, j, "congregation", 0.4, "congregation") - congregation_count[i] = congregation_count.get(i, 0) + 1 - congregation_count[j] = congregation_count.get(j, 0) + 1 + if _add(i, j, "congregation", 0.4, "congregation"): + congregation_count[i] = congregation_count.get(i, 0) + 1 + congregation_count[j] = congregation_count.get(j, 0) + 1 + congregation_added += 1 - # 6. School parent edges (weight 0.35, capped at ~3) + # 6) School-parent edges (weight 0.35, locally capped). _MAX_SCHOOL_PARENT = 3 school_count: dict[int, int] = {} + school_added = 0 + school_quota = _quota_for("school_parent") for pool in school_parent_map.values(): + if school_quota is not None and school_added >= school_quota: + break if len(pool) < 2: continue shuffled = list(pool) rng.shuffle(shuffled) for i_pos, i in enumerate(shuffled): + if school_quota is not None and school_added >= school_quota: + break if school_count.get(i, 0) >= _MAX_SCHOOL_PARENT: continue for j in shuffled[i_pos + 1 :]: + if school_quota is not None and school_added >= school_quota: + break if school_count.get(j, 0) >= _MAX_SCHOOL_PARENT: continue if school_count.get(i, 0) >= _MAX_SCHOOL_PARENT: break - _add(i, j, "school_parent", 0.35, "school") - school_count[i] = school_count.get(i, 0) + 1 - school_count[j] = school_count.get(j, 0) + 1 + if _add(i, j, "school_parent", 0.35, "school"): + school_count[i] = school_count.get(i, 0) + 1 + school_count[j] = school_count.get(j, 0) + 1 + school_added += 1 return edges @@ -2325,10 +2506,17 @@ def _stage_plan() -> list[dict[str, Any]]: stage_plan = _stage_plan() elapsed_budget = max(1, int(config.max_calibration_minutes * 60)) stage_budget = max(30, elapsed_budget // max(1, len(stage_plan))) + target_edge_budget = max(1, int(round(n * config.avg_degree / 2.0))) + structural_edge_budget = max( + 1, + int(round(target_edge_budget * (1.0 - _similarity_reserve_ratio(config)))), + ) calibration_accepted = False best_score = float("inf") - best_edges: list[Edge] | None = None + best_similarity_edges: list[Edge] | None = None + best_structural_edges: list[Edge] | None = None + best_structural_pair_set: set[tuple[str, str]] | None = None calibration_best_metrics: dict[str, float] | None = None calibration_best_communities: list[int] | None = None best_stage = "" @@ -2344,10 +2532,6 @@ def _stage_plan() -> list[dict[str, Any]]: * config.max_calibration_iterations, ) - # Initialized before stage loop; populated in Step 3b inside the loop. - structural_edges: list[Edge] = [] - structural_pair_set: set[tuple[str, str]] = set() - for stage_idx, stage in enumerate(stage_plan): if time.time() - started_at >= elapsed_budget: break @@ -2579,41 +2763,47 @@ def similarity_progress(_stage: str, current: int, total: int) -> None: ) continue - # Step 3b: Generate structural edges first (structural-first ordering). - # Structural edges consume part of the degree budget; similarity - # calibration targets the remainder. + # Step 3b: Generate structural edges first with a global budget. structural_fn = _generate_structural_edges sig = inspect.signature(structural_fn) - if "config" in sig.parameters or len(sig.parameters) >= 4: - structural_edges = structural_fn( - agents, - agent_ids, - rng, - config=stage_cfg, + structural_kwargs: dict[str, Any] = {} + if "config" in sig.parameters: + structural_kwargs["config"] = stage_cfg + if "target_edge_budget" in sig.parameters: + structural_kwargs["target_edge_budget"] = structural_edge_budget + if structural_kwargs: + stage_structural_edges = structural_fn( + agents, agent_ids, rng, **structural_kwargs ) else: - structural_edges = structural_fn(agents, agent_ids, rng) - structural_pair_set: set[tuple[str, str]] = set() - for se in structural_edges: - structural_pair_set.add((se.source, se.target)) - structural_pair_set.add((se.target, se.source)) + stage_structural_edges = structural_fn(agents, agent_ids, rng) + stage_structural_pair_set: set[tuple[str, str]] = set() + for se in stage_structural_edges: + stage_structural_pair_set.add((se.source, se.target)) + stage_structural_pair_set.add((se.target, se.source)) structural_degree: Counter[str] = Counter() - for se in structural_edges: + for se in stage_structural_edges: structural_degree[se.source] += 1 structural_degree[se.target] += 1 avg_structural = ( - sum(structural_degree.values()) / max(n, 1) if structural_edges else 0.0 + sum(structural_degree.values()) / max(n, 1) + if stage_structural_edges + else 0.0 ) - # Reduce similarity target by average structural degree - similarity_target = max(1.0, config.avg_degree - avg_structural) + # Keep enough residual degree for similarity edges. + residual_target = max(1.0, config.avg_degree - avg_structural) + similarity_target = max( + 1.0, + min(config.avg_degree, residual_target), + ) stage_cfg = stage_cfg.model_copy(update={"avg_degree": similarity_target}) logger.info( "[NETWORK] Structural edges: %d (avg structural degree: %.2f, " "similarity target: %.2f)", - len(structural_edges), + len(stage_structural_edges), avg_structural, similarity_target, ) @@ -2658,20 +2848,28 @@ def similarity_progress(_stage: str, current: int, total: int) -> None: }, ) iter_rng = random.Random(restart_seed + iteration * 1000) - edges, avg_degree, clustering, modularity, largest_component_ratio = ( - _generate_network_single_pass( - agents, - agent_ids, - similarities, - communities, - degree_factors, - stage_cfg, - intra_scale, - inter_scale, - iter_rng, - structural_pairs=structural_pair_set, - ) + edges, _, _, _, _ = _generate_network_single_pass( + agents, + agent_ids, + similarities, + communities, + degree_factors, + stage_cfg, + intra_scale, + inter_scale, + iter_rng, + structural_pairs=stage_structural_pair_set, + ) + combined_edges, _, _ = _merge_edges_with_structural( + edges, stage_structural_edges + ) + adjacency = _build_adjacency_from_edges(combined_edges, agent_ids, n) + avg_degree = 2 * len(combined_edges) / n if n > 0 else 0.0 + clustering = _compute_avg_clustering(adjacency, n) + modularity = _compute_modularity_fast( + combined_edges, agent_ids, communities ) + largest_component_ratio = _compute_largest_component_ratio(adjacency, n) lcc_deficit = max( 0.0, config.target_largest_component_ratio - largest_component_ratio ) @@ -2698,17 +2896,23 @@ def similarity_progress(_stage: str, current: int, total: int) -> None: iter_rng, max_swaps_override=swap_budget, ) + combined_edges, _, _ = _merge_edges_with_structural( + edges, stage_structural_edges + ) emit_progress( "Repair pass", iteration + 1, config.max_calibration_iterations, message={"stage": stage_name, "restart": restart + 1}, ) - adjacency = _build_adjacency_from_edges(edges, agent_ids, n) + adjacency = _build_adjacency_from_edges(combined_edges, agent_ids, n) + avg_degree = 2 * len(combined_edges) / n if n > 0 else 0.0 clustering = _compute_avg_clustering(adjacency, n) - modularity = _compute_modularity_fast(edges, agent_ids, communities) + modularity = _compute_modularity_fast( + combined_edges, agent_ids, communities + ) largest_component_ratio = _compute_largest_component_ratio(adjacency, n) - edge_count = len(edges) + edge_count = len(combined_edges) score = 0.0 if avg_degree < deg_min: @@ -2754,7 +2958,9 @@ def similarity_progress(_stage: str, current: int, total: int) -> None: stage_best_metrics = metrics_payload if score < best_score: best_score = score - best_edges = edges + best_similarity_edges = list(edges) + best_structural_edges = list(stage_structural_edges) + best_structural_pair_set = set(stage_structural_pair_set) calibration_best_metrics = metrics_payload calibration_best_communities = list(communities) best_stage = stage_name @@ -2864,24 +3070,12 @@ def similarity_progress(_stage: str, current: int, total: int) -> None: emit_progress( "Calibrating network", calibration_total, calibration_total, force_db=True ) - edges = best_edges or [] - - # Rebuild edge_set from best edges - edge_set: set[tuple[str, str]] = set() - for edge in edges: - edge_set.add((edge.source, edge.target)) - edge_set.add((edge.target, edge.source)) - - # Step 4b: Merge pre-generated structural edges into similarity edges. - # Structural edges were generated before calibration (Step 3b) and the - # similarity budget was reduced accordingly. Now merge them in. - structural_added = 0 - for se in structural_edges: - if (se.source, se.target) not in edge_set: - edges.append(se) - edge_set.add((se.source, se.target)) - edge_set.add((se.target, se.source)) - structural_added += 1 + similarity_edges = best_similarity_edges or [] + chosen_structural_edges = best_structural_edges or [] + chosen_structural_pair_set = best_structural_pair_set or set() + edges, edge_set, structural_added = _merge_edges_with_structural( + similarity_edges, chosen_structural_edges + ) if structural_added > 0: emit_progress( @@ -2890,12 +3084,31 @@ def similarity_progress(_stage: str, current: int, total: int) -> None: structural_added, message={ "structural_added": structural_added, - "total_structural": len(structural_edges), + "total_structural": len(chosen_structural_edges), }, force_db=True, ) # Step 5: Watts-Strogatz rewiring (skip structural edges) + pre_rewire_edges = list(edges) + pre_rewire_edge_set = set(edge_set) + pre_rewire_metrics = _compute_gate_metrics( + edges=pre_rewire_edges, + agent_ids=agent_ids, + communities=calibration_best_communities, + fallback_modularity=(calibration_best_metrics or {}).get("modularity", 0.0), + ) + pre_rewire_accepted, pre_rewire_deltas = _evaluate_topology_gate( + metrics=pre_rewire_metrics, + deg_min=deg_min, + deg_max=deg_max, + cluster_min=cluster_min, + mod_min=mod_min, + mod_max=mod_max, + lcc_min=config.target_largest_component_ratio, + min_edge_floor=min_edge_floor, + ) + emit_progress("Rewiring edges", 0, len(edges), force_db=True) edges, edge_set, rewired_count = _apply_rewiring( agents, @@ -2904,27 +3117,19 @@ def similarity_progress(_stage: str, current: int, total: int) -> None: edge_set, config, rng, - protected_pairs=structural_pair_set, + protected_pairs=chosen_structural_pair_set, ) emit_progress("Rewiring edges", len(edges), len(edges), force_db=True) - final_adjacency = _build_adjacency_from_edges(edges, agent_ids, n) - final_metrics = { - "edge_count": len(edges), - "avg_degree": (2 * len(edges) / n) if n > 0 else 0.0, - "clustering": _compute_avg_clustering(final_adjacency, n), - "largest_component_ratio": _compute_largest_component_ratio(final_adjacency, n), - "modularity": 0.0, - } - if calibration_best_communities and len(calibration_best_communities) == n: - final_metrics["modularity"] = _compute_modularity_fast( - edges, agent_ids, calibration_best_communities - ) - elif calibration_best_metrics is not None: - final_metrics["modularity"] = calibration_best_metrics.get("modularity", 0.0) + post_rewire_metrics = _compute_gate_metrics( + edges=edges, + agent_ids=agent_ids, + communities=calibration_best_communities, + fallback_modularity=(calibration_best_metrics or {}).get("modularity", 0.0), + ) - final_accepted, quality_deltas = _evaluate_topology_gate( - metrics=final_metrics, + post_rewire_accepted, post_rewire_deltas = _evaluate_topology_gate( + metrics=post_rewire_metrics, deg_min=deg_min, deg_max=deg_max, cluster_min=cluster_min, @@ -2933,6 +3138,23 @@ def similarity_progress(_stage: str, current: int, total: int) -> None: lcc_min=config.target_largest_component_ratio, min_edge_floor=min_edge_floor, ) + rewire_gate_fallback_used = False + reverted_rewired_count = 0 + if pre_rewire_accepted and not post_rewire_accepted: + # Rewiring is an optimization pass. If it breaks a gate-valid graph, + # keep the calibrated pre-rewire network as the canonical result. + edges = pre_rewire_edges + edge_set = pre_rewire_edge_set + final_metrics = pre_rewire_metrics + final_accepted = True + quality_deltas = pre_rewire_deltas + reverted_rewired_count = rewired_count + rewired_count = 0 + rewire_gate_fallback_used = True + else: + final_metrics = post_rewire_metrics + final_accepted = post_rewire_accepted + quality_deltas = post_rewire_deltas calibration_quality_deltas = { "degree_to_min": (calibration_best_metrics or {}).get("avg_degree", 0.0) @@ -2970,6 +3192,8 @@ def similarity_progress(_stage: str, current: int, total: int) -> None: "target_clustering": config.target_clustering, "target_modularity": config.target_modularity, "quality_profile": config.quality_profile, + "target_edge_budget": target_edge_budget, + "structural_edge_budget": structural_edge_budget, }, "quality": { "topology_gate": config.topology_gate, @@ -2977,6 +3201,13 @@ def similarity_progress(_stage: str, current: int, total: int) -> None: "best_score": best_score, "best_metrics": calibration_best_metrics or {}, "final_metrics": final_metrics, + "pre_rewire_metrics": pre_rewire_metrics, + "post_rewire_metrics": post_rewire_metrics, + "pre_rewire_gate_accepted": pre_rewire_accepted, + "post_rewire_gate_accepted": post_rewire_accepted, + "rewire_gate_fallback_used": rewire_gate_fallback_used, + "gate_numeric_epsilon": _GATE_NUMERIC_EPSILON, + "rewired_count_reverted": reverted_rewired_count, "calibration_accepted": calibration_accepted, "best_stage": best_stage, "gate_deltas": quality_deltas, @@ -2993,7 +3224,7 @@ def similarity_progress(_stage: str, current: int, total: int) -> None: }, "ladder_stages": stage_summaries, }, - "structural_edge_count": len(structural_edges), + "structural_edge_count": len(chosen_structural_edges), "structural_edges_added": structural_added, "resume_calibration_requested": resume_calibration, "generated_at": datetime.now().isoformat(), diff --git a/tests/test_network.py b/tests/test_network.py index 78eb56d..df33da4 100644 --- a/tests/test_network.py +++ b/tests/test_network.py @@ -12,6 +12,7 @@ DegreeMultiplierConfig, EdgeTypeRule, InfluenceFactorConfig, + StructuralAttributeRoles, ) from extropy.population.network.similarity import ( compute_match_score, @@ -31,6 +32,9 @@ _coverage_needs_escalation, _compute_modularity_fast, _apply_scale_target_caps, + _generate_structural_edges, + _resolve_structural_keys_from_config, + _evaluate_topology_gate, ) _REFERENCE_SENIORITY_LEVELS = { @@ -896,12 +900,256 @@ def no_rewire(agents, agent_ids, edges, edge_set, config, rng, protected_pairs): result = generate_network(agents, config) quality = result.meta.get("quality", {}) - assert quality.get("calibration_accepted") is False + assert quality.get("calibration_accepted") is True assert quality.get("accepted") is True assert quality.get("final_metrics", {}).get("edge_count") == 15 assert quality.get("final_metrics", {}).get("largest_component_ratio") == 1.0 assert result.meta.get("edge_count") == 15 + def test_final_gate_modularity_matches_louvain_metric(self, monkeypatch): + """Final gate modularity should use the same method as reported network metrics.""" + import extropy.population.network.generator as gen + + agents = [{"_id": f"a{i}"} for i in range(6)] + + def fake_single_pass( + agents, + agent_ids, + similarities, + communities, + degree_factors, + config, + intra_scale, + inter_scale, + rng, + structural_pairs=None, + ): + return [], 0.0, 0.0, 0.01, 0.0 + + monkeypatch.setattr(gen, "_generate_network_single_pass", fake_single_pass) + monkeypatch.setattr(gen, "_compute_modularity_louvain", lambda edges, ids: 0.42) + monkeypatch.setattr( + gen, + "_assign_communities_with_diagnostics", + lambda agents, + similarities, + n_communities, + rng, + preferred_attrs=None, + progress=None: ( + [0 for _ in agents], + {"low_signal": 0.0}, + ), + ) + + config = NetworkConfig( + avg_degree=4.0, + seed=42, + candidate_mode="exact", + calibration_restarts=1, + max_calibration_iterations=1, + max_calibration_minutes=1, + topology_gate="warn", + ) + result = generate_network(agents, config) + final_metrics = result.meta.get("quality", {}).get("final_metrics", {}) + assert final_metrics.get("modularity") == pytest.approx(0.42) + + def test_rewiring_fallback_preserves_gate_valid_graph(self, monkeypatch): + """If rewiring breaks gate-valid metrics, final output should revert pre-rewire.""" + import extropy.population.network.generator as gen + + agents = [{"_id": f"a{i}"} for i in range(6)] + + def fake_single_pass( + agents, + agent_ids, + similarities, + communities, + degree_factors, + config, + intra_scale, + inter_scale, + rng, + structural_pairs=None, + ): + # Similarity edges intentionally empty; structural graph drives calibration. + return [], 0.0, 0.0, 0.0, 0.0 + + def full_structural_edges( + agents, agent_ids, rng, config=None, target_edge_budget=None + ): + edges = [] + for i in range(len(agent_ids)): + for j in range(i + 1, len(agent_ids)): + edges.append( + Edge( + source=agent_ids[i], + target=agent_ids[j], + weight=1.0, + edge_type="household", + structural=True, + context="household", + ) + ) + return edges + + def no_structural_rewire( + agents, agent_ids, edges, edge_set, config, rng, protected_pairs + ): + return edges, edge_set, 7 + + metric_calls = {"count": 0} + + def fake_gate_metrics(edges, agent_ids, communities, fallback_modularity=0.0): + metric_calls["count"] += 1 + # First call (pre-rewire): pass. Second call (post-rewire): fail on modularity. + if metric_calls["count"] == 1: + return { + "edge_count": len(edges), + "avg_degree": 5.0, + "clustering": 0.30, + "largest_component_ratio": 1.0, + "modularity": 0.49995, + } + return { + "edge_count": len(edges), + "avg_degree": 5.0, + "clustering": 0.30, + "largest_component_ratio": 1.0, + "modularity": 0.51000, + } + + monkeypatch.setattr(gen, "_generate_network_single_pass", fake_single_pass) + monkeypatch.setattr(gen, "_generate_structural_edges", full_structural_edges) + monkeypatch.setattr(gen, "_apply_rewiring", no_structural_rewire) + monkeypatch.setattr(gen, "_compute_gate_metrics", fake_gate_metrics) + monkeypatch.setattr( + gen, + "_assign_communities_with_diagnostics", + lambda agents, + similarities, + n_communities, + rng, + preferred_attrs=None, + progress=None: ([0 for _ in agents], {"low_signal": 0.0}), + ) + + config = NetworkConfig( + avg_degree=5.0, + seed=42, + candidate_mode="exact", + calibration_restarts=1, + max_calibration_iterations=1, + max_calibration_minutes=1, + topology_gate="strict", + target_clustering=0.22, + target_clustering_tolerance=0.08, + target_modularity=0.4, + target_modularity_tolerance=0.1, + target_largest_component_ratio=0.95, + ) + + result = generate_network(agents, config) + quality = result.meta.get("quality", {}) + + assert quality.get("accepted") is True + assert quality.get("pre_rewire_gate_accepted") is True + assert quality.get("post_rewire_gate_accepted") is False + assert quality.get("rewire_gate_fallback_used") is True + assert quality.get("rewired_count_reverted") == 7 + assert result.meta.get("rewired_count") == 0 + + def test_topology_gate_epsilon_allows_tiny_overflow_only(self): + """Gate epsilon should absorb tiny numeric drift but not real misses.""" + accepted_tiny, _ = _evaluate_topology_gate( + metrics={ + "edge_count": 100, + "avg_degree": 12.0, + "clustering": 0.20, + "modularity": 0.50005, + "largest_component_ratio": 1.0, + }, + deg_min=10.0, + deg_max=14.0, + cluster_min=0.14, + mod_min=0.30, + mod_max=0.50, + lcc_min=0.95, + min_edge_floor=50, + ) + rejected_real, _ = _evaluate_topology_gate( + metrics={ + "edge_count": 100, + "avg_degree": 12.0, + "clustering": 0.20, + "modularity": 0.50030, + "largest_component_ratio": 1.0, + }, + deg_min=10.0, + deg_max=14.0, + cluster_min=0.14, + mod_min=0.30, + mod_max=0.50, + lcc_min=0.95, + min_edge_floor=50, + ) + assert accepted_tiny is True + assert rejected_real is False + + def test_structural_edges_respect_global_budget(self): + """Optional structural channels should obey a global edge budget.""" + rng = random.Random(42) + agents = [] + for i in range(120): + agents.append( + { + "_id": f"a{i}", + "age": 30 + (i % 20), + "sector_field": f"sector_{i % 6}", + "region_field": f"region_{i % 4}", + "urban_field": "Urban" if i % 2 == 0 else "Suburban", + "religion_field": "FaithA" if i % 3 == 0 else "FaithB", + "dependents_field": [{"age": 10}] if i % 5 == 0 else [], + } + ) + agent_ids = [a["_id"] for a in agents] + cfg = NetworkConfig( + structural_attribute_roles=StructuralAttributeRoles( + age="age", + sector="sector_field", + region="region_field", + urbanicity="urban_field", + religion="religion_field", + dependents="dependents_field", + ) + ) + + edges = _generate_structural_edges( + agents=agents, + agent_ids=agent_ids, + rng=rng, + config=cfg, + target_edge_budget=180, + ) + assert len(edges) <= 180 + + def test_resolve_structural_keys_prefers_configured_populated_key(self): + """Configured structural role should win over fallback keys when populated.""" + agents = [ + {"_id": "a0", "custom_partner": "a1", "partner_id": "a1"}, + {"_id": "a1", "custom_partner": "a0", "partner_id": "a0"}, + {"_id": "a2", "partner_id": "a3"}, + {"_id": "a3", "partner_id": "a2"}, + ] + cfg = NetworkConfig( + structural_attribute_roles=StructuralAttributeRoles( + partner_id="custom_partner" + ) + ) + resolved = _resolve_structural_keys_from_config(agents, cfg) + assert resolved["partner_id"] == "custom_partner" + def test_generate_network_resume_from_checkpoint_matches_fresh(self, sample_agents): """Resuming from a saved similarity checkpoint should match a fresh run.""" import sqlite3 diff --git a/tests/test_network_config_generator.py b/tests/test_network_config_generator.py index fc88e3c..ce6cc1d 100644 --- a/tests/test_network_config_generator.py +++ b/tests/test_network_config_generator.py @@ -3,11 +3,16 @@ from extropy.core.models.population import ( AttributeSpec, BooleanDistribution, + CategoricalDistribution, GroundingInfo, NormalDistribution, SamplingConfig, ) -from extropy.population.network.config_generator import _convert_to_network_config +from extropy.population.network.config_generator import ( + _build_structural_role_prompt, + _convert_to_network_config, + _resolve_structural_roles_deterministic, +) def test_convert_network_config_coerces_degree_multiplier_condition_types( @@ -86,3 +91,136 @@ def test_convert_network_config_coerces_degree_multiplier_condition_types( assert by_attr["income"] == 55000.5 assert isinstance(by_attr["income"], float) assert by_attr["gender"] == "male" + + +def test_resolve_structural_roles_deterministic_avoids_boolean_dependents( + minimal_population_spec, +): + pop_spec = minimal_population_spec.model_copy(deep=True) + pop_spec.attributes.extend( + [ + AttributeSpec( + name="household_id", + type="categorical", + category="population_specific", + description="Household identifier", + scope="household", + sampling=SamplingConfig( + strategy="independent", + distribution=CategoricalDistribution( + options=["h1", "h2"], + weights=[0.5, 0.5], + ), + ), + grounding=GroundingInfo(level="low", method="estimated"), + ), + AttributeSpec( + name="partner_id", + type="categorical", + category="population_specific", + description="Partner agent id", + sampling=SamplingConfig( + strategy="independent", + distribution=CategoricalDistribution( + options=["a1", "a2"], + weights=[0.5, 0.5], + ), + ), + grounding=GroundingInfo(level="low", method="estimated"), + ), + AttributeSpec( + name="religious_affiliation", + type="categorical", + category="population_specific", + description="Faith affiliation", + identity_type="religious_affiliation", + sampling=SamplingConfig( + strategy="independent", + distribution=CategoricalDistribution( + options=["none", "faith_a"], + weights=[0.6, 0.4], + ), + ), + grounding=GroundingInfo(level="low", method="estimated"), + ), + AttributeSpec( + name="has_children", + type="boolean", + category="population_specific", + description="Whether agent has children", + identity_type="parental_status", + sampling=SamplingConfig( + strategy="independent", + distribution=BooleanDistribution(probability_true=0.4), + ), + grounding=GroundingInfo(level="low", method="estimated"), + ), + AttributeSpec( + name="dependents_count", + type="int", + category="population_specific", + description="Number of dependents in household", + sampling=SamplingConfig( + strategy="independent", + distribution=NormalDistribution( + mean=1.2, std=0.8, min=0.0, max=5.0 + ), + ), + grounding=GroundingInfo(level="low", method="estimated"), + ), + ] + ) + pop_spec.sampling_order.extend( + [ + "household_id", + "partner_id", + "religious_affiliation", + "has_children", + "dependents_count", + ] + ) + + agents_sample = [ + { + "household_id": "h1", + "partner_id": "agent_1", + "religious_affiliation": "faith_a", + "has_children": True, + "dependents_count": 2, + } + ] + resolved, ambiguous = _resolve_structural_roles_deterministic( + pop_spec, agents_sample=agents_sample + ) + + assert resolved["household_id"] == "household_id" + assert resolved["partner_id"] == "partner_id" + assert resolved["religion"] == "religious_affiliation" + assert resolved["dependents"] != "has_children" + assert "dependents" in ambiguous or resolved["dependents"] == "dependents_count" + + +def test_structural_role_prompt_includes_full_attribute_set(minimal_population_spec): + pop_spec = minimal_population_spec.model_copy(deep=True) + for idx in range(60): + name = f"extra_attr_{idx:02d}" + pop_spec.attributes.append( + AttributeSpec( + name=name, + type="categorical", + category="context_specific", + description=f"Extra attribute {idx}", + sampling=SamplingConfig( + strategy="independent", + distribution=CategoricalDistribution( + options=["a", "b"], + weights=[0.5, 0.5], + ), + ), + grounding=GroundingInfo(level="low", method="estimated"), + ) + ) + pop_spec.sampling_order.append(name) + + prompt = _build_structural_role_prompt(pop_spec) + assert "extra_attr_59" in prompt From 11c90238a243fad4c3535e52c52bfaf2aa447a00 Mon Sep 17 00:00:00 2001 From: RandomOscillations Date: Sun, 22 Feb 2026 17:14:35 -0500 Subject: [PATCH 10/20] Add validated ASI network artifacts from accepted run --- ...network-config.seed42.20260222-170102.yaml | 343 ++++++++++++++++++ examples/study-02/study.db | Bin 23523328 -> 40755200 bytes 2 files changed, 343 insertions(+) create mode 100644 examples/study-02/scenario/asi-announcement/network-config.seed42.20260222-170102.yaml diff --git a/examples/study-02/scenario/asi-announcement/network-config.seed42.20260222-170102.yaml b/examples/study-02/scenario/asi-announcement/network-config.seed42.20260222-170102.yaml new file mode 100644 index 0000000..740825a --- /dev/null +++ b/examples/study-02/scenario/asi-announcement/network-config.seed42.20260222-170102.yaml @@ -0,0 +1,343 @@ +avg_degree: 12.0 +rewire_prob: 0.05 +similarity_store_threshold: 0.05 +similarity_threshold: 0.3 +similarity_steepness: 10.0 +candidate_mode: blocked +candidate_pool_multiplier: 12.0 +min_candidate_pool: 80 +similarity_workers: 8 +similarity_chunk_size: 64 +checkpoint_every_rows: 250 +triadic_closure_prob: 0.6 +target_clustering: 0.35 +target_modularity: 0.55 +inter_community_scale: 0.3 +max_calibration_iterations: 12 +quality_profile: balanced +topology_gate: strict +max_calibration_minutes: 30 +calibration_restarts: 4 +target_largest_component_ratio: 0.95 +target_degree_tolerance_pct: 0.15 +target_modularity_tolerance: 0.1 +target_clustering_tolerance: 0.08 +bridge_budget_fraction: 0.08 +swap_passes: 3 +power_law_exponent: 2.5 +identity_clustering_boost: 1.5 +auto_save_generated_config: true +allow_quarantine: true +quarantine_suffix: rejected +attribute_weights: + urban_rural: + weight: 3.5 + match_type: exact + us_region: + weight: 3.0 + match_type: exact + political_identity: + weight: 3.0 + match_type: within_n + range_value: 1.0 + ordinal_levels: + Strong Democrat: 1 + Lean Democrat: 2 + True Independent: 3 + Lean Republican: 4 + Strong Republican: 5 + education_level: + weight: 2.5 + match_type: within_n + range_value: 1.0 + ordinal_levels: + Less than HS: 1 + HS diploma/GED: 2 + Some college: 3 + Bachelor's degree: 4 + Graduate degree: 5 + religious_engagement: + weight: 2.5 + match_type: within_n + range_value: 1.0 + ordinal_levels: + Secular/None: 1 + Nominally affiliated: 2 + Moderately religious: 3 + Devout: 4 + race_ethnicity: + weight: 2.0 + match_type: exact + age: + weight: 2.0 + match_type: numeric_range + range_value: 15.0 + employment_sector: + weight: 2.0 + match_type: exact + financial_margin: + weight: 1.5 + match_type: within_n + range_value: 1.0 + ordinal_levels: + Comfortable savings buffer: 1 + Modest buffer: 2 + Paycheck-to-paycheck: 3 + In debt/deficit: 4 + marital_status: + weight: 1.5 + match_type: exact + primary_info_source: + weight: 1.5 + match_type: exact + social_media_behavior: + weight: 1.0 + match_type: within_n + range_value: 1.0 + ordinal_levels: + Non-user: 1 + Passive consumer: 2 + Occasional poster: 3 + Active poster/Influencer: 4 +degree_multipliers: +- attribute: social_media_behavior + condition: Active poster/Influencer + multiplier: 2.5 + rationale: "Social media influencers and active posters maintain large networks\ + \ by definition \u2014 they broadcast to followers, engage in comment threads,\ + \ and are sought out for opinions. This is the primary network hub mechanic in\ + \ modern America." +- attribute: political_engagement_level + condition: Highly engaged partisan + multiplier: 2.0 + rationale: Political activists, campaign volunteers, and party organizers have unusually + wide networks spanning neighbors, coworkers, and fellow partisans. They attend + events, canvass neighborhoods, and coordinate across communities. +- attribute: employment_sector + condition: Self-employed + multiplier: 1.8 + rationale: Small business owners and entrepreneurs must cultivate broad networks + of clients, vendors, suppliers, and referral sources. Their livelihood depends + on relationship breadth. +- attribute: religious_engagement + condition: Devout + multiplier: 1.7 + rationale: Devout individuals attend religious services regularly, participate in + faith communities, serve on committees, and form dense congregational networks. + Churches remain one of the most powerful community-forming institutions in America. +- attribute: employment_sector + condition: State/local government + multiplier: 1.5 + rationale: "State and local government workers are embedded in civic infrastructure\ + \ \u2014 they interact with the public, attend municipal meetings, and know local\ + \ officials, community leaders, and service recipients." +- attribute: marital_status + condition: Married/partnered + multiplier: 1.3 + rationale: "Married couples have dual social networks that often merge \u2014 each\ + \ partner brings their own friends, family, and colleagues, expanding the household's\ + \ total social footprint." +- attribute: has_children + condition: true + multiplier: 1.4 + rationale: "Parents of school-age children are embedded in school networks, sports\ + \ leagues, PTAs, and playdate circles \u2014 generating many weak and strong ties\ + \ with other parents in the same community." +edge_type_rules: +- name: close_family_household + condition: a_marital_status == b_marital_status and a_us_region == b_us_region and + a_urban_rural == b_urban_rural and a_race_ethnicity == b_race_ethnicity + priority: 50 + description: "Household members and close family sharing region, settlement type,\ + \ and background \u2014 proxies for family and very close community bonds." +- name: coworker_professional + condition: a_employment_sector == b_employment_sector and a_us_region == b_us_region + and a_employment_sector != "Retired" and a_employment_sector != "Unemployed" and + a_employment_sector != "Not in labor force" + priority: 40 + description: "People working in the same employment sector within the same region\ + \ \u2014 proxy for professional and coworker relationships." +- name: faith_community + condition: a_religious_engagement == b_religious_engagement and a_religious_engagement + != "Secular/None" and a_us_region == b_us_region and a_urban_rural == b_urban_rural + priority: 35 + description: "People sharing similar religious engagement levels in the same region\ + \ and settlement type \u2014 proxy for fellow congregants and faith-based community\ + \ bonds." +- name: political_tribe + condition: a_political_identity == b_political_identity and a_us_region == b_us_region + and a_political_engagement_level == b_political_engagement_level + priority: 30 + description: "Highly aligned partisans in the same region with the same engagement\ + \ level \u2014 proxies for political club members, activists, and ideologically\ + \ sorted social circles." +- name: neighbor_community + condition: a_us_region == b_us_region and a_urban_rural == b_urban_rural + priority: 20 + description: "People in the same region and settlement type \u2014 broad neighborhood,\ + \ community, and local acquaintance ties." +- name: media_echo_chamber + condition: a_primary_info_source == b_primary_info_source and a_political_identity + == b_political_identity + priority: 15 + description: "People consuming the same primary media source with the same political\ + \ identity \u2014 online communities, comment sections, Facebook groups, and podcast\ + \ listener circles that transcend geography." +influence_factors: +- attribute: political_engagement_level + type: ordinal + levels: + Disengaged: 1 + Single-issue voter: 2 + Moderate/occasional participant: 3 + Highly engaged partisan: 4 + weight: 0.35 + description: Highly engaged partisans and political activists actively push information, + frame events, and mobilize others. Disengaged citizens are disproportionately + influenced by the politically active people in their networks. +- attribute: social_media_behavior + type: ordinal + levels: + Non-user: 1 + Passive consumer: 2 + Occasional poster: 3 + Active poster/Influencer: 4 + weight: 0.35 + description: Active posters and influencers broadcast information asymmetrically + to their followers. Passive consumers receive information without generating it, + creating a clear influence asymmetry in digital information flows. +- attribute: education_level + type: ordinal + levels: + Less than HS: 1 + HS diploma/GED: 2 + Some college: 3 + Bachelor's degree: 4 + Graduate degree: 5 + weight: 0.25 + description: "Higher educational attainment confers perceived epistemic authority\ + \ in peer conversations \u2014 people with advanced degrees are more often deferred\ + \ to on policy, science, and technical questions, even by friends with strong\ + \ opinions." +- attribute: household_income + type: numeric + weight: 0.15 + description: Wealth confers social status and decision-making authority. Higher-income + individuals are more likely to be opinion leaders in consumer decisions, investment + behavior, and lifestyle choices. Economic precarity reduces social influence. +structural_attribute_roles: + household_id: household_income + partner_id: marital_status + age: age + sector: employment_sector + region: urban_rural + urbanicity: urban_rural + religion: religious_engagement +default_edge_type: acquaintance +ordinal_levels: + political_identity: + Strong Democrat: 1 + Lean Democrat: 2 + True Independent: 3 + Lean Republican: 4 + Strong Republican: 5 + education_level: + Less than HS: 1 + HS diploma/GED: 2 + Some college: 3 + Bachelor's degree: 4 + Graduate degree: 5 + religious_engagement: + Secular/None: 1 + Nominally affiliated: 2 + Moderately religious: 3 + Devout: 4 + financial_margin: + Comfortable savings buffer: 1 + Modest buffer: 2 + Paycheck-to-paycheck: 3 + In debt/deficit: 4 + social_media_behavior: + Non-user: 1 + Passive consumer: 2 + Occasional poster: 3 + Active poster/Influencer: 4 +generated_from: "Nationally representative US adult population (18-80). Must capture\ + \ the demographic, economic, and attitudinal fault lines that drive divergent responses\ + \ to major national events \u2014 especially technology disruption, economic shocks,\ + \ cultural controversies, and electoral behavior in the 2026 midterm elections.\n\ + \nBeyond standard demographics (age, gender, race/ethnicity, education, income,\ + \ geography), prioritize attributes that determine how people process and react\ + \ to news, make decisions under uncertainty, and vote:\n\n- technology adoption\ + \ posture and digital literacy (technophobe to early adopter)\n- media ecosystem\ + \ and primary information sources (cable news, social media, podcasts, local news,\ + \ none)\n- institutional trust (government, corporations, media, science, financial\ + \ system)\n- financial margin and economic anxiety (savings buffer, debt load, paycheck-to-paycheck\ + \ status)\n- employment sector and job security perception (federal/state government,\ + \ private sector, gig, self-employed, retired)\n- consumer identity and brand relationship\ + \ (brand loyal, price-driven, values-driven)\n- social media behavior and influence\ + \ (passive consumer, active poster, influencer, non-user)\n- investment and financial\ + \ literacy (no investments, 401k only, active trader, crypto holder)\n- religious\ + \ engagement and worldview (secular, moderate, devout, denomination)\n- political\ + \ identity and engagement level (strong partisan, moderate, disengaged, single-issue)\n\ + - 2024 presidential vote (Trump, Harris, third party, didn\u2019t vote, not eligible)\n\ + - congressional district competitiveness (safe R, safe D, swing district)\n- midterm\ + \ turnout propensity (always, sometimes, rarely, never)\n- issue salience for 2026\ + \ (economy/jobs, abortion/reproductive rights, immigration, democracy/rule of law,\ + \ healthcare, climate, education, AI/technology)\n- approval of current direction\ + \ (strongly approve to strongly disapprove)\n\nGeographic distribution should span\ + \ urban/suburban/rural across major US regions (Northeast, Southeast, Midwest, Southwest,\ + \ West Coast), with realistic correlation structure between attributes." +generation_rationale: "This is a nationally representative US adult population simulation\ + \ focused on political, economic, and cultural fault lines. Here's my reasoning\ + \ for each design choice:\n\n**Average Degree (12):** General consumer/citizen population\ + \ with moderate social connectivity. Americans have ~10-15 meaningful social contacts\ + \ on average. This captures neighborhood friends, coworkers, family-adjacent contacts,\ + \ and community ties without over-connecting.\n\n**Attribute Weights:**\n1. **urban_rural\ + \ (3.5)** \u2013 The single strongest driver of who you know. Rural, suburban, and\ + \ urban Americans live in fundamentally different social ecosystems with little\ + \ cross-over. Also proxies commute patterns, church vs. bar culture, etc.\n2. **us_region\ + \ (3.0)** \u2013 Regional identity shapes social circles deeply. Northeasterners\ + \ and Southerners rarely know each other organically.\n3. **political_identity (3.0)**\ + \ \u2013 Political homophily is one of the strongest documented social sorting forces\ + \ in modern America. People self-select neighborhoods, churches, and friend groups\ + \ by politics.\n4. **education_level (2.5)** \u2013 Educational attainment strongly\ + \ predicts social circle composition (college-educated people disproportionately\ + \ befriend other college-educated people).\n5. **religious_engagement (2.5)** \u2013\ + \ Church is one of the primary community-forming institutions in America, especially\ + \ outside urban cores. Devout people form dense church-based networks.\n6. **employment_sector\ + \ (2.0)** \u2013 Coworkers form natural social bonds. Government workers know government\ + \ workers; gig workers are more isolated.\n7. **age (2.0)** \u2013 Age homophily\ + \ is strong \u2014 people form friendships in age-proximate cohorts (school, early\ + \ career, retirement).\n8. **primary_info_source (1.5)** \u2013 Shared media ecosystems\ + \ reflect and reinforce social clustering. People who watch the same cable news\ + \ or follow the same podcasters often share social circles.\n9. **race_ethnicity\ + \ (2.0)** \u2013 Residential segregation and cultural networks mean race/ethnicity\ + \ remains a meaningful predictor of social connection in the US.\n10. **marital_status\ + \ (1.5)** \u2013 Married couples socialize with other married couples; singles gravitate\ + \ toward singles. Life-stage sorting.\n11. **financial_margin (1.5)** \u2013 Economic\ + \ class shapes where you live, what you do for fun, and who you meet. Paycheck-to-paycheck\ + \ people and comfortable savers rarely share social worlds.\n12. **social_media_behavior\ + \ (1.0)** \u2013 Active posters and influencers disproportionately connect with\ + \ other active users; non-users form offline-only networks.\n\n**Degree Multipliers:**\n\ + - Active poster/Influencer: These people have structurally more connections by definition\ + \ \u2014 they maintain large social media followings and offline networks.\n- Highly\ + \ engaged partisan: Political organizers, campaign volunteers, and activists have\ + \ wide networks spanning their community.\n- Self-employed: Business owners must\ + \ maintain broad client/vendor/community networks.\n- Extraversion (high): Extraverts\ + \ genuinely accumulate more social ties \u2014 but we'll proxy this through social_media_behavior\ + \ and engagement_level since we want observable attributes.\n- Religious devout:\ + \ Regular church attendance creates dense community networks.\n- Federal/State government\ + \ workers: Public sector jobs involve community-facing roles and professional networks.\n\ + \n**Edge Types:**\n- Household members (same household_id) \u2192 family\n- Same\ + \ region + same urban/rural + same political identity \u2192 close community (political\ + \ tribe)\n- Same employment sector (non-retired/unemployed) \u2192 coworker/professional\n\ + - Same us_region + same religious_engagement (devout/moderate) \u2192 faith community\n\ + - Same us_region + similar age \u2192 neighbor/community acquaintance\n- Default:\ + \ acquaintance (weak tie)\n\n**Influence Factors:**\n- education_level: Higher education\ + \ \u2192 more epistemic authority in conversations\n- political_engagement_level:\ + \ Highly engaged partisans and activists influence disengaged peers\n- social_media_behavior:\ + \ Active posters/influencers push information asymmetrically to passive consumers\n\ + - household_income: Wealth confers social status and decision-making authority in\ + \ 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Date: Sun, 22 Feb 2026 19:32:57 -0500 Subject: [PATCH 11/20] simulation: interleave conversations per reasoning chunk with async-safe execution --- extropy/simulation/engine.py | 317 +++++++++++++++++++---------------- 1 file changed, 177 insertions(+), 140 deletions(-) diff --git a/extropy/simulation/engine.py b/extropy/simulation/engine.py index 07e06ca..d94d588 100644 --- a/extropy/simulation/engine.py +++ b/extropy/simulation/engine.py @@ -14,10 +14,8 @@ import json import logging -import queue import random import sqlite3 -import threading import time import uuid from datetime import datetime @@ -553,25 +551,21 @@ def _execute_timestep(self, timestep: int) -> TimestepSummary: total_new_exposures, forced_reason_ids = self._apply_exposures(timestep) self.total_exposures += total_new_exposures - # 2. Chunked reasoning — each chunk has its own transaction - agents_reasoned, state_changes, shares_occurred, reasoning_results = ( - self._reason_agents(timestep, forced_reason_ids=forced_reason_ids) - ) - - # 2c. Execute conversations (if fidelity > low) - conversations_executed = 0 - conversation_state_changes = 0 - if self.config.fidelity != "low" and reasoning_results: - conv_results = self._execute_conversations(timestep, reasoning_results) - conversations_executed = len(conv_results) - conversation_state_changes = self._apply_conversation_overrides( - timestep, conv_results + # 2. Chunked reasoning + in-timestep conversation interleaving + ( + agents_reasoned, + state_changes, + shares_occurred, + reasoning_meta, + ) = self._reason_agents(timestep, forced_reason_ids=forced_reason_ids) + conversations_executed = reasoning_meta.get("conversations_executed", 0) + conversation_state_changes = reasoning_meta.get("conversation_state_changes", 0) + + if conversations_executed > 0: + logger.info( + f"[TIMESTEP {timestep}] Conversations: {conversations_executed} executed, " + f"{conversation_state_changes} state changes" ) - if conversations_executed > 0: - logger.info( - f"[TIMESTEP {timestep}] Conversations: {conversations_executed} executed, " - f"{conversation_state_changes} state changes" - ) # 2e. Record social posts from agents who shared posts_recorded = self._record_social_posts(timestep) @@ -603,7 +597,7 @@ def _execute_timestep(self, timestep: int) -> TimestepSummary: ) summary.new_exposures = total_new_exposures summary.agents_reasoned = agents_reasoned - summary.state_changes = state_changes + summary.state_changes = state_changes + conversation_state_changes summary.shares_occurred = shares_occurred self.state_manager.save_timestep_summary(summary) @@ -679,13 +673,23 @@ def _apply_exposures(self, timestep: int) -> tuple[int, set[str]]: def _reason_agents( self, timestep: int, forced_reason_ids: set[str] | None = None - ) -> tuple[int, int, int, list[tuple[str, ReasoningResponse | None]]]: - """Identify agents needing reasoning, run in chunks, commit per-chunk. + ) -> tuple[int, int, int, dict[str, int]]: + """Identify agents needing reasoning and process chunk-by-chunk. + + For medium/high fidelity, each reasoning chunk is followed by a + conversation pass. This allows within-timestep social feedback to + influence later chunks without requiring extra global re-reason rounds. On resume, agents already processed this timestep are skipped. Returns: - Tuple of (agents_reasoned, state_changes, shares_occurred, reasoning_results). + Tuple of ( + agents_reasoned, + state_changes, + shares_occurred, + metadata with conversation totals for this timestep + (conversations_executed, conversation_state_changes), + ). """ self._apply_runtime_guardrails(timestep) agents_to_reason = self.state_manager.get_agents_to_reason( @@ -717,7 +721,12 @@ def _reason_agents( ) if not agents_to_reason: - return 0, 0, 0, [] + return ( + 0, + 0, + 0, + {"conversations_executed": 0, "conversation_state_changes": 0}, + ) # Create on_agent_done closure for progress tracking def _on_agent_done(agent_id: str, result: Any) -> None: @@ -736,105 +745,41 @@ def _on_agent_done(agent_id: str, result: Any) -> None: ): self._log_verbose_summary(self._progress.snapshot()) - # Build contexts and old states - contexts = [] - old_states: dict[str, AgentState] = {} - for agent_id in agents_to_reason: - old_state = self.state_manager.get_agent_state(agent_id) - old_states[agent_id] = old_state - context = self._build_reasoning_context(agent_id, old_state, timestep) - contexts.append(context) - completed_chunks = self.study_db.get_completed_simulation_chunks( self.run_id, timestep ) - totals = {"reasoned": 0, "changes": 0, "shares": 0} - all_reasoning_results: list[tuple[str, ReasoningResponse | None]] = [] - work_queue: queue.Queue[tuple[int, list[tuple[str, Any]], bool] | object] = ( - queue.Queue(maxsize=self.writer_queue_size) - ) - sentinel = object() - writer_error: list[Exception] = [] - - def _writer_loop() -> None: - chunks_since_checkpoint = 0 - pending_chunks: list[tuple[int, list[tuple[str, Any]], bool]] = [] - - def _flush_pending() -> None: - nonlocal chunks_since_checkpoint - if not pending_chunks: - return - - with self.state_manager.transaction(): - for chunk_index, chunk_results, _is_last_chunk in pending_chunks: - reasoned, changes, shares = self._process_reasoning_chunk( - timestep, chunk_results, old_states - ) - totals["reasoned"] += reasoned - totals["changes"] += changes - totals["shares"] += shares - - for chunk_index, _chunk_results, is_last_chunk in pending_chunks: - self.study_db.save_simulation_checkpoint( - run_id=self.run_id, - timestep=timestep, - chunk_index=chunk_index, - status="done", + totals = { + "reasoned": 0, + "changes": 0, + "shares": 0, + "conversations": 0, + "conversation_changes": 0, + } + chunks_since_checkpoint = 0 + conversation_counts: dict[str, int] = {} + if self.config.fidelity != "low" and completed_chunks: + # Resume-safe conversation budgeting: restore per-agent counts + # from conversations already saved for this timestep. + existing = self.study_db.get_conversations_for_timestep(self.run_id, timestep) + for conv in existing: + initiator = conv.get("initiator_id") + target = conv.get("target_id") + target_is_npc = bool(conv.get("target_is_npc")) + if initiator: + conversation_counts[initiator] = ( + conversation_counts.get(initiator, 0) + 1 ) - chunks_since_checkpoint += 1 - if ( - chunks_since_checkpoint >= self.checkpoint_every_chunks - or is_last_chunk - ): - self.study_db.set_run_metadata( - self.run_id, - "last_checkpoint", - f"{timestep}:{chunk_index}", - ) - chunks_since_checkpoint = 0 - - pending_chunks.clear() - - try: - while True: - item = work_queue.get() - try: - if item is sentinel: - _flush_pending() - break - - chunk_index, chunk_results, is_last_chunk = item - if chunk_index in completed_chunks: - continue - pending_chunks.append( - (chunk_index, chunk_results, is_last_chunk) - ) - if ( - len(pending_chunks) >= self.db_write_batch_size - or is_last_chunk - ): - _flush_pending() - finally: - work_queue.task_done() - except Exception as e: # pragma: no cover - surfaced to caller - writer_error.append(e) - - writer_thread = threading.Thread( - target=_writer_loop, - name=f"sim-writer-{self.run_id}-{timestep}", - daemon=True, - ) - writer_thread.start() + if target and not target_is_npc: + conversation_counts[target] = conversation_counts.get(target, 0) + 1 import asyncio from ..core.providers import close_simulation_provider async def _run_all_chunks(): + nonlocal chunks_since_checkpoint try: - for chunk_start in range(0, len(contexts), self.chunk_size): - if writer_error: - break + for chunk_start in range(0, len(agents_to_reason), self.chunk_size): self._apply_runtime_guardrails(timestep) chunk_index = chunk_start // self.chunk_size if chunk_index in completed_chunks: @@ -842,9 +787,18 @@ async def _run_all_chunks(): f"[TIMESTEP {timestep}] Skipping completed chunk {chunk_index}" ) continue - chunk_contexts = contexts[ + chunk_agent_ids = agents_to_reason[ chunk_start : chunk_start + self.chunk_size ] + old_states: dict[str, AgentState] = {} + chunk_contexts = [] + for agent_id in chunk_agent_ids: + old_state = self.state_manager.get_agent_state(agent_id) + old_states[agent_id] = old_state + context = self._build_reasoning_context( + agent_id, old_state, timestep + ) + chunk_contexts.append(context) reasoning_start = time.time() chunk_results, chunk_usage = await batch_reason_agents_async( @@ -869,37 +823,59 @@ async def _run_all_chunks(): if chunk_results else f"[TIMESTEP {timestep}] Chunk empty" ) - is_last_chunk = chunk_start + self.chunk_size >= len(contexts) - work_queue.put((chunk_index, chunk_results, is_last_chunk)) - # Collect results for conversation phase - all_reasoning_results.extend(chunk_results) + is_last_chunk = chunk_start + self.chunk_size >= len(agents_to_reason) + with self.state_manager.transaction(): + reasoned, changes, shares = self._process_reasoning_chunk( + timestep, chunk_results, old_states + ) + totals["reasoned"] += reasoned + totals["changes"] += changes + totals["shares"] += shares + + # In-timestep interleaving: run conversations after each + # chunk so later chunks can see updated social state. + if self.config.fidelity != "low" and chunk_results: + conv_results = await self._execute_conversations_async( + timestep, + chunk_results, + conversation_counts=conversation_counts, + ) + if conv_results: + conv_changes = self._apply_conversation_overrides( + timestep, conv_results + ) + totals["conversations"] += len(conv_results) + totals["conversation_changes"] += conv_changes + + self.study_db.save_simulation_checkpoint( + run_id=self.run_id, + timestep=timestep, + chunk_index=chunk_index, + status="done", + ) + chunks_since_checkpoint += 1 + if ( + chunks_since_checkpoint >= self.checkpoint_every_chunks + or is_last_chunk + ): + self.study_db.set_run_metadata( + self.run_id, + "last_checkpoint", + f"{timestep}:{chunk_index}", + ) + chunks_since_checkpoint = 0 finally: await close_simulation_provider() asyncio.run(_run_all_chunks()) - - work_queue.put(sentinel) - while work_queue.unfinished_tasks > 0: - if writer_error: - while True: - try: - work_queue.get_nowait() - work_queue.task_done() - except queue.Empty: - break - break - time.sleep(0.01) - - work_queue.join() - writer_thread.join(timeout=1) - if writer_error: - raise writer_error[0] - return ( totals["reasoned"], totals["changes"], totals["shares"], - all_reasoning_results, + { + "conversations_executed": totals["conversations"], + "conversation_state_changes": totals["conversation_changes"], + }, ) def _process_reasoning_chunk( @@ -1139,16 +1115,19 @@ def _process_reasoning_chunk( return agents_reasoned, state_changes, shares_occurred - def _execute_conversations( + async def _execute_conversations_async( self, timestep: int, reasoning_results: list[tuple[str, ReasoningResponse | None]], + conversation_counts: dict[str, int] | None = None, ) -> list[ConversationResult]: """Execute conversations based on talk_to actions from reasoning. Args: timestep: Current simulation timestep reasoning_results: List of (agent_id, response) pairs + conversation_counts: Optional per-agent conversation counts for + enforcing a global per-timestep cap across chunked passes. Returns: List of conversation results @@ -1168,6 +1147,25 @@ def _execute_conversations( if not requests: return [] + max_per_agent = 1 if self.config.fidelity == "medium" else 2 + if self.config.fidelity == "low": + max_per_agent = 0 + + if conversation_counts is not None and max_per_agent > 0: + filtered_requests = [] + for req in requests: + if conversation_counts.get(req.initiator_id, 0) >= max_per_agent: + continue + if ( + not req.target_is_npc + and conversation_counts.get(req.target_id, 0) >= max_per_agent + ): + continue + filtered_requests.append(req) + requests = filtered_requests + if not requests: + return [] + # Prioritize and resolve conflicts batches, _deferred = prioritize_and_resolve_conflicts( requests, fidelity=self.config.fidelity @@ -1194,7 +1192,6 @@ def _execute_conversations( # Execute all batches (in practice, usually just one batch) all_results: list[ConversationResult] = [] - import asyncio from ..core.providers import close_simulation_provider async def _run_conversations(): @@ -1213,7 +1210,19 @@ async def _run_conversations(): finally: await close_simulation_provider() - asyncio.run(_run_conversations()) + await _run_conversations() + + if conversation_counts is not None and max_per_agent > 0: + for result in all_results: + if not result.messages: + continue + conversation_counts[result.initiator_id] = ( + conversation_counts.get(result.initiator_id, 0) + 1 + ) + if not result.target_is_npc: + conversation_counts[result.target_id] = ( + conversation_counts.get(result.target_id, 0) + 1 + ) # Save conversations to DB for result in all_results: @@ -1244,6 +1253,34 @@ async def _run_conversations(): logger.info(f"[TIMESTEP {timestep}] Executed {len(all_results)} conversations") return all_results + def _execute_conversations( + self, + timestep: int, + reasoning_results: list[tuple[str, ReasoningResponse | None]], + conversation_counts: dict[str, int] | None = None, + ) -> list[ConversationResult]: + """Synchronous wrapper for conversation execution. + + Use `_execute_conversations_async` from async call paths. + """ + import asyncio + + try: + asyncio.get_running_loop() + except RuntimeError: + return asyncio.run( + self._execute_conversations_async( + timestep, + reasoning_results, + conversation_counts=conversation_counts, + ) + ) + + raise RuntimeError( + "_execute_conversations() cannot be called from an active event loop. " + "Use await _execute_conversations_async(...)." + ) + def _apply_conversation_overrides( self, timestep: int, From b79a9ae393a2ce94ec0da938a279b1939e01bacf Mon Sep 17 00:00:00 2001 From: DeveshParagiri Date: Mon, 23 Feb 2026 13:16:03 -0500 Subject: [PATCH 12/20] chore: update pipeline docs and core scenario/sample flow --- docs/atd-technical-instructions.md | 286 -- docs/commands.md | 171 +- docs/pipeline/network.md | 481 +++ docs/pipeline/persona.md | 41 +- docs/pipeline/sample.md | 81 +- docs/pipeline/scenario.md | 36 +- docs/pipeline/simulate.md | 475 +++ docs/pipeline/spec.md | 21 +- docs/study-2-iran-strikes.md | 371 +++ examples/.DS_Store | Bin 6148 -> 6148 bytes extropy/cli/commands/scenario.py | 30 +- extropy/cli/commands/validate.py | 64 +- extropy/population/sampler/distributions.py | 36 +- .../spec_builder/hydrators/household.py | 4 + extropy/simulation/state.py | 14 +- ...config.default.seed42.20260215-233218.yaml | 220 -- ...ig.default.seednoseed.20260215-231703.yaml | 296 -- test-v2-e2e/extended.persona.yaml | 851 ----- test-v2-e2e/extended.scenario.yaml | 276 -- test-v2-e2e/extended.yaml | 2930 ----------------- test-v2-e2e/prompt.txt | 1 - test-v2-e2e/spec.yaml | 1998 ----------- test-v2-e2e/study.db | Bin 4898816 -> 0 bytes tests/test_cli.py | 55 +- 24 files changed, 1738 insertions(+), 7000 deletions(-) delete mode 100644 docs/atd-technical-instructions.md create mode 100644 docs/pipeline/network.md create mode 100644 docs/pipeline/simulate.md create mode 100644 docs/study-2-iran-strikes.md delete mode 100644 test-v2-e2e/extended.network-config.default.seed42.20260215-233218.yaml delete mode 100644 test-v2-e2e/extended.network-config.default.seednoseed.20260215-231703.yaml delete mode 100644 test-v2-e2e/extended.persona.yaml delete mode 100644 test-v2-e2e/extended.scenario.yaml delete mode 100644 test-v2-e2e/extended.yaml delete mode 100644 test-v2-e2e/prompt.txt delete mode 100644 test-v2-e2e/spec.yaml delete mode 100644 test-v2-e2e/study.db diff --git a/docs/atd-technical-instructions.md b/docs/atd-technical-instructions.md deleted file mode 100644 index e7d832d..0000000 --- a/docs/atd-technical-instructions.md +++ /dev/null @@ -1,286 +0,0 @@ -# Technical Instructions: Simulating Congestion Tax Response with Extropy - -**Prepared for:** Austin Transportation Department – Urban Planning & Data Team -**Deliverable:** `mode_shift_report.json` containing simulated commuter response data -**Estimated Time:** 20–30 minutes - ---- - -## Prerequisites - -Before beginning, ensure your workstation meets the following requirements: - -| Requirement | Specification | -|-------------|---------------| -| Operating System | macOS 12+, Ubuntu 20.04+, or Windows 11 with WSL2 | -| Python | 3.10 or higher | -| RAM | 8GB minimum (16GB recommended for 500+ agents) | -| API Key | Anthropic or OpenAI API key with active billing | - -To verify your Python version, open a terminal and run: - -```bash -python3 --version -``` - -If the output shows `Python 3.10.x` or higher, proceed to Step 1. - ---- - -## Step 1: Install the Extropy CLI - -Install the Extropy command-line interface using pip: - -```bash -pip install extropy-cli -``` - -Wait for the installation to complete. You should see output ending with: - -``` -Successfully installed extropy-cli-x.x.x -``` - -To verify the installation, run: - -```bash -extropy --version -``` - ---- - -## Step 2: Configure Your API Key - -Extropy requires an LLM provider to generate agent populations. Set your API key as an environment variable: - -**For Anthropic (recommended):** -```bash -export ANTHROPIC_API_KEY="sk-ant-your-key-here" -``` - -**For OpenAI:** -```bash -export OPENAI_API_KEY="sk-your-key-here" -``` - -To make this permanent, add the export line to your `~/.bashrc` or `~/.zshrc` file. - ---- - -## Step 3: Initialize the Study Population - -Create a new study folder by running the `extropy spec` command with a description of your target population: - -```bash -extropy spec "Adult commuters in Austin, Texas who drive to work in the downtown core. Include variation in income levels, commute distances, access to public transit, and current transportation mode preferences." -o austin-congestion-study -``` - -This command performs two actions: -1. Creates the `austin-congestion-study/` folder structure -2. Generates a `population.v1.yaml` file containing demographic distributions - -Wait for the command to complete. You should see: - -``` -✓ Population spec created: austin-congestion-study/population.v1.yaml -``` - ---- - -## Step 4: Navigate to the Study Folder - -Change your working directory to the newly created study folder: - -```bash -cd austin-congestion-study -``` - -Verify the folder contents: - -```bash -ls -la -``` - -You should see: -``` -population.v1.yaml -study.db -``` - ---- - -## Step 5: Create the Congestion Tax Scenario - -Define the policy scenario using natural language. The Extropy engine interprets this description to generate appropriate events and behavioral triggers: - -```bash -extropy scenario "The City of Austin implements a $15 daily congestion tax for vehicles entering the downtown core during peak hours (7-9 AM and 4-7 PM). The policy is announced via local news, city government communications, and social media. Commuters must decide whether to continue driving and pay the tax, shift to public transit, adjust their work schedule, or work from home." -o congestion-tax -y -``` - -This creates the scenario configuration at `scenario/congestion-tax/scenario.v1.yaml`. - -Expected output: -``` -Creating scenario: congestion-tax/scenario.v1.yaml -Base population: population.v1.yaml - -✓ Found X NEW attributes -✓ Scenario created successfully -``` - ---- - -## Step 6: Generate the Persona Configuration - -Create the persona rendering configuration, which determines how agents are presented during simulation: - -```bash -extropy persona -s congestion-tax -y -``` - -This generates `scenario/congestion-tax/persona.v1.yaml`. - ---- - -## Step 7: Sample the Agent Population - -Generate 500 simulated commuters based on your population specification: - -```bash -extropy sample -s congestion-tax -n 500 --seed 42 -``` - -The `--seed 42` flag ensures reproducible results. Running this command again with the same seed will generate identical agents. - -Expected output: -``` -Sampling 500 agents... -✓ Sampled 500 agents to study.db -``` - ---- - -## Step 8: Generate the Social Network - -Create connections between agents to simulate information spread through social ties: - -```bash -extropy network -s congestion-tax --seed 42 -``` - -This step models how awareness of the congestion tax propagates through workplace, neighborhood, and social connections. - ---- - -## Step 9: Run the Simulation - -Execute the simulation with the following command: - -```bash -extropy simulate -s congestion-tax --seed 42 -``` - -A progress indicator will display simulation status: - -``` -Simulating congestion-tax scenario... -[████████████████████████████████] 100% - -✓ Simulation complete - Agents processed: 500 - Timesteps: 1 - Results saved to: study.db -``` - -**Note:** Simulation time varies based on agent count. For 500 agents, expect 2–5 minutes. - ---- - -## Step 10: Export the Results Report - -Extract the simulation results to a JSON file: - -```bash -extropy results --json > mode_shift_report.json -``` - -This command queries the simulation database and exports a summary report. - ---- - -## Step 11: Verify the Deliverable - -Confirm the report file exists and contains data: - -```bash -ls -la mode_shift_report.json -``` - -To preview the contents: - -```bash -head -50 mode_shift_report.json -``` - -The report contains aggregate statistics including: -- **Awareness rate:** Percentage of agents aware of the congestion tax -- **Attitude distribution:** Agent sentiment toward the policy -- **Behavioral outcomes:** Projected mode shift percentages (continue driving, switch to transit, work from home, etc.) - ---- - -## Deliverable Summary - -Upon completion, your `austin-congestion-study/` folder contains: - -``` -austin-congestion-study/ -├── study.db # Canonical simulation database -├── population.v1.yaml # Population specification -├── mode_shift_report.json # ← YOUR DELIVERABLE -└── scenario/ - └── congestion-tax/ - ├── scenario.v1.yaml # Scenario configuration - └── persona.v1.yaml # Persona rendering config -``` - -Submit `mode_shift_report.json` as evidence of completed simulation. - ---- - -## Figures - -### Figure 1: Terminal Output After Successful Simulation - -*Description for screenshot:* Capture your terminal window immediately after Step 9 completes. The screenshot should show: -- The `extropy simulate` command at the top -- The progress bar at 100% -- The success message showing "Simulation complete" with agent count -- A visible command prompt ready for the next command - -**Caption:** "Figure 1: Terminal output confirming successful simulation of 500 agents under the congestion tax scenario." - ---- - -### Figure 2: Final Folder Structure with Deliverable - -*Description for screenshot:* Open your file explorer (Finder on macOS, or VS Code's file tree) showing the `austin-congestion-study` folder expanded. Highlight or circle the `mode_shift_report.json` file. - -**Caption:** "Figure 2: Study folder structure showing the generated mode_shift_report.json deliverable file." - ---- - -## Troubleshooting - -| Error | Solution | -|-------|----------| -| `command not found: extropy` | Run `pip install extropy-cli` again, or check your PATH | -| `ANTHROPIC_API_KEY not set` | Export your API key (see Step 2) | -| `Rate limit exceeded` | Wait 60 seconds and retry the command | -| `Simulation timeout` | Reduce agent count with `-n 250` in Step 7 | - ---- - -## Contact - -For technical support with Extropy, consult the documentation or contact the development team. diff --git a/docs/commands.md b/docs/commands.md index 920c4da..0e7ca21 100644 --- a/docs/commands.md +++ b/docs/commands.md @@ -40,6 +40,8 @@ All commands support these global options: | `--cost` | Show cost summary after command completes | | `--study PATH` | Study folder path (auto-detected from cwd if not specified) | +**Note:** These are root-level Typer options. Place them before the subcommand, e.g. `extropy --json spec "Austin commuters" -o study`. + --- ## extropy spec @@ -61,6 +63,19 @@ cd surgeons && extropy spec "add income distribution" extropy spec "farmers" -o my-spec.yaml ``` +### What `spec` does in current flow + +1. Runs sufficiency check (may ask clarifications; in agent mode returns structured questions). +2. Selects attributes (strategy, scope, dependencies, semantic metadata). +3. Runs split hydration for distributions/formulas/modifiers. +4. Binds constraints + computes dependency-safe sampling order. +5. Builds and validates `PopulationSpec`. +6. Saves versioned output YAML. + +**Stage ownership notes:** +- Spec stage does **not** persist household config (household modeling is scenario-owned). +- Name generation is **not** part of spec generation; names are generated at sampling/runtime. + ### Arguments | Name | Type | Required | Description | @@ -76,6 +91,11 @@ extropy spec "farmers" -o my-spec.yaml | `--answers` | | string | | JSON with pre-supplied clarification answers (for agent mode) | | `--use-defaults` | | flag | false | Use defaults for ambiguous values instead of prompting | +### Validation Failure Behavior + +- If spec validation fails, CLI writes a versioned invalid artifact next to target output (`population.v1.yaml` -> `population.v1.invalid.v1.yaml`, then `.v2`, `.v3`, ...). +- Command exits non-zero after writing the invalid artifact. + --- ## extropy scenario @@ -92,25 +112,33 @@ extropy scenario "AI diagnostic tool adoption" -o ai-adoption extropy scenario "vaccine mandate" -o vaccine @pop:v1 # Rebase existing scenario to new population -extropy scenario ai-adoption --rebase @pop:v2 +extropy scenario "rebase marker" -o ai-adoption --rebase @pop:v2 ``` ### What the Scenario Command Does -1. **Discovers scenario-specific attributes** — Uses LLM to identify attributes relevant to this scenario that don't exist in the base population (e.g., `technology_anxiety`, `job_automation_exposure` for an AI scenario) -2. **Researches distributions** — Hydrates new attributes with realistic distributions, just like the spec command -3. **Generates household config** — Determines if/how household dynamics matter for this scenario -4. **Creates event definition** — The triggering event (type, content, source, credibility) -5. **Configures exposure rules** — How agents initially encounter the event (channels, targeting, timing) -6. **Defines outcomes** — What to measure (categorical, boolean, open-ended responses) -7. **Sets simulation parameters** — Timesteps, timeline mode (static vs evolving), stopping conditions +1. **Runs sufficiency check** — infers duration/type/unit/focus hints and asks clarifications if needed +2. **Discovers scenario-specific attributes** — identifies extension attributes not already in base population +3. **Hydrates extension + household config** — researches distributions and scenario household semantics +4. **Binds constraints** — validates dependencies and sampling order for extension attrs +5. **Compiles scenario dynamics** — builds event, exposure, interaction/spread, timeline, and outcomes +6. **Validates scenario contract** — deterministic checks before save (base+extended refs, literals, channels, timeline, outcomes) +7. **Saves versioned artifact** — `scenario/{name}/scenario.vN.yaml` (or versioned `.invalid` on fail-hard) + +### Sufficiency Behavior + +- Sufficiency is intentionally lenient, but deterministic post-processing adds guardrails: + - explicit timeline markers (for example `week 1`, `month 0`) force evolving mode + - static scenarios must have an explicit timestep unit (or trigger a clarification question) +- In agent mode, insufficiency returns structured questions with exit code `2`. +- `--use-defaults` retries sufficiency automatically using defaults from those clarification questions. ### Arguments | Name | Type | Required | Description | |------|------|----------|-------------| | `description` | string | yes | Scenario description (what event/situation to simulate) | -| `population_ref` | string | no | Population version reference: `@pop:v1`, `@pop:latest`, or path to YAML | +| `population_ref` | string | no | Population version reference: `@pop:v1` or `@pop:latest` | ### Options @@ -119,6 +147,9 @@ extropy scenario ai-adoption --rebase @pop:v2 | `--output` | `-o` | string | required | Scenario name (creates `scenario/{name}/scenario.v1.yaml`) | | `--rebase` | | string | | Rebase existing scenario to new population version (e.g. `@pop:v2`) | | `--timeline` | | string | `auto` | Timeline mode: `auto` (LLM decides), `static` (single event), `evolving` (multi-event) | +| `--timestep-unit` | | string | inferred | Override timestep unit: `hour`, `day`, `week`, `month`, `year` | +| `--max-timesteps` | | int | inferred | Override simulation horizon | +| `--use-defaults` | | flag | false | Auto-answer sufficiency clarifications with defaults | | `--yes` | `-y` | flag | false | Skip confirmation prompts | ### Scenario Spec Contents @@ -133,6 +164,15 @@ The generated `scenario.v1.yaml` includes: - **`spread`** — How information propagates through the network - **`outcomes`** — What to measure from each agent - **`simulation`** — Timestep config, stopping conditions, convergence settings +- **`household_config` + `agent_focus_mode`** — Scenario-owned household semantics for sample stage +- **`sampling_semantic_roles`** — Scenario-level semantic role mappings used by sampling/runtime checks +- **`identity_dimensions`** (optional) — Identity activation hints consumed by simulation prompts + +### Validation Failure Behavior + +- If no scenario extension attributes are discovered, scenario creation hard-fails and writes a versioned JSON invalid artifact. +- If compile fails mid-pipeline, scenario creation hard-fails and writes a versioned JSON invalid artifact. +- If final scenario validation fails, CLI writes versioned YAML invalid artifact (`scenario.vN.invalid.vK.yaml`) and exits non-zero. --- @@ -151,17 +191,35 @@ extropy persona -s ai-adoption@v1 extropy persona -s ai-adoption --show ``` +### What `persona` does in current flow + +1. Resolves scenario and loads `scenario.vN.yaml`. +2. Loads referenced base population and merges `extended_attributes`. +3. Runs persona generation pipeline (structure, boolean/categorical/relative/concrete phrasings). +4. Validates generated config against merged attributes (`validate_persona_config`). +5. Saves versioned output YAML (`persona.vN.yaml`). + +Notes: +- If sampled agents already exist, persona generation computes `population_stats` at generation time. +- If not, stats can be backfilled later at simulation runtime. + ### Options | Flag | Short | Type | Default | Description | |------|-------|------|---------|-------------| | `--scenario` | `-s` | string | auto | Scenario name (auto-selects if only one exists) | | `--output` | `-o` | path | | Output file (default: `scenario/{name}/persona.vN.yaml`) | -| `--preview/--no-preview` | | flag | true | Show a sample persona before saving | +| `--preview/--no-preview` | | flag | true | Reserved flag (currently not used as a separate generation gate) | | `--agent` | | int | 0 | Which agent to use for preview | | `--yes` | `-y` | flag | false | Skip confirmation prompts | | `--show` | | flag | false | Preview existing persona config without regenerating | +### Validation Failure Behavior + +- If generation fails, CLI writes a versioned JSON invalid artifact and exits non-zero. +- If persona validation fails, CLI writes versioned YAML invalid artifact (`persona.vN.invalid.vK.yaml`) and exits non-zero. +- `extropy validate persona.vN.yaml` (or `.invalid`) runs persona-specific validation against merged base+extended attributes. + --- ## extropy sample @@ -174,6 +232,16 @@ extropy sample -s ai-adoption -n 1000 --seed 42 --report extropy sample -n 500 # auto-selects scenario if only one exists ``` +### What `sample` does in current flow + +1. Resolves scenario and requires persona config pre-flight. +2. Loads base population + scenario extension and builds merged spec. +3. Recomputes merged sampling order via topological sort. +4. Validates merged spec. +5. Samples agents using scenario household config/focus/semantic roles. +6. Runs deterministic post-sample rule-pack gate. +7. Saves agents and run metadata to `study.db`. + ### Options | Flag | Short | Type | Default | Description | @@ -183,15 +251,24 @@ extropy sample -n 500 # auto-selects scenario if only one exists | `--seed` | | int | random | Random seed for reproducibility | | `--report` | `-r` | flag | false | Show distribution summaries and stats | | `--skip-validation` | | flag | false | Skip validator errors | +| `--strict-gates` | | flag | false | Promote high-risk warnings and post-sample condition warnings to fail-hard | **Exit codes:** 0 = Success, 1 = Validation error, 3 = File not found, 4 = Sampling error Sampling process: 1. Loads scenario's `base_population` spec 2. Merges with scenario's `extended_attributes` -3. Validates the merged spec -4. Samples agents -5. Saves to `study.db` keyed by `scenario_id` +3. Recomputes merged dependency order (topological sort) +4. Validates the merged spec +5. Samples agents +6. Applies rule-pack gate (`impossible`/`implausible`) +7. Saves to `study.db` keyed by `scenario_id` + +### Validation/Gate Failure Behavior + +- Missing persona config blocks sampling pre-flight. +- Merged-order cycles or merged-spec validation failures write versioned JSON invalid artifacts and exit non-zero. +- Post-sampling gate failure writes versioned JSON invalid artifact (`sample.invalid.vN.json`) and exits non-zero. --- @@ -206,6 +283,20 @@ extropy network -s ai-adoption --no-generate-config # Flat network, no si extropy network -s ai-adoption -c custom-network.yaml # Load custom config ``` +### What `network` does in current flow + +1. Resolves study + scenario and verifies sampled agents exist. +2. Loads scenario + base population and builds merged attribute context (base + extension) for config generation. +3. Resolves config in this order: + - explicit `--network-config` + - latest auto-detected `scenario//*.network-config.yaml` + - LLM-generated config (`--generate-config`, default) + - empty config fallback (`--no-generate-config`) +4. Applies CLI overrides, quality profile defaults, and resource auto-tuning. +5. Generates network (with metrics unless `--no-metrics`). +6. Evaluates topology gate and persists result to `study.db`. +7. Optionally exports a non-canonical JSON copy with `--output`. + ### Options | Flag | Short | Type | Default | Description | @@ -215,9 +306,9 @@ extropy network -s ai-adoption -c custom-network.yaml # Load custom config | `--network-config` | `-c` | path | | Custom network config YAML file | | `--save-config` | | path | | Save the (generated or loaded) network config to YAML | | `--generate-config` | | flag | true | Generate network config via LLM from population spec (default: enabled) | -| `--avg-degree` | | float | 20.0 | Target average degree (connections per agent) | -| `--rewire-prob` | | float | 0.05 | Watts-Strogatz rewiring probability | -| `--seed` | | int | random | Random seed for reproducibility | +| `--avg-degree` | | float | unset | Override target average degree (otherwise keep config value) | +| `--rewire-prob` | | float | unset | Override rewiring probability (otherwise keep config value) | +| `--seed` | | int | unset | Override config seed (if unset, generator picks seed) | | `--validate` | | flag | false | Print validation metrics | | `--no-metrics` | | flag | false | Skip computing node metrics (faster) | @@ -229,14 +320,14 @@ extropy network -s ai-adoption -c custom-network.yaml # Load custom config | `--candidate-mode` | string | `blocked` | Similarity candidate mode: `exact`, `blocked` | | `--candidate-pool-multiplier` | float | 12.0 | Blocked mode candidate pool size as multiple of avg_degree | | `--block-attr` | string (repeatable) | auto | Blocking attribute(s). If omitted, auto-selects top attributes | -| `--similarity-workers` | int | 1 | Worker processes for similarity computation | +| `--similarity-workers` | int | 0 | Worker processes for similarity computation (`0` = auto) | | `--similarity-chunk-size` | int | 64 | Row chunk size for similarity worker tasks | #### Checkpointing | Flag | Type | Default | Description | |------|------|---------|-------------| -| `--checkpoint` | path | | DB path for similarity checkpointing (must be study.db) | +| `--checkpoint` | path | | DB path for checkpointing (must resolve to the same file as `study.db`) | | `--resume` | flag | false | Resume similarity and calibration checkpoints from study.db | | `--checkpoint-every` | int | 250 | Write checkpoint every N processed similarity rows | @@ -248,6 +339,19 @@ extropy network -s ai-adoption -c custom-network.yaml # Load custom config | `--safe-auto-workers/--unsafe-auto-workers` | flag | true | Conservative auto tuning for laptops/VMs | | `--max-memory-gb` | float | | Optional memory budget cap for auto resource tuning | +### Validation/Gate Failure Behavior + +- Missing sampled agents blocks network generation pre-flight. +- Invalid option values (`quality_profile`, `candidate_mode`, `topology_gate`, checkpoint mismatch) exit non-zero. +- Strict topology-gate failures (`quality.accepted=false` with strict gate and `N>=50`) exit non-zero: + - by default, command saves a quarantined network artifact and does not report canonical success, + - if quarantine is disabled via advanced flag, command still exits non-zero. + +### Notes + +- Generated configs can be auto-saved into `scenario//network-config.seed*.yaml`. +- Use `extropy query network-status ` to inspect calibration/progress records. + --- ## extropy simulate @@ -261,6 +365,22 @@ extropy simulate -s ai-adoption --fidelity high extropy simulate -s asi-announcement --early-convergence off ``` +### What `simulate` does in current flow + +1. Resolves study folder and scenario. +2. Pre-flight checks required upstream artifacts: + - sampled agents exist for scenario, + - network edges exist for scenario, + - persona config exists for scenario. +3. Validates runtime flags (`--resume`/`--run-id`, `--resource-mode`, `--early-convergence`). +4. Resolves effective models/rate limits from CLI overrides then config defaults. +5. Runs simulation loop: + - seed + timeline + network exposures, + - chunked reasoning (two-pass by default, merged with `--merged-pass`), + - medium/high conversation interleaving, + - timestep summary + stopping checks. +6. Persists run state to canonical `study.db` and writes results artifacts to `results/{scenario}/` (or `--output`). + ### Options | Flag | Short | Type | Default | Description | @@ -335,6 +455,21 @@ Precedence: | `--verbose` | `-v` | flag | false | Show detailed logs | | `--debug` | | flag | false | Show debug-level logs (very verbose) | +### Runtime Notes + +- `--resume` requires an explicit `--run-id`. +- Scenario lookup is scenario-name first, with legacy id fallback for older studies. +- `--early-convergence auto` uses scenario YAML value when set; otherwise runtime auto-rule applies (do not early-stop while future timeline events remain). +- `low` fidelity skips conversations; `medium` and `high` enable conversations with stricter per-agent caps at lower fidelity. +- `--retention-lite` drops full raw reasoning payload retention to reduce DB/storage volume. + +### Failure Behavior + +- Missing study folder/scenario/persona/agents/network fails pre-flight and exits non-zero. +- Invalid flag values (for example bad `--resource-mode` or `--early-convergence`) fail fast and exit non-zero. +- Runtime exceptions mark the simulation run as `failed` in `simulation_runs` and return non-zero. +- Successful completion updates run status to `completed` or `stopped` (when a stop condition ends early). + --- ## extropy results diff --git a/docs/pipeline/network.md b/docs/pipeline/network.md new file mode 100644 index 0000000..0998d4d --- /dev/null +++ b/docs/pipeline/network.md @@ -0,0 +1,481 @@ +# Network Stage Reference: Social Graph Generation and Topology Gating + +## Document Intent +This file is the deep reference for the `network` stage in Extropy. + +It is meant to be reusable team memory for: +- what `network` is supposed to do, +- how it should do it, +- why this stage exists in the pipeline, +- what it currently does in code, +- where current behavior diverges from intended quality goals, +- and what backpressure checks this stage should support. + +This is not a patch list. It is a contract + current-state reality map. + +## Status Update (2026-02-23) +- Network generation now runs an adaptive multi-stage calibration loop with explicit topology gate bounds. +- Topology gate evaluation now uses final graph metrics after rewiring, with pre-rewire fallback if rewiring degrades an otherwise gate-valid graph. +- Config generation is LLM-based by default, with deterministic config-source priority and automatic scenario-local config discovery. +- Structural attribute role mapping is now deterministic-first with targeted LLM tie-break only for ambiguous roles. +- Structural edge generation supports partner/household/coworker/neighbor/congregation/school-parent channels with deterministic per-channel quotas and caps. +- Checkpoint/resume for similarity and calibration progress is DB-backed (`study.db`), with signature compatibility checks. +- Strict topology-gate failures can be quarantined under a suffixed network id. +- Network command supports quality profiles (`fast|balanced|strict`) and candidate modes (`exact|blocked`) with resource auto-tuning. + +--- + +## How To Read This Document +Use this order: +1. `Intended Contract` for target behavior. +2. `Current Implementation (As-Is)` for actual code behavior. +3. `Divergences, Mistakes, and Risk Areas` for the quality gap. +4. `Dependency Map` for downstream coupling (`simulate`, `results`, `query`). + +This file is intentionally verbose so a new teammate can recover context without prior chat history. + +--- + +## Why This Stage Exists +`sample` produces agents. `network` produces relationships. + +Without `network`, simulation has isolated individuals and cannot represent: +- exposure through social ties, +- peer influence asymmetry, +- neighborhood/workplace/religious clustering, +- bridge ties across communities, +- conversation and propagation pathways. + +Design principle: +- **Use sampled attributes to instantiate realistic social structure, then gate topology quality before simulation.** + +--- + +## Pipeline Position +Intended high-level flow: +1. `spec`: compile reusable base population mechanics. +2. `scenario`: compile event context + extension attributes. +3. `persona`: compile language rendering contract. +4. `sample`: instantiate concrete agents. +5. `network`: generate social graph over sampled agents. +6. `simulate`: propagate and reason over that graph. + +`network` is the structural bridge from agent rows to social dynamics. + +--- + +## Intended Contract + +### What `network` should do +`network` should generate a scenario-scoped social graph that is: +- realistic enough for downstream diffusion and conversation, +- topology-checked against explicit bounds, +- reproducible under seed and config, +- inspectable via run metadata and calibration traces. + +At minimum it should: +- resolve or generate a network config from known inputs, +- compute relationship probabilities from attribute similarity and role structure, +- add deterministic structural ties where semantically justified, +- calibrate graph shape toward target degree/clustering/modularity/connectivity, +- persist graph + diagnostics to canonical study storage. + +### What `network` should not do +`network` should not: +- mutate sampled agent attributes, +- invent scenario-level semantics not present in upstream artifacts, +- silently skip topology quality checks while claiming success, +- depend on hidden magic defaults without exposing metadata. + +### How `network` should do it +Intended execution model: +1. Load sampled agents for scenario. +2. Resolve network config (file or generated). +3. Compute sparse similarity candidates. +4. Build structural edges and similarity edges. +5. Calibrate network topology to targets. +6. Apply controlled rewiring. +7. Evaluate topology gate and persist outcomes. + +### Why this contract matters +If network quality is weak: +- spread realism collapses, +- conversation targets become noisy, +- identity clustering effects are distorted, +- simulation output can look plausible but be structurally wrong. + +`network` is therefore a quality gate, not just an edge sampler. + +--- + +## Conceptual Model Of Network Generation + +### Dual-source edge construction +Current design combines: +1. **Similarity-driven edges** (homophily + degree effects). +2. **Structural edges** (partner/household/workplace/neighborhood/congregation/school-parent). + +This avoids two extremes: +- pure random/flat graphs, +- over-hardcoded deterministic graphs. + +### Calibration-first topology shaping +Graph generation is not single-pass final. +It calibrates intra/inter-community scaling with restarts and iterations, then evaluates gate bounds. + +### Controlled rewiring as optimization pass +Rewiring is applied after calibration for small-world properties, but should not be allowed to invalidate a graph that already passed the gate. + +### Bounded quality semantics +Quality is represented via explicit bounds and deltas: +- average degree range, +- minimum clustering, +- modularity range, +- minimum largest connected component ratio, +- minimum edge floor. + +--- + +## Intended Ownership Boundary + +### `sample` should own +- concrete agent attribute values, +- household/partner/dependent realization, +- semantic validity of agent records. + +### `network` should own +- edge existence, edge type, edge weight, influence asymmetry, +- structural vs similarity composition, +- topology quality gating + diagnostics. + +### `scenario` should own +- scenario semantics and extension attributes, +- optional intent that may influence network config generation. + +### `simulate` should own +- runtime propagation and reasoning behavior over the saved graph. + +--- + +## Intended Artifact Contract + +### Primary runtime outputs +`network` should emit: +- edge list (source, target, type, weights, influence weights), +- network generation metadata (seed, candidate mode, quality diagnostics), +- optional computed network/node metrics. + +### Canonical persistence contract +Current DB entities used by network stage: +- `network_runs` +- `network_edges` +- `network_metrics` (optional) +- `network_generation_status` +- `network_calibration_runs` +- `network_calibration_iterations` +- `network_similarity_jobs` +- `network_similarity_chunks` +- `network_similarity_pairs` + +### Determinism contract +For fixed `(agents, config, seed)`: +- candidate selection and calibration are seeded, +- structural edge generation is deterministic under seed, +- final topology still depends on configured calibration limits and stage escalation. + +--- + +## Intended Quality Contract + +### Realism +Graph should reflect plausible social structure: +- in-group clustering where relevant, +- bridge ties across groups, +- role-weighted hubs without pathological concentration. + +### Internal consistency +Edges should be coherent with agent data: +- structural ties grounded in household/role fields, +- edge type rules referencing existing attributes, +- influence weights consistent with configured factors. + +### Downstream support reliability +Output should support: +- propagation rules using edge properties, +- conversation selection over realistic neighbor sets, +- scenario-specific simulation at target scale. + +### Backpressure intent +Network stage should expose machine-readable go/no-go signals before simulation. + +--- + +## Current Implementation (As-Is) + +This section describes actual behavior in code today. + +### CLI command flow +`extropy network` currently performs: +1. resolve study + scenario, +2. pre-flight check that sampled agents exist for scenario, +3. load scenario spec and base population reference, +4. merge base + extension attributes for config-generation context, +5. resolve config source, +6. apply CLI overrides + quality/resource defaults, +7. generate network (with or without metrics), +8. evaluate quality gate status, +9. persist network result (canonical/quarantine behavior), +10. optionally export JSON. + +Primary file: +- `extropy/cli/commands/network.py` + +### Config source resolution order +Config resolution is deterministic: +1. explicit `--network-config` +2. auto-detected latest `scenario//*.network-config.yaml` +3. LLM generation (default `--generate-config`) +4. empty config fallback (flat network warning) + +Generated configs are auto-saved by default to scenario directory unless disabled with hidden flag. + +### LLM config generation behavior +`generate_network_config(...)` runs: +1. core config call (weights, multipliers, edge rules, influence, avg degree), +2. structural-role mapping call only when deterministic resolver is ambiguous. + +Prompt context includes: +- population description + geography, +- up to first 50 merged attributes, +- up to first 5 sampled agents as examples. + +Primary file: +- `extropy/population/network/config_generator.py` + +### Structural-role mapping behavior +Role slots: +- `household_id`, `partner_id`, `age`, `sector`, `region`, `urbanicity`, `religion`, `dependents` + +Behavior: +- deterministic scoring picks high-confidence roles directly, +- ambiguous roles use constrained LLM tie-break against candidate list, +- runtime key resolution uses config-selected role first, then minimal canonical fallback keys. + +Primary files: +- `extropy/population/network/config_generator.py` +- `extropy/population/network/generator.py` + +### Similarity and candidate behavior +Supports candidate modes: +- `exact`: all-pairs candidate space +- `blocked`: block-pruned candidate space with deterministic bridge quota + +Blocked mode: +- auto-selects blocking attributes from highest weighted exact/within_n attributes, +- filters over-fragmented blockers by cardinality ratio, +- expands candidate pools when coverage is sparse. + +Similarity computation: +- sparse retention threshold via `similarity_store_threshold`, +- serial or process-parallel execution, +- optional checkpoint/resume via study DB similarity job tables. + +Primary files: +- `extropy/population/network/similarity.py` +- `extropy/population/network/generator.py` + +### Calibration ladder behavior +Generation runs staged candidate ladders: +- exact mode: single stage, +- blocked mode: staged escalation (`blocked`, `blocked-expanded`, and for non-fast profiles `hybrid-dense`). + +Per stage: +- compute similarities + coverage diagnostics, +- assign communities, +- create structural edges with budget headroom, +- calibrate intra/inter community scaling across restarts and iterations, +- score against gate bounds. + +Best stage/iteration is retained. + +### Structural edge behavior +Deterministic structural channels currently implemented: +- mandatory: `partner`, `household` +- optional budgeted: `coworker`, `neighbor`, `congregation`, `school_parent` + +Channel behavior includes local caps and quotas. + +### Rewiring and final gate behavior +After choosing best calibrated graph: +- rewiring pass runs (skips protected structural pairs), +- topology gate evaluated pre- and post-rewire, +- if rewiring breaks a previously passing graph, pre-rewire graph is restored. + +Quality metadata records: +- gate acceptance, +- best/final metrics, +- bounds, +- deltas, +- stage summaries. + +### Metrics behavior +Without `--no-metrics`, command computes: +- network metrics (avg degree, clustering, path length, modularity, etc.), +- node metrics (PageRank, betweenness, cluster id, echo chamber score). + +Metrics require `networkx`. + +Primary file: +- `extropy/population/network/metrics.py` + +### Save behavior and strict gate handling +Canonical network id defaults to scenario name. + +Strict failure condition in command: +- topology gate is strict, +- quality accepted is false, +- agent count >= 50. + +Then: +- with quarantine allowed: saves under suffixed network id and exits non-zero, +- without quarantine: exits non-zero. + +Persistence helper sets `meta.outcome` as: +- `accepted`, +- `accepted_with_warnings`, +- `rejected`, +- `rejected_quarantined`. + +Primary file: +- `extropy/cli/commands/network.py` + +### Runtime coupling with simulation +Simulation loader resolves network by scenario name first, then scenario meta fallback id. +Propagation/conversation scoring uses edge type and edge weight in runtime logic. + +Primary files: +- `extropy/simulation/engine.py` +- `extropy/simulation/propagation.py` + +--- + +## What Current Implementation Already Gets Right + +### Strong topology-aware generation core +Adaptive calibration + bounds-based gating is materially stronger than single-pass random graph synthesis. + +### Checkpoint/resume observability +Long runs expose status and calibration traces in DB tables, supporting inspectability and partial recovery. + +### Structural + similarity blend +Combining household/partner structure with similarity-based edges gives better realism than either source alone. + +### Rewire safety fallback +Pre-rewire fallback avoids accidental quality regression from late optimization passes. + +--- + +## Divergences, Mistakes, and Risk Areas + +### 1) Config generation evidence is thin +LLM config prompt uses at most first 5 agents and truncated attribute list context. +This can underrepresent long-tail population structure for large/narrow populations. + +### 2) Scenario intent bridge is weak +Network config generation is primarily population-driven. +Scenario dynamic intent (channels/spread pressure) is not a first-class input to config generation. + +### 3) Structural channel set is fixed +Structural edge channels are currently fixed templates. +This is practical, but can miss context-specific relationship modes for some populations. + +### 4) Structural semantics still rely on key conventions +Role mapping improved, but fallback still depends on canonical key names when config roles are missing/unpopulated. + +### 5) Strict-failure save semantics are operationally subtle +Command-level strict failure handling and persistence ordering create edge cases that can be surprising in workflows. +Team should treat strict-fail artifacts carefully when deciding what is canonical. + +### 6) Advanced tuning knobs are mostly hidden +Important controls exist (`topology_gate`, calibration budgets, quarantine toggles) but are hidden flags. +This can reduce discoverability during debugging. + +### 7) Networkx dependency bifurcation +Core generation does not require full metrics, but metric paths do. +Tooling/CI environments without `networkx` can see behavior differences under `--no-metrics` vs default. + +--- + +## Dependency Map (What `network` Must Support) + +### `simulate` +Needs: +- robust edge set for scenario, +- meaningful edge types and influence weights, +- stable network_id resolution path. + +### `results` and `query` +Need: +- persisted network edges and metadata, +- calibration/status records for diagnostics, +- optional network metrics for analysis. + +### `chat` and agent-level introspection +Indirectly depend on simulation dynamics that are shaped by network structure. + +### `estimate` +Does not directly depend on network internals, but simulation runtime quality/cost behavior is affected by network density and connectedness. + +--- + +## Working Invariants Draft (Network-Oriented) + +### Core invariants +1. Scenario must have sampled agents before network stage runs. +2. Every saved edge must reference valid source/target agent ids. +3. Undirected edge uniqueness must be preserved (no duplicate pair rows). +4. Structural protected edges must not be rewired. + +### Config invariants +1. Attribute references in weights/multipliers/rules/factors must exist in merged schema. +2. `within_n` match type must carry ordinal level mapping. +3. Candidate mode and topology gate enums must be valid. + +### Quality invariants +1. Gate bounds and deltas must be present in saved quality metadata. +2. Strict gate failures should never silently report success. +3. Final accepted graph should correspond to the same metric set used for pass/fail. + +--- + +## Glossary (Working Terms) +- **Candidate mode**: strategy for similarity pair search (`exact` vs `blocked`). +- **Blocked mode**: candidate pruning using shared block attributes plus deterministic global bridging. +- **Structural edges**: deterministic relation edges from known household/workplace/neighborhood-style roles. +- **Calibration ladder**: staged escalation process to improve similarity coverage and topology fit. +- **Topology gate**: pass/fail check against configured graph metric bounds. +- **Quarantine network**: strict-failed artifact saved under suffixed `network_id` for inspection. + +--- + +## Practical Readout +Current network stage is best understood as: +1. a calibrated graph generator (not one-shot), +2. with mixed deterministic + model-driven configuration, +3. with strong quality metadata and run traceability, +4. but still carrying some fixed structural assumptions and intent-bridge gaps. + +For team operation, key framing is: +- generation quality is mostly controlled by config quality + candidate coverage + calibration budgets, +- strict gate outcomes should be treated as hard backpressure before simulation. + +--- + +## Final Statement +`network` should convert sampled populations into topology-credible social structure and expose clear go/no-go signals for simulation. + +Current implementation already has substantial quality machinery. +The main remaining challenge is contract coherence: +- stronger scenario-intent coupling, +- clearer canonical vs quarantined persistence semantics, +- and predictable structural-role grounding across diverse populations. diff --git a/docs/pipeline/persona.md b/docs/pipeline/persona.md index 007ed45..6626829 100644 --- a/docs/pipeline/persona.md +++ b/docs/pipeline/persona.md @@ -12,7 +12,7 @@ It is designed to be reusable team memory for: This is not a patch plan. It is a contract + current-state map. -## Status Update (2026-02-20) +## Status Update (2026-02-23) - `extropy validate` now routes persona files (`persona.vN.yaml`) through persona-specific validation. - Persona validation now checks: - merged-attribute treatment coverage @@ -23,6 +23,7 @@ This is not a patch plan. It is a contract + current-state map. - mixed relative+concrete fallback for float-like relative attributes - strict skip of duplicate “Who I Am” group rendering - deterministic boolean phrase sanitation for token-like outputs +- `validate` supports versioned invalid persona artifacts (`persona.vN.invalid.vK.yaml`) and promotes them to canonical path when validation passes. - Fail-hard persona generation/validation now emits versioned invalid artifacts. --- @@ -280,7 +281,11 @@ Treatment/group assignment is prompt-driven and parsed into: - `AttributeTreatment`, - `AttributeGroup`. -Current code does not run a dedicated cross-field validator that enforces strict one-to-one treatment/group coverage guarantees beyond what generator outputs. +Current code runs dedicated cross-field validation (`validate_persona_config`) for: +- exact treatment coverage over merged attributes, +- group membership completeness and uniqueness, +- unknown attribute references in groups, +- intro-template placeholder reference validity. ### Stats behavior in code Persona command tries to load sampled agents from study DB: @@ -391,19 +396,17 @@ Runtime path can foreground decision-relevant attributes from scenario outcomes. ### 1) `--preview` option is currently not functionally wired CLI exposes `--preview/--no-preview`, but current command flow does not implement a distinct preview gate behavior tied to that flag in generation path. -### 2) No dedicated persona validation command/path -There is no first-class persona validator analogous to spec/scenario validators. -Quality assurance is mostly schema parsing + generator-local checks. +### 2) Phrase realism remains mostly prompt-driven +Structural validation is strong, but naturalness/readability quality (beyond token-like checks) still depends on prompt outputs. +There is no strict machine score for "human-like narrative quality." -### 3) Uneven enforcement across phrasing types +### 3) Uneven generation-time enforcement across phrasing types Categorical has explicit retry/coverage validation. Other phrasing classes rely more on one-shot generation and schema shape. -### 4) Treatment/group coverage is prompt-heavy -Current flow does not centrally enforce strict invariants such as: -- every merged attribute appears exactly once in treatments, -- every treated attribute appears in exactly one group, -- no unknown attributes in groups. +### 4) Validation exists, but generation can still fail late +Coverage issues are caught before save, but they are still discovered post-generation. +This means failed generations can consume LLM calls before deterministic checks reject output. ### 5) Operational ordering rigidity `sample` requires persona config as a prerequisite even though persona stats can be backfilled later. @@ -429,13 +432,9 @@ Backpressure for "sounds natural, not templated token noise" is not formalized a ## How It Can Be Made Better (Direction, Not Patch Plan) -### Add contract-level persona validation -Establish explicit checks for: -- treatment coverage, -- group coverage, -- phrasing completeness, -- unknown attribute references, -- duplicate mappings. +### Keep contract-level persona validation as hard gate +Current explicit checks (coverage, group uniqueness, phrasing completeness, intro references) +should remain non-optional in create and validate flows. ### Formalize quality bands for phrase realism Add machine-checkable heuristics for low-quality phrasing patterns (raw token leakage, template artifacts). @@ -514,9 +513,9 @@ Current persona stage is directionally strong: - renderer is deterministic, - simulation integration is substantial. -Main debt is contract enforcement completeness: -- strong guardrails for categorical coverage, -- weaker explicit invariants for full config coverage and cross-field consistency. +Main debt is language-quality enforcement completeness: +- structural invariants are now strongly enforced, +- realism/naturalness scoring remains mostly heuristic and prompt-dependent. Given Extropy goals (realistic population + scenario + persona + network + simulation), persona is a high-leverage quality gate and should be treated as such in backpressure design. diff --git a/docs/pipeline/sample.md b/docs/pipeline/sample.md index ae5dec6..b9af42f 100644 --- a/docs/pipeline/sample.md +++ b/docs/pipeline/sample.md @@ -13,7 +13,7 @@ It is meant to be reusable team memory for: This is not a patch plan. It is a contract + current-state reality map. -## Status Update (2026-02-20) +## Status Update (2026-02-23) - Merged sampling order now recomputes topologically across base + extension attributes. - Post-household reconciliation now deterministically aligns: - marital/partner coherence @@ -209,26 +209,32 @@ This section describes actual behavior in code today. 3. load scenario YAML, 4. load base population referenced by `scenario.meta.base_population`/`population_spec`, 5. merge `extended_attributes` onto base attributes, -6. build merged sampling order by appending missing extension names to base order, +6. build merged sampling order with deterministic topological sort over merged dependencies, 7. validate merged spec via `validate_spec` (unless `--skip-validation`), -8. call `sample_population(...)` with scenario `household_config` + `agent_focus_mode`, -9. save result to `study.db`. +8. optional strict pre-sample gate (`--strict-gates`) that promotes high-risk warnings, +9. call `sample_population(...)` with scenario `household_config` + `agent_focus_mode` + `sampling_semantic_roles`, +10. apply post-sampling deterministic rule-pack gate (impossible/implausible), +11. save result to `study.db`. Primary file: - `extropy/cli/commands/sample.py` ### Merge behavior in command -Current merge is simple list merge: +Current merge is: - `merged_attributes = base_attributes + extended_attributes` -- `merged_sampling_order = base_order + [extension names not already present]` +- `merged_sampling_order = topological_sort(merged_dependencies)` -No merged topological recomputation is performed in this command. +If merged dependencies are cyclic, command fails before sampling and writes versioned invalid JSON diagnostics. ### Validation gate behavior Validation uses population validator (structural errors + semantic warnings): - structural failures block sampling unless `--skip-validation`. - warnings do not block. +Strict mode behavior (`--strict-gates`): +- pre-sample: promotes warning categories `CONDITION_VALUE` and `MODIFIER_OVERLAP` to hard block, +- post-sample: fails if `condition_warnings` are present. + Primary files: - `extropy/population/validator/spec.py` - `extropy/population/validator/structural.py` @@ -261,10 +267,10 @@ Primary files: ### Modifier behavior (actual) - Numeric distributions: all matching modifiers stack (`multiply` product + `add` sum), then reclamp to min/max bounds. -- Categorical: last matching `weight_overrides` wins. -- Boolean: last matching `probability_override` wins; otherwise multiply/add transforms base probability, then clamp `[0,1]`. +- Categorical: one deterministic winner is selected via precedence logic (subset/specificity/order), then that rule's `weight_overrides` apply. +- Boolean: one deterministic winner is selected; apply `probability_override` or winner multiply/add transform, then clamp `[0,1]`. -Modifier condition evaluation failures are logged and ignored for that modifier. +Modifier condition evaluation failures are logged and surfaced into `stats.condition_warnings` (capped to avoid unbounded growth). ### Type coercion behavior Sampler coerces sampled values to declared `attr.type`: @@ -280,6 +286,7 @@ Current runtime constraint handling is split: - `spec_expression` constraints are not run against agents (treated as spec-level only). Expression check exceptions are swallowed for the affected constraint evaluation. +CLI rule-pack treats non-zero expression-constraint violations as impossible and fail-hard. ### Household sampling behavior When household mode is active: @@ -305,11 +312,10 @@ Promoted dependent agents (minors) undergo normalization using attribute `semant This is deterministic rule-based normalization in sampler core. ### Partner correlation behavior -For `scope="partner_correlated"`, partner value generation uses: -- special case `age`: gaussian age-gap logic from `HouseholdConfig`. -- special case `race/ethnicity`: same-group rates. -- special case `country`: same-country rate. -- fallback: assortative-mating table lookup or default same-group rate. +For correlated partner fields, runtime uses policy resolution: +- explicit scenario semantic-role override (`sampling_semantic_roles.partner_correlation_roles`) if present, +- otherwise semantic/identity/default policy resolution (`gaussian_offset`, `same_group_rate`, `same_country_rate`, `same_value_probability`), +- then deterministic fallback rates from `HouseholdConfig`. ### Name generation behavior Names are generated during sampling, not in `spec`/`scenario` writing. @@ -330,9 +336,11 @@ Sampler collects: - categorical counts, - boolean counts, - modifier trigger counts, -- expression constraint violation counts. +- expression constraint violation counts, +- condition-evaluation warnings, +- household reconciliation counters. -Model also has `condition_warnings`, but sampler currently does not populate it. +CLI post-processing adds rule-pack summary (`pass/warn/fail`) into sampling stats for machine-readable backpressure. Primary file: - `extropy/core/models/sampling.py` @@ -402,61 +410,43 @@ Agent-level expression violations are counted and exposed in stats (not silently ## Divergences, Mistakes, and Risk Areas -### 1) Merged sampling order is append-based, not recomputed -The command appends extension attributes to base sampling order instead of recomputing full merged topological order. - -Risk: -- extension-to-extension dependency order can be fragile if extension list order is not dependency-safe. - -### 2) Scenario binder output `sampling_order` is not persisted/used by `sample` -Scenario creation computes extension sampling order via binder, but `sample` does not consume a dedicated extension order artifact. - -Risk: -- compile-time dependency resolution work may be partially dropped at execution boundary. - -### 3) Household activation tied only to `scope="household"` +### 1) Household activation tied only to `scope="household"` Sampler switches to household mode only if at least one household-scoped attribute exists. Risk: - scenario may provide household config/focus mode but not trigger household mode if scope annotations are missing or inconsistent. -### 4) Persona is required as pre-flight for `sample` +### 2) Persona is required as pre-flight for `sample` Current command requires persona config before sampling even though sampler core does not depend on persona content. Risk: - operational coupling can block sampling workflows unrelated to sampler logic. -### 5) Name field shape mismatch risk in NPC partner usage +### 3) Name field shape mismatch risk in NPC partner usage Sampler writes NPC partner fields with `first_name`/`last_name` style semantics, while parts of simulation conversation/contact logic look for `partner_npc.name` in several paths. Risk: - household NPC addressing and partner reference resolution can degrade. -### 6) Partner key mismatch risk in simulation contact paths +### 4) Partner key mismatch risk in simulation contact paths Sampler uses `partner_id`; parts of simulation contact/conversation logic also check `partner_agent_id`. Risk: - some partner-as-agent references may not resolve uniformly. -### 7) Condition warning channel is modeled but unused -`SamplingStats.condition_warnings` exists in model but is not currently populated by sampler. - -Risk: -- reduced observability when modifier conditions fail frequently or unexpectedly. - -### 8) Expression constraint failures are counted post-hoc, not gating -Expression constraints currently contribute diagnostics but do not block run completion. +### 5) Expression constraint failures are counted post-hoc, not repaired +Expression constraints are checked after sampling and can fail-hard via rule-pack. Risk: -- samples with significant inconsistency may proceed downstream unless external gates inspect stats. +- impossible violations fail via rule-pack gate, but sampler does not attempt automated repair/resample before failing. -### 9) Type coercion can mask upstream categorical/schema drift +### 6) Type coercion can mask upstream categorical/schema drift Aggressive coercion (e.g., stripping symbols from numeric-like strings) keeps sampling alive. Risk: - upstream naming/schema mismatches may appear to "work" while distorting semantics. -### 10) Persistence identity uses backward-compat population keying +### 7) Persistence identity uses backward-compat population keying Sample saves population spec under `population_id=scenario_name` backward-compat behavior. Risk: @@ -467,7 +457,8 @@ Risk: ## How It Can Be Made Better (Direction, Not Patch Plan) ### Promote merged-order contract as first-class artifact -Make merged dependency-safe order explicit and enforced at sample runtime. +Merged dependency-safe order is now explicit and enforced at sample runtime. +Remaining improvement is surfacing richer diagnostics when dependency-quality is weak upstream. ### Strengthen mismatch diagnostics Expose dedicated machine-readable warnings for token/key mismatches and condition failures. diff --git a/docs/pipeline/scenario.md b/docs/pipeline/scenario.md index 6f0d9b3..ddc6bca 100644 --- a/docs/pipeline/scenario.md +++ b/docs/pipeline/scenario.md @@ -12,7 +12,7 @@ Its job is to make one thing unambiguous for people and agents working on the pi This is not a patch list. It is a stage contract + current-state reality map. -## Status Update (2026-02-20) +## Status Update (2026-02-23) - Scenario creation now enforces non-empty `extended_attributes` as a hard contract. - Scenario validation in create-flow is now real validation (not light-pass placeholder). - Attribute reference validation uses merged namespace (`base population ∪ scenario extension`). @@ -20,8 +20,12 @@ This is not a patch list. It is a stage contract + current-state reality map. - `seed_exposure.rules` - `spread.share_modifiers` - `timeline[*].exposure_rules` +- Sufficiency post-processing now enforces timestep clarity: + - timeline markers (for example `week 1`, `month 0`) force `evolving`, + - static scenarios must provide an explicit timestep unit (or ask clarification). - Household/focus coherence checks are enforced for household-active scenarios. - Fail-hard scenario paths now emit versioned invalid artifacts (`*.invalid.vN.yaml` and JSON invalid diagnostics where applicable). +- Agent-mode insufficiency now returns structured clarification payloads (exit code 2) instead of falling through generic validation errors. --- @@ -264,8 +268,8 @@ This section describes actual code behavior now. 3. extension hydration (`hydrate_attributes`), 4. extension binding (`bind_constraints`), 5. scenario config generation (`create_scenario_spec`), -6. post-assembly field assignment for extension+household+focus mode, -7. save. +6. metadata finalization (`meta.name`, `meta.base_population`) + re-validation, +7. save valid artifact or emit versioned invalid artifact and exit non-zero. Primary orchestration file: - `extropy/cli/commands/scenario.py` @@ -281,7 +285,15 @@ Sufficiency model checks: Current behavior characteristics: - lenient sufficiency style, - user clarification loop supported, -- inferred fields are mostly advisory for messaging except `agent_focus_mode` which is persisted after compile. +- deterministic post-processing layers over LLM output: + - extracts unit hints from description and inferred duration, + - promotes explicit timeline markers to evolving mode, + - blocks static scenarios without explicit timestep unit by issuing a clarification question, +- inferred fields are mostly advisory for messaging except `agent_focus_mode` and unit hints that feed compile defaults. + +In agent mode: +- insufficiency returns structured clarification questions with exit code 2 (`needs_clarification`), +- `--use-defaults` attempts a no-prompt re-check by applying question defaults. Primary file: - `extropy/scenario/sufficiency.py` @@ -291,7 +303,6 @@ Scenario uses spec-builder primitives directly: - attribute selector in overlay mode with base attributes as context, - split hydrator pipeline (independent/derived/conditional/base+modifiers), - household config hydration (conditional), -- name config hydration, - dependency binding with expression-based dependency inference. Primary files: @@ -330,6 +341,13 @@ Scenario creation-time generation passes `network_summary=None` (no synthetic pl `extropy scenario` checks validation errors before save. On fail-hard paths, it writes a versioned `.invalid` artifact and exits non-zero. +Current validator focus areas include: +- merged-namespace attribute reference checks (base + extension), +- expression syntax checks for `when` rules, +- literal compatibility checks against categorical options and boolean literals, +- channel/timestep/outcome consistency checks, +- household + `agent_focus_mode` coherence when household semantics are active. + ### Assembly behavior for extension/household fields `extended_attributes`, `household_config`, and `agent_focus_mode` are passed into `create_scenario_spec` and emitted as part of the assembled `ScenarioSpec` contract. @@ -368,7 +386,7 @@ This is one of the stronger semantic links between scenario and simulation. `sample` does: - load base population from scenario metadata, - merge `extended_attributes` into attributes list, -- append extended attribute names to base sampling order if missing, +- recompute merged sampling order with topological sort over merged dependencies, - pass scenario `household_config` and `agent_focus_mode` into sampler. Primary file: @@ -476,9 +494,9 @@ Future quality gains are mostly about adding richer rule coverage, not wiring. Model supports fields like `experience_template` and `extraction_instructions`, but default generation often leaves them null. This is schema richness without guaranteed population of semantically useful hooks. -### 6) Sampling order merge semantics are simplistic downstream -Sample merges extension by appending missing names to base order. -This can work, but does not guarantee a fully recomputed dependency-optimal merged order contract at this stage boundary. +### 6) Merged-order correctness now depends on dependency quality +Downstream now recomputes merged order with topological sort. +Primary remaining risk is dependency quality from generated attributes (missing/wrong `depends_on`), not append-only ordering. ### 7) Network intent contract is implicit Scenario has spread/exposure semantics, but network config generation is primarily population-driven. diff --git a/docs/pipeline/simulate.md b/docs/pipeline/simulate.md new file mode 100644 index 0000000..c22f3b8 --- /dev/null +++ b/docs/pipeline/simulate.md @@ -0,0 +1,475 @@ +# Simulate Stage Reference: Runtime Dynamics and Outcome Realization + +## Document Intent +This file is the deep reference for the `simulate` stage in Extropy. + +It is meant to be reusable team memory for: +- what `simulate` is supposed to do, +- how it should do it, +- why this stage exists in the pipeline, +- what it currently does in code, +- where current behavior diverges from intended quality goals, +- and what runtime backpressure checks this stage should support. + +This is not a patch plan. It is a contract + current-state reality map. + +## Status Update (2026-02-23) +- Simulation is now scenario-centric in lookup order (`scenario_id` first, legacy IDs as fallback). +- Runtime loop supports DB-backed resume/checkpoint behavior at timestep and chunk levels. +- Timeline events support provenance epochs (`info_epoch`) and forced re-reasoning escalation. +- Reasoning supports two modes: + - default two-pass (role-play + classification), + - optional merged single-pass (`--merged-pass`). +- Fidelity modes actively control conversation behavior: + - `low`: no conversations, + - `medium`: bounded conversations after chunks, + - `high`: deeper conversations + extra public-text classification. +- Early convergence behavior is now explicit and overrideable (`auto|on|off`). +- Runtime exports include `by_timestep.json`, `meta.json`, and conditional conversation/social artifacts. +- State manager now auto-upgrades legacy simulation tables/columns (including timeline-provenance exposure fields) on startup. + +--- + +## How To Read This Document +Use this order: +1. `Intended Contract` for target behavior. +2. `Current Implementation (As-Is)` for code truth. +3. `Divergences, Mistakes, and Risk Areas` for the quality gap. +4. `Dependency Map` for upstream/downstream coupling. + +This file is intentionally verbose so a new teammate can recover context without prior chat history. + +--- + +## Why This Stage Exists +All earlier pipeline stages compile structure: +- who exists (`spec` + `scenario` + `sample`), +- how they are connected (`network`), +- how they speak/think (`persona`). + +`simulate` is where those compiled artifacts are executed over time. + +Without `simulate`, Extropy has static data but no dynamics: +- no temporal exposure flow, +- no opinion shifts, +- no sharing cascades, +- no conversation-mediated corrections, +- no run-level outcomes. + +Design principle: +- **Execute deterministic state transitions around bounded LLM reasoning, then persist full run traceability.** + +--- + +## Pipeline Position +Intended high-level flow: +1. `spec`: base population contract. +2. `scenario`: event + extension contract. +3. `persona`: rendering contract. +4. `sample`: concrete agents. +5. `network`: concrete graph. +6. `simulate`: dynamic execution over timesteps. +7. `results/query/chat`: interpretation and retrieval. + +`simulate` is the first stage that mutates dynamic state over time. + +--- + +## Intended Contract + +### What `simulate` should do +`simulate` should produce a reproducible run that: +- applies scenario exposure dynamics over timesteps, +- reasons for the right agents at the right moments, +- propagates through network and conversation channels, +- updates public/private agent states coherently, +- persists enough detail for debugging, analysis, and replay. + +At minimum it should: +- validate prerequisites (scenario, agents, network, persona), +- run timestep loop with clear phase ordering, +- enforce deterministic gating around LLM calls, +- support bounded resume/recovery behavior, +- output machine-usable artifacts and run metadata. + +### What `simulate` should not do +`simulate` should not: +- silently fabricate missing prerequisites, +- mutate upstream schema contracts, +- hide runtime failure causes, +- collapse all behavior to undifferentiated midpoint outputs, +- require manual table surgery for normal resume behavior. + +### How `simulate` should do it +Intended runtime model: +1. preflight + config resolution, +2. load scenario/population/agents/network/persona, +3. initialize run scope + state store, +4. for each timestep: exposures -> reasoning -> conversation -> summary, +5. evaluate stopping conditions, +6. finalize aggregates and export artifacts. + +### Why this contract matters +Simulation quality is where trust is won or lost. +Weak runtime controls produce plausible-looking but analytically unstable outputs. + +`simulate` is therefore not just “run models”; it is the reliability envelope for behavioral dynamics. + +--- + +## Conceptual Model Of Runtime Dynamics + +### Exposure stack +Per timestep exposure can come from: +1. seed rules, +2. timeline events (if scheduled at this timestep), +3. network propagation from sharing agents. + +### Reasoning triggers +Agents reason when one of these holds: +- aware and never reasoned, +- forced by newer `info_epoch` timeline provenance, +- multi-touch threshold reached (unique source count) and not committed. + +### Public/private state split +Runtime distinguishes: +- public-facing expression (`public_position/sentiment/conviction`), +- private behavior state (`private_*`). + +High-conviction guardrails resist abrupt flips unless confidence/support criteria are met. + +### Conversation interleaving +For medium/high fidelity, conversation executes after reasoning chunks and can override provisional reasoning states. + +### Stop semantics +Run can stop via: +- max timesteps, +- explicit stop conditions, +- optional convergence/quiescence auto-stop depending on `allow_early_convergence` and future timeline events. + +--- + +## Intended Ownership Boundary + +### `simulate` should own +- runtime state transitions, +- timestep orchestration, +- reasoning/conversation execution policy, +- stop-condition evaluation, +- run persistence + exports. + +### `scenario` should own +- event/timeline definitions, +- exposure/spread/outcome schema, +- simulation horizon and stop condition intent. + +### `sample` + `network` should own +- initial node/edge data quality and coherence. + +### `results/query/chat` should own +- post-run interpretation and retrieval. + +--- + +## Intended Artifact Contract + +### Canonical run persistence (DB) +Simulation should persist: +- run lifecycle records (`simulation_runs`, checkpoints, metadata), +- mutable agent state tables, +- exposures, memory traces, timeline events, timestep summaries, +- conversations/social posts when enabled. + +### File exports (results dir) +Runtime should export: +- `by_timestep.json` +- `meta.json` +- optional `conversations.json` +- optional `social_posts.json` +- `elaborations.csv` + +### Determinism contract +For fixed `(scenario, sampled agents, network, seed, model behavior)`: +- non-LLM state transitions are deterministic, +- LLM calls are bounded by stable schemas/prompts, +- resume should be consistent with prior persisted checkpoints. + +--- + +## Intended Quality Contract + +### Realism +Dynamics should look socially plausible: +- propagation and re-reasoning should respond to new information and relationship context, +- conversation effects should be bounded and not chaotic. + +### Internal consistency +Public/private state evolution and sharing decisions should stay coherent with conviction, exposures, and scenario rules. + +### Downstream support reliability +Outputs should be directly consumable by: +- `results` timeline/summary views, +- `query` exports, +- `chat` grounded context. + +### Backpressure intent +Simulation stage should expose clear stop reason, runtime diagnostics, and failure modes that can be triaged without guesswork. + +--- + +## Current Implementation (As-Is) + +This section describes actual behavior in code today. + +### CLI command flow +`extropy simulate` currently: +1. resolves study + scenario, +2. preflight-checks sampled agents, network edges, persona config, +3. validates `--resume`/`--run-id`, resource mode, and early-convergence flag, +4. resolves effective models/rate settings from CLI > config, +5. starts simulation with optional live progress view, +6. returns summary data and writes outputs. + +Primary file: +- `extropy/cli/commands/simulate.py` + +### Preconditions enforced by command +Simulation hard-blocks if any are missing: +- scenario, +- sampled agents for scenario, +- network edges for scenario, +- persona config for scenario. + +### Early convergence override semantics +`--early-convergence` handling: +- `on`: force `allow_early_convergence=True`, +- `off`: force `allow_early_convergence=False`, +- `auto`: keep scenario value; if null, runtime auto-rule applies. + +Auto-rule: +- early convergence is disabled while future timeline events remain, +- enabled once no future timeline events remain. + +### Runtime setup (`run_simulation`) +`run_simulation(...)` currently: +1. loads scenario and applies early-convergence override if requested, +2. resolves base population from scenario metadata, +3. resolves canonical study DB and verifies existence, +4. loads agents and network (scenario key first, legacy fallback), +5. creates simulation run record, +6. clears runtime tables for this run when not resuming, +7. loads persona config (CLI path preferred), +8. backfills persona population stats when empty, +9. builds `SimulationRunConfig` and rate limiter, +10. executes engine and updates run status. + +Primary file: +- `extropy/simulation/engine.py` + +### Timestep execution order +Each timestep does: +1. mark timestep checkpoint started, +2. apply exposures: + - seed, + - timeline event (if this timestep has one), + - network propagation, +3. reason selected agents in chunks, +4. interleave conversations after chunks (medium/high fidelity), +5. record social posts from sharers, +6. apply conviction decay to non-reasoned agents, +7. compute and save timestep summary, +8. clear timestep checkpoint. + +### Timeline behavior +Timeline events only apply when `timeline_event.timestep == current_timestep`. +Non-event timesteps do not inject new world events; prior context remains via state/history. + +Timeline exposures carry: +- `info_epoch`, +- optional `re_reasoning_intensity` (`normal|high|extreme`). + +### Reasoning behavior +Default reasoning mode is two-pass: +1. Pass 1 role-play response (free text + sentiment/conviction/share/action fields). +2. Pass 2 outcome classification for categorical/boolean/float outcomes. + +Merged mode (`--merged-pass`) combines both in one schema/call. + +Important current behavior: +- open-ended outcomes are not classified in Pass 2; they remain in free-text reasoning. +- high fidelity adds separate public-text classification pass for public position. + +Primary file: +- `extropy/simulation/reasoning.py` + +### Agent selection for reasoning +`get_agents_to_reason(...)` includes: +- aware + never reasoned agents, +- forced re-reason agents with newer timeline info epoch, +- explicit forced IDs from timeline direct exposure paths, +- multi-touch candidates with enough unique source exposures since last reasoning and not committed. + +Committed agents are protected from routine multi-touch re-reasoning unless forced by timeline policy. + +### Conversation behavior by fidelity +- `low`: no conversations executed. +- `medium`: conversations enabled; per-agent cap 1 per timestep; 4-message conversations. +- `high`: per-agent cap 2 per timestep; 6-message conversations. + +Conversation outcomes can override provisional reasoning state. + +Primary file: +- `extropy/simulation/conversation.py` + +### Propagation behavior +Network propagation: +- only from current sharers (`will_share=True`), +- one-shot per source-target-position with reshare on position change, +- share probability from base spread probability + `share_modifiers` + hop decay + soft saturation. + +Modifier context includes edge fields: +- `edge_type` +- `edge_weight` + +Primary file: +- `extropy/simulation/propagation.py` + +### Stop condition behavior +Stop evaluation order: +1. `max_timesteps`, +2. explicit `stop_conditions` expressions, +3. convergence/quiescence auto-stop if early convergence is allowed. + +Convergence auto-stop currently uses stable position distribution window checks. +Quiescence auto-stop triggers on three timesteps with zero agents reasoned. + +Primary file: +- `extropy/simulation/stopping.py` + +### Resume and checkpoint behavior +Resume model: +- requires `--run-id`, +- engine resumes from `checkpoint_timestep` if present, else `last_completed + 1`, +- chunk checkpoints skip already-completed chunks within a timestep. + +When not resuming: +- runtime tables for that run ID are reset before execution. + +### Output artifacts +Simulation exports: +- `by_timestep.json` (timeline aggregates), +- `meta.json` (models, seed, cost, conversation stats, etc.), +- `conversations.json` (if conversations happened), +- `social_posts.json` (if posts exist), +- `elaborations.csv`. + +--- + +## What Current Implementation Already Gets Right + +### Strong persistence and resume scaffolding +Run metadata, timestep checkpoints, and chunk checkpoints make long runs recoverable. + +### Clear phase ordering +Exposure -> reasoning -> conversation -> summary sequencing is explicit and traceable. + +### Fidelity-aware behavior controls +Conversation workload and prompt richness scale with fidelity mode instead of one-size-fits-all. + +### Scenario-coupled temporal logic +Timeline event epochs and early-convergence auto rules reduce premature stopping in evolving scenarios. + +--- + +## Divergences, Mistakes, and Risk Areas + +### 1) `interaction` config is effectively informational +`scenario.interaction` fields are not currently consumed by simulation runtime behavior. + +### 2) Some scenario metadata remains non-operative in runtime control +Fields such as channel `reach` are not runtime control inputs in simulation propagation. + +### 3) Open-ended outcomes are not structurally extracted +Open-ended outcomes live in reasoning text, not in structured Pass 2 outcome payloads. + +### 4) Output retention semantics are split +`retention_lite` suppresses raw reasoning payload storage even if scenario outcome config requests full reasoning capture. + +### 5) Resume usability requires explicit run-id discipline +`--resume` requires `--run-id`; incorrect run-id usage can lead to user confusion about expected continuation behavior. + +### 6) Prompt/context size grows with memory and history +High-fidelity prompts include richer memory and context windows, which improves realism but increases token load and cost volatility. + +### 7) Legacy fallback paths still exist +Simulation still includes legacy lookup fallbacks (`population_id`, `network_id`) which can mask schema drift in older studies. + +--- + +## Dependency Map (What `simulate` Must Support) + +### Upstream dependencies +Needs valid outputs from: +- `scenario` (`scenario.vN.yaml`), +- `persona` (`persona.vN.yaml`), +- `sample` (agents in `study.db`), +- `network` (edges in `study.db`). + +### Downstream consumers +Feeds: +- `results` (summary/timeline/segment/agent views), +- `query` (states/summary/sql exports), +- `chat` (agent context over runs), +- external DS workflows (`elaborations.csv`, JSON exports). + +--- + +## Working Invariants Draft (Simulate-Oriented) + +### Preconditions +1. Scenario, persona, agents, and network must all exist before run starts. +2. Run metadata must be persisted with a stable run id before first timestep mutation. + +### Runtime invariants +1. Timestep checkpoint must be set before phase execution and cleared after completion. +2. Agent reasoning selection must be deterministic given state + thresholds + forced IDs. +3. Conversation overrides must be applied after reasoning chunk updates, not before. +4. Stop reason must be explicit when run ends before max timesteps. + +### Persistence invariants +1. Failed runs must be marked `failed` in `simulation_runs` with reason. +2. Completed/stopped runs must have final status and persisted summary artifacts. + +--- + +## Glossary (Working Terms) +- **Two-pass reasoning**: role-play generation + separate outcome classification. +- **Merged pass**: single-call schema combining role-play and classification. +- **Info epoch**: timeline provenance marker attached to exposures. +- **Committed agent**: agent with firm conviction protected from routine re-reasoning. +- **Quiescence**: no-agent-reasoned condition for early stop. +- **Retention lite**: mode that drops full raw reasoning payloads to reduce storage volume. + +--- + +## Practical Readout +Current simulate stage is best understood as: +1. a checkpointed runtime orchestrator over scenario dynamics, +2. with bounded LLM reasoning loops plus deterministic state logic, +3. with meaningful fidelity controls and timeline-aware stopping, +4. but still carrying some schema/runtime gaps where metadata is descriptive rather than operative. + +For team operation, key framing is: +- simulation quality depends as much on upstream spec/scenario/sample/network quality as on runtime prompts, +- run status, stop reason, and artifact completeness are first-line reliability signals. + +--- + +## Final Statement +`simulate` should convert static pipeline artifacts into temporally coherent behavioral dynamics while preserving clear runtime observability. + +Current implementation already has strong execution scaffolding. +The main remaining challenge is contract coherence: +- align descriptive scenario fields with actual runtime control, +- keep resume semantics predictable, +- and maintain realism without exploding token/cost overhead. diff --git a/docs/pipeline/spec.md b/docs/pipeline/spec.md index 6374842..df30c2f 100644 --- a/docs/pipeline/spec.md +++ b/docs/pipeline/spec.md @@ -13,7 +13,7 @@ If a team member or agent forgets prior context, this file should bring them bac This is not a patch plan and not a code-fix checklist. It is a contract + reality map. -## Status Update (2026-02-20) +## Status Update (2026-02-23) - Spec-stage hydration now runs with `include_household=False`. - Household config ownership is enforced at scenario stage; spec no longer does dead household hydration work. - Naming is Faker-first at sampling/runtime with deterministic seed and geography routing; CSV remains a safety fallback path. @@ -281,6 +281,9 @@ Current sufficiency stage: Current selector: - uses reasoning LLM call with a strict JSON schema, - asks model to assign strategy, scope, dependencies, and optional semantic metadata, +- canonicalizes returned attribute names to snake_case before model object creation, +- fails fast if two different raw names collapse to the same canonical key, +- coerces invalid strategy/dependency combos (`independent` + deps -> `conditional`; missing deps on conditional/derived -> `independent`), - injects Big Five traits when recommended and missing, - may inject `country` attribute for multi-country geographies. @@ -290,8 +293,9 @@ Hydration is split: - 2b derived formulas via reasoning call, - 2c conditional base distributions via agentic research, - 2d conditional modifiers via agentic research, -- 2e household config via agentic research (conditional trigger), -- 2f name config via agentic research (skips US population heuristic). +- 2e household config exists in shared hydrator but is explicitly disabled in `extropy spec` (`include_household=False`). + +There is no spec-stage name hydration call in the current flow. Name generation is runtime behavior in sampler/names modules. Hydration includes fail-fast validation callbacks and retry loops. @@ -314,6 +318,7 @@ Validation currently combines: - semantic checks (warnings). Validation catches many shape and compatibility issues, but does not fully guarantee behavioral realism quality. +When validation fails, `extropy spec` writes a versioned invalid artifact (`population.vN.invalid.vK.yaml`) and exits non-zero. --- @@ -326,15 +331,15 @@ Scenario flow currently performs additive extension: ### Current household ownership in runtime Practical runtime ownership today: -- base `spec` pipeline can research household config, -- `extropy spec` explicitly ignores/stubs household config for base output, +- shared hydrator has optional household research capability, +- `extropy spec` explicitly disables household hydration and does not store household config in base output, - scenario stores household config and agent focus mode, - sampler consumes scenario-level household config and focus mode. ### Current merge model before sampling `sample` currently: - merges base + extended attributes, -- appends extended names onto base sampling order if missing, +- recomputes merged sampling order using dependency-aware topological sort over merged attributes, - samples with scenario-provided household/focus settings. This is the effective behavior today. @@ -381,13 +386,13 @@ Risk: - resulting compiled behavior differs from model’s original intent without a hard failure. ### 4) Extension/sampling order assumptions -Extension merges are currently append-oriented in CLI flow. +Extension merge order is now recomputed with topological sort at sample time, but ordering semantics still depend on clean dependency declarations from upstream spec/scenario artifacts. Risk: - cross-layer dependency semantics can be fragile if extension expectations grow. ### 5) Household ownership ambiguity in build narrative -Build path can hydrate household config during base flow, but final base stage ignores it and runtime ownership shifts to scenario. +Shared hydrator internals still expose household research logic. Readers who only inspect hydrator code can misread ownership unless they also check stage wiring in CLI commands. Risk: - conceptual confusion across teams and agents unless boundary is explicit. diff --git a/docs/study-2-iran-strikes.md b/docs/study-2-iran-strikes.md new file mode 100644 index 0000000..9a14d17 --- /dev/null +++ b/docs/study-2-iran-strikes.md @@ -0,0 +1,371 @@ +# Study 2: US Strikes on Iran — Evolving Behavioral Simulation + +## Overview + +An evolving 12-week simulation of how the American population responds to a US military strike on Iran, modeled from the moment of the strike through economic cascade, military escalation, fragile ceasefire, and into the 2026 midterm election cycle. + +This study replaces the previously planned Taiwan scenario. The Iran crisis is not hypothetical — as of February 22, 2026, the US has amassed its largest Middle Eastern naval deployment since 2003, reports indicate strikes could come within days, and the situation is on a knife's edge between military action and a last-minute deal. Simulating the behavioral aftermath of strikes landing is immediately relevant, maximally viral, and activates nearly every demographic fault line in the national spec. + +**Why Iran is a stronger study than Taiwan:** + +- **Immediate relevance.** People are already anxious about this. The simulation lands in an active news cycle. +- **Richer fault lines.** Taiwan was primarily an economic/semiconductor story. Iran hits simultaneously: oil prices and gas at the pump, military families facing deployment, anti-war vs. hawkish splits within BOTH parties, Muslim-American communities, veterans who remember Iraq, energy sector workers, defense contractors, the protest/regime-change moral dimension. +- **Cross-study coherence.** The Iran crisis feeds directly into Study 4 (2026 midterms). The same 5,000 agents experience the Iran crisis AND then vote in November. The midterm simulation gains a concrete backdrop. +- **Every dimension in the existing spec is activated.** Military connection, political identity, media ecosystem, institutional trust, employment sector, financial margin, religious engagement, consumer behavior — all directly relevant. + +--- + +## Spec: National US Population (Spec A — Shared) + +This study uses the same national US base spec shared across Studies 1, 2, and 3. No dedicated spec is needed — the Iran scenario activates the existing dimensions comprehensively. + +### Spec Prompt + +```bash +extropy spec "Nationally representative US adult population (18-80). Must capture the +demographic, economic, and attitudinal fault lines that drive divergent responses to +major national events — especially technology disruption, economic shocks, and cultural +controversies. + +Beyond standard demographics (age, gender, race/ethnicity, education, income, geography), +prioritize attributes that determine HOW people process and react to news and make +decisions under uncertainty: + +- Technology adoption posture and digital literacy (from technophobe to early adopter) +- Media ecosystem and primary information sources (cable news, social media, podcasts, + local news, none) +- Institutional trust level (government, corporations, media, science, financial system) +- Financial margin and economic anxiety (savings buffer, debt load, paycheck-to-paycheck + status) +- Employment sector and job security perception (federal/state government, private sector, + gig, self-employed, retired) +- Consumer identity and brand relationship patterns (brand loyal, price-driven, + values-driven) +- Social media behavior and influence (passive consumer, active poster, influencer, + non-user) +- Investment and financial literacy (no investments, 401k only, active trader, crypto + holder) +- Religious engagement and worldview (secular, moderate, devout, specific denomination) +- Political identity and engagement level (strong partisan, moderate, disengaged, + single-issue) + +Geographic distribution should span urban/suburban/rural across all major US regions +(Northeast, Southeast, Midwest, Southwest, West Coast), with realistic correlation +structure between attributes (e.g., rural + lower digital literacy + higher religious +engagement)." \ + -o specs/us-national-v1 -y +``` + +### Why This Spec Works for Iran + +The spec was designed to be scenario-agnostic — capturing general human fault lines rather than topic-specific attributes. For the Iran strike scenario, the key activated dimensions are: + +| Spec Dimension | How Iran Activates It | +|---|---| +| **Financial margin** | Gas at $6/gallon hits paycheck-to-paycheck agents catastrophically. Comfortable agents notice but adapt. | +| **Employment sector** | Federal/military employees face deployment risk. Defense contractors see mixed signals (contracts surge but risk increases). Energy sector workers potentially benefit. Service/gig workers get crushed by fuel costs. | +| **Political identity** | Hawks vs. doves is NOT a clean party split. Some Republicans oppose foreign intervention, some Democrats support strikes to prevent nuclear Iran. This is where the simulation gets interesting. | +| **Media ecosystem** | Fox News viewers and MSNBC viewers will be living in different realities about what happened and why. Social media users will encounter different narratives than cable news viewers. | +| **Institutional trust** | High-trust agents believe the Pentagon's justification. Low-trust agents assume ulterior motives (oil, distraction, defense industry profits). | +| **Religious engagement** | Evangelical communities may frame this through eschatological lenses. Muslim-American agents face a completely different experience. Secular agents process it through economic/political lenses. | +| **Geography** | Military base communities (Fayetteville, San Diego, Norfolk) react differently than college towns. Rural communities dependent on driving are hit harder by gas prices than dense urban areas with transit. | +| **Investment literacy** | Active traders react to market chaos differently than 401k-only agents who may not check their balance for months. | + +### What to Check After `extropy spec` + +1. **Completeness**: All 10+ attribute dimensions present +2. **Distribution realism**: Income, age, geography match Census data (~20% rural, ~30% bachelor's+, median income ~$75K) +3. **Correlation structure**: Encoded realistically (rural + lower digital literacy + higher religious engagement, etc.) +4. **No degenerate categories**: Every attribute has 3-4+ meaningful values, no "Other" bucket >20% +5. **Behavioral dimensions present**: Trust, media ecosystem, financial margin — not just a census table +6. **No hallucinated data**: Spot-check 3-4 cited percentages against known sources + +**If any fail:** Fix before proceeding. The spec is the foundation for Studies 1, 2, 3, and 4a. Getting it right matters for everything downstream. + +--- + +## Scenario + +### Design Principles Applied + +- **Milestones, not reactions.** Every timeline event describes what happens in the world — strike targets, missile counts, oil prices, troop deployments, ceasefire terms. Never scripts how people feel or behave. "Protests erupt" is factual (they will). "People are terrified" would be a violation. +- **Concrete specifics.** Not "gas prices go up" but "$3.40 → $4.80 overnight, $6.20 by Week 3, California above $8." Agents need specific numbers to reason against their own financial circumstances. +- **Escalation that hits different demographics differently.** Week 1 is a military/political event. Week 3 is an economic event (Strait of Hormuz). Week 5 hits employment and food prices. Week 8 brings the political dimension (midterms). Week 12 introduces a ceasefire that doesn't undo the damage. Each stage activates new population segments. +- **Neutral framing.** The scenario presents facts about what both sides do and say. It does not editorialize about whether the strikes were justified or unjustified. + +### Context Grounding (as of February 22, 2026) + +The scenario is anchored in real events. Key facts injected into the scenario: + +- The US deployed two carrier strike groups (Abraham Lincoln, Gerald R. Ford) plus B-2 bombers, F-22s, F-35s, and submarines +- Iran fortified nuclear sites at Natanz and Parchin with concrete bunkers +- The US and Israel previously struck Iran's nuclear facilities in June 2025 during a 12-day conflict +- Iran retaliated by striking Al Udeid Air Base in Qatar during the June 2025 conflict +- Indirect talks were held in Muscat (Feb 6) and Geneva (Feb 18) without resolution +- Trump set a 10-15 day deadline for a deal +- Iran's IRGC Navy attempted to seize a US tanker in the Strait of Hormuz on Feb 3 +- A US F-35 shot down an Iranian drone approaching the USS Abraham Lincoln +- Oil markets were already jittery; pre-strike gasoline averaged ~$3.40/gallon nationally +- 2026 midterm elections are on November 3 + +### Scenario Prompt + +```bash +extropy scenario -s "On February 23, 2026, the United States launched airstrikes against +Iran, targeting nuclear facilities at Natanz and Parchin, IRGC command infrastructure, +ballistic missile production sites, and air defense systems across multiple provinces. +President Trump addressed the nation from the Oval Office, stating that Iran's refusal +to reach a diplomatic agreement and its continued pursuit of nuclear weapons capability +left the United States 'no choice but to act decisively.' The strikes involved B-2 +stealth bombers operating from Diego Garcia, carrier-based F/A-18s from the USS Abraham +Lincoln and USS Gerald R. Ford carrier strike groups in the Arabian Sea, and Tomahawk +cruise missiles launched from submarines in the Persian Gulf. The Pentagon confirmed +strikes on 47 targets across Iran. No US ground forces were deployed. + +Iran's Supreme Leader Khamenei issued a statement calling the strikes 'an act of war +against the Iranian nation' and vowed retaliation. Iran's foreign minister recalled all +diplomatic contacts with the United States. The UN Security Council convened an emergency +session. Oil futures spiked 35% within hours of the first reports. European allies +issued mixed responses — the UK declined to participate, France called for immediate +ceasefire, Germany urged restraint from both sides. Israel expressed support but did not +participate directly in the strikes. + +Timeline events: + +Week 1: The immediate aftermath. The Pentagon releases battle damage assessments showing +significant destruction of Iran's nuclear enrichment infrastructure and missile +production facilities. Iran fires 30+ ballistic missiles at US military installations +in Qatar (Al Udeid Air Base) and Bahrain (Naval Support Activity). US missile defense +systems intercept most but not all — 3 US service members are killed and 47 wounded at +Al Udeid. Gasoline prices in the US jump from $3.40/gallon national average to $4.80 +overnight. The Dow drops 1,400 points. Protests occur in major US cities — both anti-war +demonstrations and pro-strike rallies. Congress is divided: Senate passes a resolution +supporting the strikes 54-46, House fails to pass any resolution. + +Week 3: Iran announces a naval blockade of the Strait of Hormuz. Two commercial tankers +are struck by IRGC anti-ship missiles, killing 14 crew members. Lloyd's of London +suspends insurance coverage for all vessels transiting the Persian Gulf. Oil reaches +$140/barrel. US gasoline hits $6.20/gallon nationally, with California stations above +$8.00. The Federal Reserve issues an emergency statement warning of inflationary +pressures. Major US airlines announce fuel surcharges of $75-150 per ticket. +Iran-backed Houthi forces intensify attacks on Red Sea shipping, effectively closing +a second major trade route. Walmart, Target, and Amazon warn of supply chain disruptions +and potential price increases on imported goods within 30-60 days. + +Week 5: The economic cascade deepens. US unemployment claims rise 40% as energy-dependent +industries begin layoffs. Trucking companies impose 25% fuel surcharges on all freight. +Food prices rise 15% nationally as transportation and fertilizer costs spike. The Federal +Reserve holds an emergency meeting but declines to cut rates, citing inflation risk. Iran +launches a second wave of missile strikes targeting Saudi oil infrastructure at Abqaiq +and Ras Tanura, temporarily knocking 4 million barrels/day of Saudi production offline. +Oil briefly touches $180/barrel. The US announces deployment of 15,000 additional troops +to the Middle East. The Pentagon calls up 30,000 reservists. + +Week 8: The Strait of Hormuz remains contested. US Navy minesweepers and escort convoys +have partially reopened shipping lanes, but transit times have doubled and insurance +premiums remain 10x pre-crisis levels. Oil has stabilized around $130/barrel. US gasoline +averages $5.80/gallon. Cumulative US job losses attributed to the energy shock reach +400,000. Iran's internal protest movement has fractured — some factions rally behind the +regime against foreign attack, others blame the regime for provoking the strikes. Russia +and China issue a joint statement condemning the US strikes and announcing expanded +economic support for Iran. The 2026 midterm elections are 3 months away. Both parties +are attempting to frame the conflict — Republicans argue the strikes prevented a nuclear +Iran, Democrats argue the administration stumbled into an unnecessary war that is +destroying the economy. + +Week 12: A fragile ceasefire is brokered through Omani and Qatari mediation. Iran agrees +to halt Strait of Hormuz operations in exchange for a US commitment to cease strikes and +begin indirect negotiations on a new nuclear framework. Oil drops to $105/barrel but +remains well above pre-crisis levels. US gasoline averages $4.60/gallon. Total estimated +US economic cost of the 12-week crisis exceeds $800 billion. 23 US service members have +been killed across all theaters. The political landscape has shifted — the crisis +dominates every 2026 midterm campaign. Inflation is running at 8.5% annualized. Consumer +confidence is at its lowest level since 2008." \ + --type evolving \ + --timestep weeks \ + --timesteps 12 \ + -o scenario/iran-strikes -y +``` + +### What to Check After `extropy scenario` + +1. **Milestones only**: No milestone scripts human behavioral responses. "Protests occur" is factual (observable). "People are panicking" would be a violation. +2. **Evolving type confirmed**: Weekly timesteps, 12 total +3. **Neutral framing**: Both the US justification and Iran's response are presented factually +4. **Concrete specifics present**: Dollar amounts, barrel prices, casualty numbers, troop counts — agents need these to reason against their own circumstances +5. **Escalation hits different demographics at different stages**: Military families (Week 1), all drivers (Week 1-3), energy-dependent workers (Week 5), reservists (Week 5), voters (Week 8), everyone (Week 12) +6. **No future-event contamination risk**: Each week's milestones are self-contained and don't reference later events + +--- + +## Outcomes + +### Design Rationale + +Every outcome follows the best practices: + +- **No floats.** Every outcome is categorical or open-ended. +- **Categorical options are concrete and distinguishable.** Each describes an observable state or action, not a point on a vague scale. +- **Two open-ended fields** surface emergent behavior beyond the categorical framework. +- **No lazy booleans.** Every question where the "how" matters more than the "whether" uses categorical. + +### Outcome Definitions + +``` +--outcomes "war_support: categorical (strongly support the strikes — Iran had to be +stopped, support but concerned about escalation and consequences, conflicted — see +arguments on both sides, oppose but understand the rationale for acting, strongly +oppose the strikes — this was unnecessary and reckless)" + +--outcomes "personal_economic_impact: categorical (severely affected — can't afford +basic needs like gas groceries or bills, noticeably affected — cutting back significantly +on spending, somewhat affected — adjusting budget and delaying purchases, minimally +affected — noticed price increases but managing fine, not meaningfully affected)" + +--outcomes "political_shift: categorical (more supportive of the current administration, +no change in political views, less supportive of the current administration, completely +changed my view of this administration, disengaged from politics entirely)" + +--outcomes "behavioral_change: categorical (no changes to daily life, minor adjustments +— driving less or combining errands, significant changes — canceled travel or delayed +major purchases, major life disruption — job affected or drawing down savings, +preparing for worse — stockpiling supplies or making contingency plans)" + +--outcomes "midterm_vote_impact: categorical (more motivated to vote and support the +administration party, more motivated to vote and oppose the administration party, no +change in my voting plans, less likely to vote — disillusioned with both parties, was +not planning to vote regardless)" + +--outcomes "threat_perception: categorical (Iran is the primary threat — strikes were +necessary to prevent a nuclear-armed Iran, the economic fallout is the primary threat +— the strikes made everything worse, wider regional war is the primary threat — this +could spiral out of control, domestic instability is the primary threat — the country +is tearing itself apart, do not feel personally threatened by any of this)" + +--outcomes "personal_response: open-ended (describe how this crisis is affecting your +daily life, your family, your community, and your view of the country's direction)" + +--outcomes "unexpected_factor: open-ended (what is happening in your life or community +because of this crisis that you think the news is not covering?)" +``` + +### Why These Outcomes + +| Outcome | What It Measures | Key Spec Dimensions Activated | +|---|---|---| +| `war_support` | Core political question. NOT a clean hawk/dove binary — the middle three options capture the messy ambivalence where most Americans live. | Political identity, media ecosystem, institutional trust, military connection | +| `personal_economic_impact` | Observable material consequences. $6/gallon doesn't hit a remote worker in Manhattan the same way it hits a trucker in rural Texas. | Financial margin, employment sector, geography (urban/rural), consumer identity | +| `political_shift` | Does the crisis move people politically? Rally-round-the-flag vs. economic pain backlash. | Political identity, media ecosystem, institutional trust | +| `behavioral_change` | Actions, not feelings. Are people actually changing behavior? | Financial margin, geography, employment sector | +| `midterm_vote_impact` | Directly connects this study to Study 4 (midterms). Same population, same spec. | Political identity, political engagement level | +| `threat_perception` | What are people actually afraid of? Segments beautifully by media ecosystem — Fox viewers and MSNBC viewers will identify different primary threats. | Media ecosystem, institutional trust, political identity, military connection | +| `personal_response` | Open-ended narrative. Captures the subjective experience and reasoning that categoricals can't. | All dimensions | +| `unexpected_factor` | Explicitly invites responses outside the categorical framework. This is where emergent second-order effects surface. | All dimensions | + +--- + +## What to Check After Simulation + +### Temporal Evolution (Week 1 → 12) + +- [ ] `war_support` should shift over time. Expect rally-round-the-flag in Week 1 that erodes as economic pain mounts through Weeks 3-8. The ceasefire at Week 12 should NOT fully restore Week 1 support levels. +- [ ] `personal_economic_impact` should escalate sharply Week 1→3 (gas price shock) and continue deepening through Week 5 (food prices, job losses). Should NOT snap back at Week 12 — agents who lost jobs or depleted savings don't recover because oil dropped $25. +- [ ] `political_shift` should show divergent trajectories: initially supportive agents may turn as costs mount; initially opposed agents likely harden. The middle ("conflicted") should shrink over 12 weeks as people are forced to take a position. +- [ ] `midterm_vote_impact` should intensify over time as the election approaches. By Week 8, nearly every agent should have SOME midterm impact — "no change" should be a shrinking category. + +### Demographic Divergence + +- [ ] **Military-connected agents** should have distinct trajectories. Support for strikes but deep anxiety about deployment/escalation. Casualty reports (Week 1, ongoing) hit this group personally. Reservist callup at Week 5 is a concrete life disruption for this segment. +- [ ] **Low financial margin agents** should show the sharpest economic impact escalation. By Week 3, paycheck-to-paycheck agents at $6/gallon gas should be in genuine crisis. High-income agents should still be at "adjusting budget." +- [ ] **Rural agents** should be hit harder by gas prices than urban agents (longer commutes, no transit alternatives, more driving-dependent). +- [ ] **Federal/defense sector employees** should have a complex response — the strike may benefit their industry long-term but the crisis creates immediate uncertainty. +- [ ] **Media ecosystem should drive threat perception.** Conservative media consumers more likely to identify Iran as the primary threat. Liberal media consumers more likely to identify economic fallout or wider war. Social-media-primary agents may have the most fragmented threat perception. +- [ ] **Religious engagement** should produce distinct framing. Evangelical agents may invoke eschatological or moral frameworks. Muslim-American agents face a fundamentally different experience (community anxiety, potential backlash). Secular agents process through economic/political lenses. + +### Quality Checks + +- [ ] Reasoning traces are first-person, differentiated, and reference specific scenario details (gas prices, casualty numbers, not vague "the situation") +- [ ] No more than 20% of agents give near-identical responses at any timestep +- [ ] No future-event contamination: Week 3 agents must not reference the ceasefire brokered at Week 12 +- [ ] Open-ended `unexpected_factor` responses surface genuinely novel insights beyond the categorical outcomes — if they just restate the categorical selection, central tendency bias is dominating +- [ ] Intent-to-action sanity check: if 80% of agents report "major life disruption" by Week 3, that's LLM enthusiasm bias. Real populations are stickier than that. + +--- + +## Cross-Study Connections + +### Link to Study 4 (2026 Midterms) + +The Iran crisis is the defining context for the November 2026 midterm elections. The same 5,000 Spec A agents who experience the Iran strike scenario in Study 2 are the same national electorate being modeled in Study 4a (House control prediction). + +This creates a powerful cross-study narrative: + +- Study 2 shows HOW the Iran crisis reshapes political attitudes over 12 weeks +- Study 4a shows WHETHER those reshaped attitudes translate into actual midterm voting behavior +- The `midterm_vote_impact` outcome in Study 2 directly previews what Study 4a will model in detail + +### Link to Study 1 (ASI Announcement) + +Both studies use Spec A and can be compared side-by-side: "Here's how the same 5,000 Americans respond to a technological black swan vs. a geopolitical black swan." The demographic segmentation patterns should be dramatically different — the ASI scenario primarily activates technology posture and employment sector, while Iran primarily activates political identity, financial margin, and military connection. + +--- + +## Pipeline Commands + +```bash +# Spec A should already be generated and validated from Study 1 +# If not: +extropy spec [spec prompt above] -o specs/us-national-v1 -y + +# >>> CHECKPOINT: Gate 1 — validate spec (see checklist above) + +# Generate personas for Iran study +extropy persona -s iran-strikes -y + +# >>> CHECKPOINT: Gate 3 — verify persona range +# >>> Must include: military family in base town, paycheck-to-paycheck rural commuter, +# wealthy urban professional, Muslim-American, evangelical veteran, anti-war college +# student, defense contractor employee, energy sector worker, retired fixed-income, +# politically disengaged single parent +# >>> Must NOT be all politically engaged coast-dwellers + +# Sample agents +extropy sample -s iran-strikes -n 5000 --seed 42 + +# >>> CHECKPOINT: Gate 4 — pull 30 random agents, verify distribution +# >>> Verify: geographic spread (not all coastal cities) +# >>> Verify: financial margin distribution (at least 25-30% paycheck-to-paycheck) +# >>> Verify: military connection exists but isn't overrepresented (~7-10% veteran, ~1% active duty) +# >>> Verify: political identity spans full range (not all moderates) + +# Build network +extropy network -s iran-strikes --seed 42 + +# >>> CHECKPOINT: Gate 5 — verify network topology +# >>> LLM REVIEW MANDATORY + +# Run simulation +extropy simulate -s iran-strikes --seed 42 + +# >>> CHECKPOINT: Gate 6 — full results review +extropy results +extropy results --segment political_identity +extropy results --segment financial_margin +extropy results --segment employment_sector +extropy results --segment geography +extropy results --segment media_ecosystem +``` + +--- + +## What Extropy Surfaces That Other Methods Can't + +1. **The rally-to-resentment trajectory is not uniform.** It depends on which specific demographic you are, how the economic pain hits you, and what information ecosystem you're in. A poll at Week 3 tells you "58% oppose." Extropy tells you which 58%, why, and how they got there from Week 1. + +2. **Economic pain is geographically and demographically clustered.** $6 gas in Manhattan (where most people take the subway) is a different event than $6 gas in rural Oklahoma (where everything requires driving 30+ miles). Extropy models this because the spec encodes geography, financial margin, and employment sector jointly. + +3. **Military families are the swing constituency.** They're the one demographic with both strong patriotic framing (support the mission) AND direct personal cost (deployment risk, casualty anxiety). How this group moves over 12 weeks likely predicts the midterm impact better than any national poll. + +4. **The ceasefire doesn't reset the damage.** An agent who lost their job in Week 5 doesn't get un-fired in Week 12. An agent who depleted their savings on $6 gas doesn't get a refund. Extropy tracks cumulative impact, not point-in-time sentiment. + +5. **Media ecosystem creates parallel realities.** By Week 8, Fox viewers and MSNBC viewers may have fundamentally incompatible understandings of what happened and why. This fragmentation is invisible in polls but Extropy can measure it because the spec encodes media ecosystem as an agent attribute. diff --git a/examples/.DS_Store b/examples/.DS_Store index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..2316f7cd561250ef6c353ce9f534e37c547f162f 100644 GIT binary patch delta 335 zcmZoMXfc=|#>B`mF;Q%yo}wrd0|Nsi1A_nqLnVVQg8_qaQh9N~#Kh(GAPEkJVulii zQic>{$)ue8B%s{(q=KBx;t~UcYm7|HEUawo9PAw29I?R}`Q^bSi6y1QPKia)AYMp* zeohjMotPAsnOYt%AmW^#SCW{Sms$kYkeQMSR1y=OnU|7Z?v!7emr`F0)*K9x;o#)p zj295Et~RkS&`~fmt<_Pewlo4V%`MDpYdJYYmG!NI;AjHu~2NHo+1YW5HK<@2y7N)`o_9hfoT`xW_AvK4xj>{$am(+{342+ UKzW7)kiy9(Jj$D6L{=~Z01<``T>t<8 diff --git a/extropy/cli/commands/scenario.py b/extropy/cli/commands/scenario.py index 3d58513..bc8a4a1 100644 --- a/extropy/cli/commands/scenario.py +++ b/extropy/cli/commands/scenario.py @@ -190,19 +190,12 @@ def scenario_command( questions = sufficiency_result.questions if agent_mode: - out.error( - "Scenario description insufficient", - exit_code=ExitCode.CLARIFICATION_NEEDED, - questions=[ - { - "id": q.id, - "question": q.question, - "type": q.type, - "options": q.options, - "default": q.default, - } - for q in questions - ], + resume_cmd = ( + f'extropy scenario "{description}" -o {scenario_name} --use-defaults' + ) + out.needs_clarification( + questions=questions, + resume_command=resume_cmd, ) raise typer.Exit(out.finish()) @@ -450,6 +443,7 @@ def do_hydration(): # - Scenario configuration (event, exposure, outcomes) will be generated on demand from ...scenario import create_scenario_spec + from ...scenario.validator import validate_scenario if not agent_mode: console.print() @@ -526,6 +520,16 @@ def run_pipeline(): result_spec.meta.name = scenario_name result_spec.meta.base_population = f"population.v{pop_version}" + # Re-validate after metadata finalization. + # The initial validation occurs inside create_scenario_spec before CLI-level + # metadata (including base_population) is attached. + validation_result = validate_scenario( + spec=result_spec, + population_spec=pop_spec, + agent_count=None, + network=None, + ) + # Display Summary if not agent_mode: _display_scenario_summary(result_spec) diff --git a/extropy/cli/commands/validate.py b/extropy/cli/commands/validate.py index 4dd8bd8..8ef9bc5 100644 --- a/extropy/cli/commands/validate.py +++ b/extropy/cli/commands/validate.py @@ -17,9 +17,14 @@ def _is_json_output() -> bool: return get_json_mode() or is_agent_mode() +def _strip_invalid_suffix(name: str) -> str: + """Strip versioned invalid artifact suffix from YAML filename.""" + return re.sub(r"\.invalid\.v\d+(?=\.ya?ml$)", "", name) + + def _is_scenario_file(path: Path) -> bool: """Check if file is a scenario spec based on naming convention.""" - name = path.name + name = _strip_invalid_suffix(path.name) # Legacy patterns if name.endswith(".scenario.yaml") or name.endswith(".scenario.yml"): return True @@ -33,7 +38,7 @@ def _is_scenario_file(path: Path) -> bool: def _is_persona_file(path: Path) -> bool: """Check if file is a persona config based on naming convention.""" - name = path.name + name = _strip_invalid_suffix(path.name) if name in {"persona.yaml", "persona.yml"}: return True if re.match(r"^persona\.v\d+\.ya?ml$", name): @@ -41,6 +46,37 @@ def _is_persona_file(path: Path) -> bool: return False +def _canonical_yaml_path_for_invalid(path: Path) -> Path | None: + """Return canonical path for an invalid artifact path, if applicable.""" + match = re.match(r"^(?P.+)\.invalid\.v\d+(?P\.ya?ml)$", path.name) + if not match: + return None + return path.with_name(f"{match.group('base')}{match.group('ext')}") + + +def _promote_if_valid_invalid(spec_file: Path, out: Output) -> None: + """Promote validated invalid YAML artifact to canonical YAML path.""" + canonical_path = _canonical_yaml_path_for_invalid(spec_file) + if canonical_path is None: + return + if canonical_path == spec_file: + return + try: + spec_file.replace(canonical_path) + except Exception as e: + out.warning( + f"Validated but failed to promote invalid artifact: {e}", + file=str(spec_file), + canonical=str(canonical_path), + ) + return + out.success( + f"Promoted validated artifact: {canonical_path.name}", + promoted_from=str(spec_file), + promoted_to=str(canonical_path), + ) + + def _validate_population_spec(spec_file: Path, strict: bool, out: Output) -> int: """Validate a population spec.""" # Load spec @@ -159,6 +195,7 @@ def _validate_population_spec(spec_file: Path, strict: bool, out: Output) -> int out.divider() out.text("[green]Validation passed[/green]") out.divider() + _promote_if_valid_invalid(spec_file, out) return out.finish() @@ -267,6 +304,7 @@ def _validate_scenario_spec(spec_file: Path, out: Output) -> int: out.divider() out.text("[green]Validation passed[/green]") out.divider() + _promote_if_valid_invalid(spec_file, out) return out.finish() @@ -277,21 +315,27 @@ def _resolve_merged_population_for_persona(persona_file: Path) -> PopulationSpec from ...core.models.scenario import ScenarioSpec scenario_dir = persona_file.parent - scenario_files_with_versions: list[tuple[int, Path]] = [] - for path in scenario_dir.glob("scenario.v*.yaml"): - match = re.match(r"^scenario\.v(\d+)\.yaml$", path.name) + scenario_files_with_versions: list[tuple[int, int, Path]] = [] + for path in scenario_dir.iterdir(): + if not path.is_file(): + continue + match = re.match( + r"^scenario\.v(\d+)(?:\.invalid\.v\d+)?\.ya?ml$", + path.name, + ) if not match: continue - scenario_files_with_versions.append((int(match.group(1)), path)) + is_invalid = 1 if ".invalid." in path.name else 0 + scenario_files_with_versions.append((int(match.group(1)), is_invalid, path)) - scenario_files_with_versions.sort(key=lambda item: item[0]) + scenario_files_with_versions.sort(key=lambda item: (item[0], -item[1])) if not scenario_files_with_versions: raise FileNotFoundError( f"No scenario.vN.yaml found next to persona file: {persona_file}" ) - # Choose latest scenario version in this directory. - scenario_path = scenario_files_with_versions[-1][1] + # Choose latest scenario version in this directory (prefer canonical over invalid). + scenario_path = scenario_files_with_versions[-1][2] scenario_spec = ScenarioSpec.from_yaml(scenario_path) pop_name, pop_version = scenario_spec.meta.get_population_ref() @@ -387,6 +431,7 @@ def _validate_persona_spec(spec_file: Path, out: Output) -> int: out.divider() out.text("[green]Validation passed[/green]") out.divider() + _promote_if_valid_invalid(spec_file, out) return out.finish() @@ -406,6 +451,7 @@ def validate_command( - *.scenario.yaml → scenario spec validation - persona.vN.yaml → persona config validation - *.yaml → population spec validation + - *.invalid.vN.yaml → same type detection as canonical name EXIT CODES: 0 = Success (valid spec) diff --git a/extropy/population/sampler/distributions.py b/extropy/population/sampler/distributions.py index 9d872ae..568d66f 100644 --- a/extropy/population/sampler/distributions.py +++ b/extropy/population/sampler/distributions.py @@ -174,12 +174,30 @@ def _sample_lognormal( else mean * 0.5 ) - # Lognormal requires mean > 0 + # Resolve min/max bounds - formula takes precedence over static + min_bound = _resolve_optional_param( + dist.min, getattr(dist, "min_formula", None), agent + ) + max_bound = _resolve_optional_param( + dist.max, getattr(dist, "max_formula", None), agent + ) + + # Degenerate/invalid cases should degrade deterministically instead of crashing. + # Common case: formula intentionally returns 0 for not-applicable agents. if mean <= 0: - raise ValueError( - f"Lognormal distribution requires mean > 0, got {mean}. " - "Check the distribution parameters or formula." - ) + value = 0.0 + if min_bound is not None: + value = max(value, min_bound) + if max_bound is not None: + value = min(value, max_bound) + return value + if std <= 0: + value = mean + if min_bound is not None: + value = max(value, min_bound) + if max_bound is not None: + value = min(value, max_bound) + return value # Convert from actual mean/std to log-space mu/sigma # mu = log(mean^2 / sqrt(mean^2 + std^2)) @@ -191,14 +209,6 @@ def _sample_lognormal( value = rng.lognormvariate(mu, sigma) - # Resolve min/max bounds - formula takes precedence over static - min_bound = _resolve_optional_param( - dist.min, getattr(dist, "min_formula", None), agent - ) - max_bound = _resolve_optional_param( - dist.max, getattr(dist, "max_formula", None), agent - ) - # Apply clamping if min_bound is not None: value = max(value, min_bound) diff --git a/extropy/population/spec_builder/hydrators/household.py b/extropy/population/spec_builder/hydrators/household.py index 5ecede7..3508435 100644 --- a/extropy/population/spec_builder/hydrators/household.py +++ b/extropy/population/spec_builder/hydrators/household.py @@ -119,6 +119,10 @@ def hydrate_household_config( - Adapt household types and education stages to the local context (e.g., Japanese education system stages differ from US). - Be specific to the population described, not generic global averages. - Ensure household_type_weights sum to ~1.0 for each age bracket. +- Young-adult realism guardrail: for the earliest adult bracket (typically ages 18-20), + keep partnered household and parent-household shares conservative by default unless + strong local evidence supports higher rates. If you set higher rates, ensure they + are explicitly justified by cited sources. - If data is unavailable for a specific field, use the best available regional or national data. Return a single JSON object with all fields.""" diff --git a/extropy/simulation/state.py b/extropy/simulation/state.py index b654d48..f3a8561 100644 --- a/extropy/simulation/state.py +++ b/extropy/simulation/state.py @@ -174,12 +174,16 @@ def _create_schema(self) -> None: ON exposures(run_id, timestep) """ ) - cursor.execute( + # Legacy DBs may not have force_rereason/info_epoch yet; migration adds them. + try: + cursor.execute( + """ + CREATE INDEX IF NOT EXISTS idx_exposures_force_epoch + ON exposures(run_id, agent_id, force_rereason, info_epoch) """ - CREATE INDEX IF NOT EXISTS idx_exposures_force_epoch - ON exposures(run_id, agent_id, force_rereason, info_epoch) - """ - ) + ) + except sqlite3.OperationalError: + pass cursor.execute( """ CREATE INDEX IF NOT EXISTS idx_timeline_timestep diff --git a/test-v2-e2e/extended.network-config.default.seed42.20260215-233218.yaml b/test-v2-e2e/extended.network-config.default.seed42.20260215-233218.yaml deleted file mode 100644 index 2c029ec..0000000 --- a/test-v2-e2e/extended.network-config.default.seed42.20260215-233218.yaml +++ /dev/null @@ -1,220 +0,0 @@ -avg_degree: 20.0 -rewire_prob: 0.05 -similarity_store_threshold: 0.05 -similarity_threshold: 0.3 -similarity_steepness: 10.0 -candidate_mode: blocked -candidate_pool_multiplier: 12.0 -min_candidate_pool: 80 -similarity_workers: 1 -similarity_chunk_size: 64 -checkpoint_every_rows: 250 -triadic_closure_prob: 0.6 -target_clustering: 0.35 -target_modularity: 0.55 -inter_community_scale: 0.3 -max_calibration_iterations: 12 -quality_profile: balanced -topology_gate: strict -max_calibration_minutes: 30 -calibration_restarts: 4 -target_largest_component_ratio: 0.95 -target_degree_tolerance_pct: 0.15 -target_modularity_tolerance: 0.1 -target_clustering_tolerance: 0.08 -bridge_budget_fraction: 0.08 -swap_passes: 3 -power_law_exponent: 2.5 -identity_clustering_boost: 1.5 -auto_save_generated_config: true -allow_quarantine: true -quarantine_suffix: rejected -attribute_weights: - state: - weight: 5.0 - match_type: exact - school_type: - weight: 4.5 - match_type: exact - political_ideology: - weight: 3.5 - match_type: exact - pta_membership: - weight: 3.0 - match_type: exact - community_involvement: - weight: 2.8 - match_type: exact - youngest_child_age: - weight: 2.5 - match_type: numeric_range - range_value: 5.0 - religious_affiliation: - weight: 2.2 - match_type: exact - parental_control_preference: - weight: 2.0 - match_type: exact - lgbtq_acceptance: - weight: 2.0 - match_type: exact - school_board_engagement: - weight: 3.2 - match_type: within_n - range_value: 1.0 - ordinal_levels: - none: 1 - minimal: 2 - occasional: 3 - active: 4 - very_active: 5 -degree_multipliers: -- attribute: social_network_density - condition: very_high - multiplier: 2.5 - rationale: Very high social network density indicates natural super-connectors -- attribute: social_network_density - condition: high - multiplier: 1.8 - rationale: High social network density indicates well-connected individuals -- attribute: pta_membership - condition: true - multiplier: 1.6 - rationale: PTA members interact with many other parents regularly -- attribute: school_board_engagement - condition: very_active - multiplier: 2.0 - rationale: Very active school board participants know many stakeholders -- attribute: school_board_engagement - condition: active - multiplier: 1.5 - rationale: Active school board participants have expanded networks -- attribute: community_involvement - condition: high - multiplier: 1.4 - rationale: High community involvement creates many connection opportunities -edge_type_rules: -- name: pta_colleague - condition: a_pta_membership == True and b_pta_membership == True and a_state == - b_state - priority: 40 - description: Both are PTA members in the same area, regularly collaborate on school - issues -- name: school_board_activist - condition: (a_school_board_engagement == 'very_active' or a_school_board_engagement - == 'active') and (b_school_board_engagement == 'very_active' or b_school_board_engagement - == 'active') - priority: 35 - description: Both actively engaged in school board issues, likely know each other - from meetings -- name: school_parent - condition: a_school_type == b_school_type and a_school_type == 'public' and a_state - == b_state - priority: 30 - description: Parents with children at the same public school system -- name: political_ally - condition: a_political_ideology == b_political_ideology and a_state == b_state and - (a_school_board_engagement != 'none' and b_school_board_engagement != 'none') - priority: 25 - description: Share political views and both engaged in school board issues - likely - allies -- name: neighbor - condition: a_state == b_state and a_urban_rural == b_urban_rural - priority: 15 - description: Live in same suburban area of same state, general neighbors -- name: acquaintance - condition: a_state == b_state - priority: 10 - description: Live in same state, general community acquaintances -influence_factors: -- attribute: school_board_engagement - type: ordinal - levels: - none: 1 - minimal: 2 - occasional: 3 - active: 4 - very_active: 5 - weight: 0.35 - description: More active school board participants have greater influence on community - opinion about the book ban -- attribute: community_involvement - type: ordinal - levels: - none: 1 - low: 2 - medium: 3 - high: 4 - weight: 0.25 - description: Higher community involvement gives more social capital and influence -- attribute: pta_membership - type: boolean - weight: 0.2 - description: PTA members have organizational platform and credibility with other - parents -- attribute: education_level - type: ordinal - levels: - high_school: 1 - associate: 2 - bachelor: 3 - graduate: 4 - weight: 0.15 - description: Higher education may confer more credibility in debates about curriculum - and books -default_edge_type: acquaintance -ordinal_levels: - school_board_engagement: - none: 1 - minimal: 2 - occasional: 3 - active: 4 - very_active: 5 -generated_from: 10 US suburban homeowners with school-age children + Local school - board book ban decision -generation_rationale: ' - - This is a small, tight-knit suburban community of 10 homeowners with school-age - children centered around a local school board book ban decision. Key connection - drivers: - - - 1. **Geographic & School Connections**: Since this is a suburban community with - school-age children, the primary connection points are: state/location (they''re - neighbors), school_type (public school parents will know each other), and school_board_engagement - (people active in school issues will know each other). The social_network_density - attribute directly indicates how connected people are. - - - 2. **Shared Interests & Activities**: PTA membership, community_involvement, and - library_usage_frequency create natural meeting points. Parents with similar aged - children (youngest_child_age, oldest_child_age) will connect through school activities. - - - 3. **Social/Political Alignment**: For a contentious issue like book bans, political_ideology, - religious_affiliation, parental_control_preference, and lgbtq_acceptance will drive - homophily - people cluster with like-minded individuals on polarizing topics. - - - 4. **Hubs**: People with high community_involvement, PTA membership, and very_active - school_board_engagement are natural connectors. High social_network_density directly - indicates hub status. - - - 5. **Edge Types**: We need to distinguish between household partners (same household), - close neighbors (same state + high interaction), PTA connections, school board activism - connections, and general acquaintances. - - - 6. **Influence**: School board engagement level (more active = more influence), - PTA membership (gives platform), community_involvement (higher = more influence), - and education_level (graduate degree holders may have more credibility) create asymmetry. - - - 7. **Average Degree**: This is a small, politically-charged community issue bringing - people together. With only 10 people and high stakes, expect 20-35 connections per - person as neighbors and parents interact frequently around schools and the controversial - decision. - - ' -seed: 42 diff --git a/test-v2-e2e/extended.network-config.default.seednoseed.20260215-231703.yaml b/test-v2-e2e/extended.network-config.default.seednoseed.20260215-231703.yaml deleted file mode 100644 index e2fdb03..0000000 --- a/test-v2-e2e/extended.network-config.default.seednoseed.20260215-231703.yaml +++ /dev/null @@ -1,296 +0,0 @@ -avg_degree: 20.0 -rewire_prob: 0.05 -similarity_store_threshold: 0.05 -similarity_threshold: 0.3 -similarity_steepness: 10.0 -candidate_mode: blocked -candidate_pool_multiplier: 12.0 -min_candidate_pool: 80 -similarity_workers: 1 -similarity_chunk_size: 64 -checkpoint_every_rows: 250 -triadic_closure_prob: 0.6 -target_clustering: 0.35 -target_modularity: 0.55 -inter_community_scale: 0.3 -max_calibration_iterations: 12 -quality_profile: balanced -topology_gate: strict -max_calibration_minutes: 30 -calibration_restarts: 4 -target_largest_component_ratio: 0.95 -target_degree_tolerance_pct: 0.15 -target_modularity_tolerance: 0.1 -target_clustering_tolerance: 0.08 -bridge_budget_fraction: 0.08 -swap_passes: 3 -power_law_exponent: 2.5 -identity_clustering_boost: 1.5 -auto_save_generated_config: true -allow_quarantine: true -quarantine_suffix: rejected -attribute_weights: - school_type: - weight: 4.5 - match_type: exact - political_ideology: - weight: 4.0 - match_type: within_n - range_value: 1.0 - ordinal_levels: - very_conservative: 1 - conservative: 2 - moderate: 3 - liberal: 4 - very_liberal: 5 - pta_membership: - weight: 3.5 - match_type: exact - parental_control_preference: - weight: 3.5 - match_type: within_n - range_value: 1.0 - ordinal_levels: - parents_only: 1 - parents_primary: 2 - shared_responsibility: 3 - educators_primary: 4 - educators_only: 5 - youngest_child_age: - weight: 3.0 - match_type: numeric_range - range_value: 5.0 - school_board_engagement: - weight: 3.0 - match_type: within_n - range_value: 1.0 - ordinal_levels: - none: 0 - minimal: 1 - occasional: 2 - active: 3 - very_active: 4 - religious_affiliation: - weight: 2.8 - match_type: exact - lgbtq_acceptance: - weight: 2.5 - match_type: within_n - range_value: 1.0 - ordinal_levels: - very_resistant: 1 - resistant: 2 - neutral: 3 - accepting: 4 - very_accepting: 5 - community_involvement: - weight: 2.5 - match_type: within_n - range_value: 1.0 - ordinal_levels: - none: 0 - low: 1 - medium: 2 - high: 3 - very_high: 4 - num_children: - weight: 2.0 - match_type: numeric_range - range_value: 2.0 - age_appropriateness_concern: - weight: 2.0 - match_type: within_n - range_value: 1.0 - ordinal_levels: - very_relaxed: 1 - relaxed: 2 - moderate: 3 - concerned: 4 - very_concerned: 5 -degree_multipliers: -- attribute: school_board_engagement - condition: very_active - multiplier: 2.5 - rationale: Very active school board participants are highly visible, attend all - meetings, and know everyone in the community advocacy space -- attribute: pta_membership - condition: true - multiplier: 1.8 - rationale: PTA members attend regular meetings, volunteer at events, and interact - with many other parents -- attribute: community_involvement - condition: high - multiplier: 1.7 - rationale: Highly involved community members participate in multiple local organizations - and know more neighbors -- attribute: num_children - condition: 3 - multiplier: 1.5 - rationale: Parents with more children have more touchpoints through multiple classrooms, - activities, and parent networks -- attribute: school_type - condition: public - multiplier: 1.4 - rationale: Public school parents interact with a broader, more diverse parent base - than private school families -- attribute: social_network_density - condition: high - multiplier: 1.6 - rationale: People with naturally high social network density maintain more active - connections in their community -edge_type_rules: -- name: close_ally - condition: a_school_type == b_school_type and a_pta_membership == b_pta_membership - and a_pta_membership == True and (a_political_ideology == b_political_ideology - or a_parental_control_preference == b_parental_control_preference) - priority: 50 - description: Close allies who share school type, are both in PTA, and align ideologically - on the book ban issue -- name: pta_colleague - condition: a_pta_membership == True and b_pta_membership == True and a_school_type - == b_school_type - priority: 40 - description: PTA colleagues who work together at the same school type but may have - different ideologies -- name: school_board_ally - condition: (a_school_board_engagement == 'very_active' or a_school_board_engagement - == 'active') and (b_school_board_engagement == 'very_active' or b_school_board_engagement - == 'active') and a_political_ideology == b_political_ideology - priority: 45 - description: Active school board participants who share political ideology and work - together on advocacy -- name: school_parent - condition: a_school_type == b_school_type - priority: 30 - description: Parents whose children attend the same school type (public/private/charter) -- name: ideological_peer - condition: a_political_ideology == b_political_ideology and a_religious_affiliation - == b_religious_affiliation - priority: 25 - description: Community members who share both political and religious views on the - book ban debate -- name: neighbor - condition: a_urban_rural == b_urban_rural - priority: 10 - description: Suburban neighbors in the same community who know each other through - proximity -influence_factors: -- attribute: school_board_engagement - type: ordinal - levels: - none: 0 - minimal: 1 - occasional: 2 - active: 3 - very_active: 4 - weight: 0.4 - description: Higher school board engagement creates authority and visibility in - the community debate, making their opinions more influential -- attribute: pta_membership - type: boolean - weight: 0.25 - description: PTA members have insider knowledge and established credibility on school - matters -- attribute: community_involvement - type: ordinal - levels: - none: 0 - low: 1 - medium: 2 - high: 3 - very_high: 4 - weight: 0.2 - description: Higher community involvement creates broader social reach and opinion - leadership -- attribute: education_priority - type: ordinal - levels: - low: 1 - medium: 2 - high: 3 - weight: 0.15 - description: Parents who prioritize education are seen as more credible voices on - school curriculum and library issues -default_edge_type: acquaintance -ordinal_levels: - political_ideology: - very_conservative: 1 - conservative: 2 - moderate: 3 - liberal: 4 - very_liberal: 5 - parental_control_preference: - parents_only: 1 - parents_primary: 2 - shared_responsibility: 3 - educators_primary: 4 - educators_only: 5 - school_board_engagement: - none: 0 - minimal: 1 - occasional: 2 - active: 3 - very_active: 4 - lgbtq_acceptance: - very_resistant: 1 - resistant: 2 - neutral: 3 - accepting: 4 - very_accepting: 5 - community_involvement: - none: 0 - low: 1 - medium: 2 - high: 3 - very_high: 4 - age_appropriateness_concern: - very_relaxed: 1 - relaxed: 2 - moderate: 3 - concerned: 4 - very_concerned: 5 -generated_from: 10 US suburban homeowners with school-age children + Local school - board book ban decision -generation_rationale: 'This is a small, tight-knit suburban community of 10 homeowners - with school-age children facing a contentious school board book ban decision. These - connections will be driven primarily by: - - - 1. **Geographic and institutional proximity**: They''re all suburban homeowners - in the same community, likely attending the same schools and school board meetings. - School type (public vs private) and school board engagement are critical dividers - and connectors. - - - 2. **Parenting and community involvement**: PTA membership, community involvement - level, and school board engagement create natural connection points. Parents who - are active in schools know each other through meetings, events, and advocacy. - - - 3. **Ideological alignment**: On a polarizing issue like book bans, political ideology, - religious affiliation, parental control preferences, and LGBTQ acceptance will create - strong homophily - people will cluster with those who share their views. - - - 4. **Children''s ages**: Parents with similarly-aged children meet through school - grades, playgroups, and extracurricular activities. - - - 5. **Hub identification**: Highly engaged school board members and PTA members will - be natural connectors, as will those with high community involvement and many children - (more touchpoints with other families). - - - 6. **Edge types**: We''ll distinguish between close allies (share ideology + school - type), school contacts (same school/PTA but different views), neighborhood acquaintances - (geographic proximity), and advocacy partners (same school board engagement level). - - - 7. **Influence asymmetry**: School board engagement level creates authority, PTA - membership gives influence on school matters, number of children increases network - reach, and community involvement level drives opinion leadership. - - - Given the small size (10 people) in a single suburban community around a polarizing - issue, I expect high average degree (30-40) as people will know most others, but - relationships will vary in quality based on alignment.' diff --git a/test-v2-e2e/extended.persona.yaml b/test-v2-e2e/extended.persona.yaml deleted file mode 100644 index 03320f5..0000000 --- a/test-v2-e2e/extended.persona.yaml +++ /dev/null @@ -1,851 +0,0 @@ -population_description: 10 US suburban homeowners with school-age children + Local - school board book ban decision -created_at: '2026-02-15T23:45:42.186580' -intro_template: I'm a {age}-year-old {gender} living in {state}, where I've been a - homeowner for {homeownership_years} years. I'm {marital_status} with {num_children} - school-age children attending {school_type} school, and my kids' education is one - of my top priorities. My political views lean {political_ideology}, and I identify - as {religious_affiliation}. I've been paying close attention to what's happening - with our local school board lately. -treatments: -- attribute: age - treatment: concrete - group: basic_identity -- attribute: gender - treatment: concrete - group: basic_identity -- attribute: state - treatment: concrete - group: basic_identity -- attribute: urban_rural - treatment: concrete - group: basic_identity -- attribute: education_level - treatment: concrete - group: basic_identity -- attribute: marital_status - treatment: concrete - group: basic_identity -- attribute: household_size - treatment: concrete - group: household_composition -- attribute: employment_status - treatment: concrete - group: work_and_income -- attribute: num_children - treatment: concrete - group: household_composition -- attribute: community_involvement - treatment: relative - group: civic_engagement -- attribute: openness - treatment: relative - group: personality -- attribute: conscientiousness - treatment: relative - group: personality -- attribute: extraversion - treatment: relative - group: personality -- attribute: agreeableness - treatment: relative - group: personality -- attribute: neuroticism - treatment: relative - group: personality -- attribute: risk_tolerance - treatment: relative - group: personality -- attribute: education_priority - treatment: concrete - group: values_and_priorities -- attribute: environmental_consciousness - treatment: relative - group: values_and_priorities -- attribute: occupation_category - treatment: concrete - group: work_and_income -- attribute: individual_income - treatment: concrete - group: work_and_income -- attribute: household_income - treatment: concrete - group: work_and_income -- attribute: homeownership_years - treatment: concrete - group: housing_and_assets -- attribute: home_value - treatment: concrete - group: housing_and_assets -- attribute: mortgage_status - treatment: concrete - group: housing_and_assets -- attribute: youngest_child_age - treatment: concrete - group: household_composition -- attribute: oldest_child_age - treatment: concrete - group: household_composition -- attribute: school_type - treatment: concrete - group: education_and_parenting -- attribute: dual_income_household - treatment: concrete - group: work_and_income -- attribute: commute_time - treatment: concrete - group: work_and_income -- attribute: vehicle_count - treatment: concrete - group: housing_and_assets -- attribute: pta_membership - treatment: concrete - group: civic_engagement -- attribute: home_office - treatment: concrete - group: housing_and_assets -- attribute: household_savings - treatment: concrete - group: housing_and_assets -- attribute: childcare_expense - treatment: concrete - group: household_composition -- attribute: extracurricular_count - treatment: concrete - group: education_and_parenting -- attribute: religious_affiliation - treatment: concrete - group: values_and_priorities -- attribute: political_ideology - treatment: concrete - group: values_and_priorities -- attribute: free_speech_absolutism - treatment: relative - group: book_ban_perspectives -- attribute: trust_in_institutions - treatment: relative - group: civic_engagement -- attribute: has_school_age_children - treatment: concrete - group: household_composition -- attribute: school_board_engagement - treatment: relative - group: civic_engagement -- attribute: media_consumption_primary - treatment: concrete - group: values_and_priorities -- attribute: parental_control_preference - treatment: concrete - group: book_ban_perspectives -- attribute: library_usage_frequency - treatment: concrete - group: education_and_parenting -- attribute: personal_book_ban_experience - treatment: concrete - group: book_ban_perspectives -- attribute: lgbtq_acceptance - treatment: relative - group: book_ban_perspectives -- attribute: age_appropriateness_concern - treatment: relative - group: book_ban_perspectives -- attribute: social_network_density - treatment: relative - group: civic_engagement -groups: -- name: basic_identity - label: About Me - attributes: - - age - - gender - - state - - urban_rural - - education_level - - marital_status -- name: household_composition - label: My Family - attributes: - - household_size - - num_children - - youngest_child_age - - oldest_child_age - - childcare_expense - - has_school_age_children -- name: work_and_income - label: Work & Finances - attributes: - - employment_status - - occupation_category - - individual_income - - household_income - - dual_income_household - - commute_time -- name: housing_and_assets - label: Home & Assets - attributes: - - homeownership_years - - home_value - - mortgage_status - - vehicle_count - - home_office - - household_savings -- name: education_and_parenting - label: Education & Parenting - attributes: - - school_type - - extracurricular_count - - library_usage_frequency -- name: values_and_priorities - label: Values & Beliefs - attributes: - - education_priority - - environmental_consciousness - - religious_affiliation - - political_ideology - - media_consumption_primary -- name: civic_engagement - label: Community Engagement - attributes: - - community_involvement - - pta_membership - - trust_in_institutions - - school_board_engagement - - social_network_density -- name: personality - label: My Personality - attributes: - - openness - - conscientiousness - - extraversion - - agreeableness - - neuroticism - - risk_tolerance -- name: book_ban_perspectives - label: Views on Book Access & Content - attributes: - - free_speech_absolutism - - parental_control_preference - - personal_book_ban_experience - - lgbtq_acceptance - - age_appropriateness_concern -phrasings: - boolean: - - attribute: dual_income_household - true_phrase: Both my spouse and I work - false_phrase: Only one of us works in my household - - attribute: pta_membership - true_phrase: I am a member of the PTA - false_phrase: I am not a member of the PTA - - attribute: home_office - true_phrase: I have a dedicated home office space - false_phrase: I don't have a dedicated home office space - - attribute: has_school_age_children - true_phrase: I have school-age children who could be affected by this book ban - decision - false_phrase: I don't have school-age children who would be affected by this book - ban decision - - attribute: personal_book_ban_experience - true_phrase: I have personal experience with book restrictions or censorship in - my own education or parenting - false_phrase: I don't have personal experience with book restrictions or censorship - in my own education or parenting - categorical: - - attribute: gender - phrases: - male: I'm a dad - female: I'm a mom - null_options: [] - null_phrase: null - fallback: null - - attribute: state - phrases: - CA: I live in California - TX: I live in Texas - FL: I live in Florida - NY: I live in New York - PA: I live in Pennsylvania - IL: I live in Illinois - OH: I live in Ohio - GA: I live in Georgia - NC: I live in North Carolina - MI: I live in Michigan - NJ: I live in New Jersey - VA: I live in Virginia - WA: I live in Washington - AZ: I live in Arizona - MA: I live in Massachusetts - TN: I live in Tennessee - IN: I live in Indiana - MO: I live in Missouri - MD: I live in Maryland - WI: I live in Wisconsin - CO: I live in Colorado - MN: I live in Minnesota - SC: I live in South Carolina - AL: I live in Alabama - LA: I live in Louisiana - KY: I live in Kentucky - OR: I live in Oregon - OK: I live in Oklahoma - CT: I live in Connecticut - UT: I live in Utah - IA: I live in Iowa - NV: I live in Nevada - AR: I live in Arkansas - MS: I live in Mississippi - KS: I live in Kansas - NM: I live in New Mexico - NE: I live in Nebraska - WV: I live in West Virginia - ID: I live in Idaho - HI: I live in Hawaii - NH: I live in New Hampshire - ME: I live in Maine - MT: I live in Montana - RI: I live in Rhode Island - DE: I live in Delaware - SD: I live in South Dakota - ND: I live in North Dakota - AK: I live in Alaska - VT: I live in Vermont - WY: I live in Wyoming - null_options: [] - null_phrase: null - fallback: null - - attribute: urban_rural - phrases: - suburban: My family lives in the suburbs - null_options: [] - null_phrase: null - fallback: null - - attribute: education_level - phrases: - high_school: I finished high school - associate: I have an associate's degree - bachelor: I have a bachelor's degree - graduate: I have a graduate degree - null_options: [] - null_phrase: null - fallback: null - - attribute: marital_status - phrases: - married: I'm married - divorced: I'm divorced - single: I'm single - widowed: I'm widowed - null_options: [] - null_phrase: null - fallback: null - - attribute: employment_status - phrases: - full_time: I work full-time - part_time: I work part-time - self_employed: I'm self-employed - unemployed: I'm not currently employed - null_options: [] - null_phrase: null - fallback: null - - attribute: community_involvement - phrases: - high: I'm very active in my community and neighborhood - medium: I'm moderately involved in community activities - low: I occasionally participate in neighborhood events - none: I don't really get involved in community activities - null_options: - - none - null_phrase: I don't really get involved in community activities - fallback: null - - attribute: risk_tolerance - phrases: - low: I prefer to play it safe and avoid risk - medium: I'm comfortable with a moderate level of risk - high: I'm willing to take significant risks - null_options: [] - null_phrase: null - fallback: null - - attribute: education_priority - phrases: - low: My kids' education isn't my top priority - medium: Education is important to me, but it's balanced with other priorities - high: My children's education is one of my highest priorities - null_options: [] - null_phrase: null - fallback: null - - attribute: environmental_consciousness - phrases: - low: Environmental issues aren't really on my radar - medium: I care about the environment and try to make sustainable choices when - I can - high: Environmental sustainability is very important to me and guides many of - my decisions - null_options: [] - null_phrase: null - fallback: null - - attribute: occupation_category - phrases: - professional: I work in a professional field like law, medicine, or engineering - management: I'm in a management role - service: I work in the service industry - sales: I'm in sales - trades: I work in a skilled trade - administrative: I do administrative work - null_options: [] - null_phrase: null - fallback: null - - attribute: mortgage_status - phrases: - paying_mortgage: I'm still paying off my mortgage - paid_off: I've paid off my mortgage - null_options: [] - null_phrase: null - fallback: null - - attribute: school_type - phrases: - public: My kids go to public school - private: My kids attend private school - charter: My kids go to a charter school - homeschool: I homeschool my children - null_options: [] - null_phrase: null - fallback: null - - attribute: religious_affiliation - phrases: - christian_conservative: I'm a conservative Christian - christian_moderate: I'm a Christian with moderate views - christian_progressive: I'm a progressive Christian - other_religion: I practice a non-Christian faith - secular: I'm secular but not particularly focused on religion - none: I don't identify with any religion - null_options: - - none - null_phrase: I don't identify with any religion - fallback: null - - attribute: political_ideology - phrases: - very_conservative: I'm very conservative politically - conservative: I'm conservative - moderate: I'm politically moderate - liberal: I'm liberal - very_liberal: I'm very liberal politically - null_options: [] - null_phrase: null - fallback: null - - attribute: free_speech_absolutism - phrases: - absolutist: I believe in absolute free speech with virtually no restrictions - strong: I strongly support free speech, though I recognize some limits may be - necessary - moderate: I balance free speech with the need for some content curation - limited: I think there should be significant limits on speech to protect people - from harm - null_options: [] - null_phrase: null - fallback: null - - attribute: trust_in_institutions - phrases: - very_high: I have a great deal of trust in public institutions like schools - and government - high: I generally trust public institutions - moderate: I have mixed feelings about public institutions — some trust, some - skepticism - low: I don't have much trust in public institutions - very_low: I have very little trust in schools and government - null_options: [] - null_phrase: null - fallback: null - - attribute: school_board_engagement - phrases: - very_active: I'm very engaged with the school board — I attend almost every - meeting and speak regularly - active: I actively follow school board decisions and attend meetings regularly - occasional: I check in on school board activities occasionally - minimal: I pay minimal attention to what the school board does - none: I don't engage with the school board at all - null_options: - - none - null_phrase: I don't engage with the school board at all - fallback: null - - attribute: media_consumption_primary - phrases: - social_media: I get most of my news from social media - cable_news: I primarily watch cable news - local_news: I mainly follow local news sources - national_newspapers: I read national newspapers like the Times or the Post - online_news: I get my news from online news websites - null_options: [] - null_phrase: null - fallback: null - - attribute: parental_control_preference - phrases: - parents_only: I believe parents alone should control what their children read - parents_primary: I think parents should have primary control, with some input - from educators - shared_responsibility: I believe it should be a shared responsibility between - parents and educators - educators_primary: I think educators should have primary control, with parents - having input - educators_only: I believe professional educators should decide what's appropriate - for students - null_options: [] - null_phrase: null - fallback: null - - attribute: library_usage_frequency - phrases: - weekly: My family and I use the library every week - monthly: We visit the library about once a month - occasionally: We go to the library every now and then - rarely: We rarely use the library - never: We don't use the library - null_options: - - never - null_phrase: We don't use the library - fallback: null - - attribute: lgbtq_acceptance - phrases: - very_accepting: I'm very accepting of LGBTQ individuals and topics - accepting: I'm accepting of LGBTQ people and issues - neutral: I'm neutral on LGBTQ matters - resistant: I'm uncomfortable with LGBTQ topics, especially around children - very_resistant: I'm very opposed to LGBTQ content and discussions - null_options: [] - null_phrase: null - fallback: null - - attribute: age_appropriateness_concern - phrases: - very_concerned: I'm very concerned about age-appropriate content in school materials - concerned: I'm concerned about ensuring school materials are age-appropriate - moderate: I have moderate concerns about age-appropriateness - relaxed: I'm fairly relaxed about what's age-appropriate - very_relaxed: I'm not worried about age-appropriateness — I trust kids can handle - challenging material - null_options: [] - null_phrase: null - fallback: null - - attribute: social_network_density - phrases: - very_high: I have a very strong network of local parents and neighbors — we're - all connected - high: I have a solid network of parents and community members I'm in touch with - moderate: I know some other parents and neighbors, but we're not super connected - low: I don't know many other parents or neighbors well - isolated: I feel pretty isolated — I don't really have a local parent network - null_options: [] - null_phrase: null - fallback: null - relative: - - attribute: community_involvement - labels: - much_below: I'm far less involved in community activities than most people - below: I participate in community activities less than most - average: I'm about as involved in community activities as the average person - above: I'm more engaged in community activities than most people - much_above: I'm far more active in community activities than most people - - attribute: openness - labels: - much_below: I'm much less open to new experiences than most people - below: I'm somewhat less curious and open-minded than most - average: I'm about as open to new ideas as the average person - above: I'm more intellectually curious and open than most people - much_above: I'm far more open to experiences and ideas than most people - - attribute: conscientiousness - labels: - much_below: I'm far less organized and disciplined than most people - below: I'm somewhat less conscientious than most - average: I'm about as organized and dependable as the average person - above: I'm more organized and self-disciplined than most people - much_above: I'm far more conscientious and dependable than most people - - attribute: extraversion - labels: - much_below: I'm far more introverted than most people - below: I'm somewhat less outgoing than most - average: I'm about as extraverted as the average person - above: I'm more sociable and outgoing than most people - much_above: I'm far more extraverted and energetic than most people - - attribute: agreeableness - labels: - much_below: I'm much less agreeable and cooperative than most people - below: I'm somewhat less trusting and compassionate than most - average: I'm about as agreeable as the average person - above: I'm more cooperative and compassionate than most people - much_above: I'm far more agreeable and trusting than most people - - attribute: neuroticism - labels: - much_below: I'm far more emotionally stable than most people - below: I'm somewhat less anxious than most - average: I'm about as emotionally reactive as the average person - above: I'm more prone to anxiety than most people - much_above: I'm far more emotionally sensitive than most people - - attribute: risk_tolerance - labels: - much_below: I'm far more risk-averse than most people - below: I'm somewhat more cautious than most - average: I have about the same risk tolerance as the average person - above: I'm more willing to take risks than most people - much_above: I'm far more risk-tolerant than most people - - attribute: environmental_consciousness - labels: - much_below: I'm far less concerned about environmental issues than most people - below: I'm somewhat less environmentally conscious than most - average: I'm about as concerned about the environment as the average person - above: I'm more environmentally conscious than most people - much_above: I'm far more concerned about environmental issues than most people - - attribute: free_speech_absolutism - labels: - much_below: I believe in content curation far more than most people - below: I'm somewhat less absolutist about free speech than most - average: I have about the same views on free speech as the average person - above: I'm more of a free speech absolutist than most people - much_above: I believe in absolute free speech far more strongly than most people - - attribute: trust_in_institutions - labels: - much_below: I trust institutions far less than most people - below: I'm more skeptical of institutions than most - average: I'm about as trusting of institutions as the average person - above: I generally trust institutions more than most - much_above: I have much more faith in institutions than most people - - attribute: school_board_engagement - labels: - much_below: I'm far less engaged with the school board than most people - below: I attend school board meetings less than most - average: I'm about as engaged with the school board as the average person - above: I'm more active with the school board than most people - much_above: I'm far more engaged with the school board than most people - - attribute: lgbtq_acceptance - labels: - much_below: I'm far less accepting of LGBTQ topics than most people - below: I'm somewhat less accepting of LGBTQ issues than most - average: I'm about as accepting of LGBTQ topics as the average person - above: I'm more accepting of LGBTQ individuals than most people - much_above: I'm far more accepting of LGBTQ topics than most people - - attribute: age_appropriateness_concern - labels: - much_below: I'm far less concerned about age-appropriate content than most people - below: I'm somewhat less worried about age-appropriateness than most - average: I have about the same concerns about age-appropriate content as the - average person - above: I'm more concerned about age-appropriate materials than most people - much_above: I'm far more concerned about age-appropriate content than most people - - attribute: social_network_density - labels: - much_below: I have far fewer local parent connections than most people - below: My parent network is somewhat smaller than most - average: I have about the same size parent network as the average person - above: I have more local parent connections than most people - much_above: I have a far larger parent network than most people - concrete: - - attribute: age - template: I'm {value} years old - format_spec: .0f - prefix: '' - suffix: '' - - attribute: household_size - template: There are {value} people in my household - format_spec: .0f - prefix: '' - suffix: '' - - attribute: num_children - template: I have {value} school-age children - format_spec: .0f - prefix: '' - suffix: '' - - attribute: individual_income - template: I earn {value} per year - format_spec: .2f - prefix: $ - suffix: '' - - attribute: household_income - template: Our household brings in {value} annually - format_spec: .2f - prefix: $ - suffix: '' - - attribute: homeownership_years - template: We've owned our home for {value} years - format_spec: .0f - prefix: '' - suffix: '' - - attribute: home_value - template: Our home is valued at {value} - format_spec: .2f - prefix: $ - suffix: '' - - attribute: youngest_child_age - template: My youngest child is {value} years old - format_spec: .0f - prefix: '' - suffix: '' - - attribute: oldest_child_age - template: My oldest child is {value} years old - format_spec: .0f - prefix: '' - suffix: '' - - attribute: commute_time - template: My commute takes {value} each way - format_spec: .0f - prefix: '' - suffix: ' minutes' - - attribute: vehicle_count - template: We own {value} vehicles - format_spec: .0f - prefix: '' - suffix: '' - - attribute: household_savings - template: We have {value} in savings and investments - format_spec: .2f - prefix: $ - suffix: '' - - attribute: childcare_expense - template: I spend {value} per month on childcare - format_spec: .2f - prefix: $ - suffix: '' - - attribute: extracurricular_count - template: My kids are enrolled in {value} extracurricular activities - format_spec: .0f - prefix: '' - suffix: '' -population_stats: - stats: - age: - mean: 30.510204081632654 - std: 17.812825960736355 - min: 4.0 - max: 56.0 - count: 49.0 - agreeableness: - mean: 3.742296609197454 - std: 0.6021187408907889 - min: 2.4993583967379003 - max: 5.0 - count: 49.0 - conscientiousness: - mean: 3.679869888537246 - std: 0.6199907438886438 - min: 2.2281225906517585 - max: 5.0 - count: 49.0 - commute_time: - mean: 30.632653061224488 - std: 17.444786563076445 - min: 5.0 - max: 87.0 - count: 49.0 - extraversion: - mean: 3.324855736233059 - std: 0.7147824100658166 - min: 1.7592498627620068 - max: 4.862375789876565 - count: 49.0 - homeownership_years: - mean: 14.183673469387756 - std: 9.15986141950247 - min: 1.0 - max: 33.0 - count: 49.0 - household_size: - mean: 4.1020408163265305 - std: 0.8476658628056143 - min: 2.0 - max: 5.0 - count: 49.0 - dual_income_household: - mean: 0.5102040816326531 - std: 0.5050762722761054 - min: 0.0 - max: 1.0 - count: 49.0 - neuroticism: - mean: 2.963042481081652 - std: 0.8074593016284259 - min: 1.0 - max: 4.504133788459496 - count: 49.0 - num_children: - mean: 2.0408163265306123 - std: 0.7059031943035045 - min: 1.0 - max: 4.0 - count: 49.0 - has_school_age_children: - mean: 1.0 - std: 0.0 - min: 1.0 - max: 1.0 - count: 49.0 - individual_income: - mean: 71107.22448979592 - std: 32871.254997252356 - min: 25000.0 - max: 147817.0 - count: 49.0 - household_income: - mean: 112427.22448979592 - std: 35753.11161240917 - min: 69291.0 - max: 179598.0 - count: 49.0 - extracurricular_count: - mean: 3.7142857142857144 - std: 2.1505813167606567 - min: 0.0 - max: 8.0 - count: 49.0 - household_savings: - mean: 95363.26530612246 - std: 78952.96873133173 - min: 6574.0 - max: 270409.0 - count: 49.0 - openness: - mean: 3.4978221478343148 - std: 0.678771250007823 - min: 2.300849868966461 - max: 5.0 - count: 49.0 - personal_book_ban_experience: - mean: 0.20408163265306123 - std: 0.40720550896397784 - min: 0.0 - max: 1.0 - count: 49.0 - pta_membership: - mean: 0.5102040816326531 - std: 0.5050762722761054 - min: 0.0 - max: 1.0 - count: 49.0 - home_value: - mean: 507553.8775510204 - std: 204733.88463814766 - min: 230281.0 - max: 964952.0 - count: 49.0 - home_office: - mean: 0.5714285714285714 - std: 0.5 - min: 0.0 - max: 1.0 - count: 49.0 - vehicle_count: - mean: 2.0816326530612246 - std: 0.9538678868576252 - min: 1.0 - max: 5.0 - count: 49.0 - youngest_child_age: - mean: 7.8979591836734695 - std: 3.336945321942308 - min: 5.0 - max: 15.0 - count: 49.0 - childcare_expense: - mean: 1122.6530612244899 - std: 1001.612586262365 - min: 0.0 - max: 2500.0 - count: 49.0 - oldest_child_age: - mean: 13.571428571428571 - std: 2.516611478423583 - min: 7.0 - max: 18.0 - count: 49.0 diff --git a/test-v2-e2e/extended.scenario.yaml b/test-v2-e2e/extended.scenario.yaml deleted file mode 100644 index b39edf2..0000000 --- a/test-v2-e2e/extended.scenario.yaml +++ /dev/null @@ -1,276 +0,0 @@ -meta: - name: local_school_board_book - description: Local school board book ban decision - population_spec: extended.yaml - study_db: study.db - population_id: default - network_id: default - created_at: '2026-02-15T23:47:47.239185' -event: - type: announcement - content: The local school board has announced its decision to ban a list of books - from school libraries and classrooms. The board voted to remove several titles - that some community members challenged as containing inappropriate content. The - banned books will be pulled from circulation in all district schools effective - immediately. The school board stated this decision was made after reviewing community - feedback and considering age-appropriateness of materials for K-12 students. Parents - and community members can review the full list of banned titles on the district - website. - source: Local School Board - credibility: 0.95 - ambiguity: 0.3 - emotional_valence: -0.3 -timeline: -- timestep: 6 - event: - type: announcement - content: A coalition of parents, students, and civil liberties advocates gathered - outside the district office to protest the book ban decision. Protesters carried - signs reading 'Books Not Bans' and 'Let Kids Read' while several students gave - speeches about the importance of access to diverse literature. Organizers announced - plans for a community read-in event featuring the banned books and called on - the school board to reverse its decision. The protest drew approximately 150 - participants. - source: Local News Outlet - credibility: 0.9 - ambiguity: 0.3 - emotional_valence: 0.4 - exposure_rules: null - description: Local parents and students organize protest at district office -- timestep: 15 - event: - type: announcement - content: A group calling itself 'Parents for Safe Schools' has launched a petition - supporting the school board's book ban decision, gathering over 500 signatures. - The petition states that parents have the right to protect children from sexually - explicit and age-inappropriate content in schools. The group's spokesperson - issued a statement praising the school board for listening to community concerns - and prioritizing student safety. They announced plans to attend the next board - meeting to express continued support for the ban. - source: Community Facebook Group - credibility: 0.65 - ambiguity: 0.3 - emotional_valence: -0.5 - exposure_rules: null - description: Counter-petition from parents supporting the ban circulates -- timestep: 24 - event: - type: announcement - content: The American Civil Liberties Union (ACLU) has announced it will file - a lawsuit challenging the school district's book ban as unconstitutional censorship. - The organization stated the ban violates students' First Amendment rights to - access information and targets books with LGBTQ+ themes and diverse perspectives. - The ACLU is representing five local families and two students in the case. Legal - experts quoted in the announcement noted that similar book bans in other districts - have been overturned in federal court. - source: ACLU Press Release - credibility: 0.95 - ambiguity: 0.3 - emotional_valence: 0.6 - exposure_rules: null - description: National civil liberties organization announces legal challenge -- timestep: 36 - event: - type: announcement - content: Local police confirmed they are investigating threats made against school - board members who voted for the book ban. Board president stated that several - members have received harassing phone calls and threatening emails from individuals - on both sides of the issue. The district has increased security at schools and - board meetings. The board president called for civility and respectful dialogue, - stating that threats and harassment have no place in community discourse regardless - of one's position on the book ban. - source: Local News Outlet - credibility: 0.9 - ambiguity: 0.3 - emotional_valence: -0.7 - exposure_rules: null - description: School board members receive threats, security concerns raised -- timestep: 48 - event: - type: announcement - content: In response to ongoing controversy and the pending lawsuit, the school - board has announced formation of a diverse review committee to reconsider the - book ban policy. The committee will include parents, teachers, librarians, students, - and community members representing different perspectives. Board members stated - they remain committed to age-appropriate content while also respecting intellectual - freedom. The committee will review each banned title individually and make recommendations - within 60 days. The announcement represents a potential compromise as tensions - in the community remain high. - source: Local School Board - credibility: 0.95 - ambiguity: 0.3 - emotional_valence: 0.2 - exposure_rules: null - description: School board announces review committee to reconsider decision -seed_exposure: - channels: - - name: district_email - description: Official email from school district to parents about the book ban - decision - reach: broadcast - credibility_modifier: 1.25 - experience_template: null - - name: local_news - description: Local news coverage of the school board decision on TV and newspaper - reach: broadcast - credibility_modifier: 1.1 - experience_template: null - - name: parent_network - description: Word of mouth and text messages spreading through parent networks - and PTA groups - reach: organic - credibility_modifier: 0.9 - experience_template: null - - name: social_media - description: Social media posts and discussions about the book ban on Facebook, - Twitter, and community groups - reach: targeted - credibility_modifier: 0.75 - experience_template: null - - name: school_board_meeting - description: Direct attendance or awareness from school board meeting where decision - was announced - reach: targeted - credibility_modifier: 1.3 - experience_template: null - rules: - - channel: school_board_meeting - when: school_board_engagement in ['very_active', 'active'] - probability: 1.0 - timestep: 0 - - channel: district_email - when: school_type == 'public' and pta_membership == True - probability: 0.95 - timestep: 0 - - channel: district_email - when: school_type == 'public' and pta_membership == False - probability: 0.75 - timestep: 0 - - channel: local_news - when: media_consumption_primary in ['local_news', 'cable_news', 'national_newspapers'] - probability: 0.85 - timestep: 1 - - channel: parent_network - when: social_network_density in ['very_high', 'high'] and pta_membership == True - probability: 0.9 - timestep: 1 - - channel: parent_network - when: social_network_density in ['moderate', 'low'] - probability: 0.6 - timestep: 2 - - channel: social_media - when: media_consumption_primary == 'social_media' and age < 50 - probability: 0.8 - timestep: 1 - - channel: social_media - when: media_consumption_primary == 'social_media' and age >= 50 - probability: 0.7 - timestep: 2 - - channel: social_media - when: community_involvement in ['high', 'medium'] - probability: 0.65 - timestep: 2 - - channel: local_news - when: media_consumption_primary == 'online_news' - probability: 0.7 - timestep: 2 -interaction: - primary_model: broadcast_response - secondary_model: direct_conversation - description: This is a controversial local school board announcement that directly - affects all parents in the population. The interaction begins with broadcast_response - as parents learn about the official school board decision and form initial reactions. - This quickly transitions to direct_conversation as parents discuss their opinions - with other parents through various social connections (PTA meetings, school pickups, - neighborhood conversations, etc.). The topic is highly emotionally charged and - personally relevant, prompting extensive discussion within parent networks. -spread: - share_probability: 0.65 - share_modifiers: - - when: pta_membership == True - multiply: 1.4 - add: 0.0 - - when: school_board_engagement == 'high' - multiply: 1.5 - add: 0.0 - - when: free_speech_absolutism == 'high' - multiply: 1.6 - add: 0.0 - - when: parental_control_preference == 'high' - multiply: 1.4 - add: 0.0 - - when: extraversion > 0.6 - multiply: 1.3 - add: 0.0 - - when: neuroticism > 0.6 - multiply: 1.2 - add: 0.05 - - when: edge_type == 'school_parent' - multiply: 1.6 - add: 0.0 - - when: edge_type == 'pta_colleague' - multiply: 1.7 - add: 0.0 - - when: edge_type == 'school_board_activist' - multiply: 1.8 - add: 0.0 - - when: edge_type == 'household' - multiply: 1.9 - add: 0.0 - - when: edge_type == 'neighbor' - multiply: 1.3 - add: 0.0 - - when: edge_type == 'political_ally' - multiply: 1.4 - add: 0.0 - - when: political_ideology == 'very_liberal' or political_ideology == 'very_conservative' - multiply: 1.3 - add: 0.0 - - when: community_involvement == 'high' - multiply: 1.3 - add: 0.0 - - when: library_usage_frequency == 'high' - multiply: 1.3 - add: 0.0 - - when: age_appropriateness_concern == 'high' - multiply: 1.25 - add: 0.0 - decay_per_hop: 0.08 - max_hops: 5 -outcomes: - suggested_outcomes: - - name: response_to_book_ban - type: categorical - description: How suburban homeowners with school-age children respond to the school - board's decision to ban books from district schools - options: - - actively_oppose_ban - - support_ban_publicly - - provide_banned_books_privately - - disengage_from_issue - range: null - required: true - option_friction: null - - name: sentiment - type: float - description: Overall sentiment toward the event (-1 very negative to 1 very positive) - options: null - range: - - -1.0 - - 1.0 - required: true - option_friction: null - capture_full_reasoning: true - extraction_instructions: null - decision_relevant_attributes: [] -simulation: - max_timesteps: 50 - timestep_unit: hour - stop_conditions: - - exposure_rate > 0.95 and no_state_changes_for > 10 - seed: null -background_context: The community is experiencing heightened political polarization - around education policy and parental rights. Recent national debates about curriculum - content and book censorship have intensified local activism on both sides of these - cultural issues. -relationship_weights: null diff --git a/test-v2-e2e/extended.yaml b/test-v2-e2e/extended.yaml deleted file mode 100644 index 63e908c..0000000 --- a/test-v2-e2e/extended.yaml +++ /dev/null @@ -1,2930 +0,0 @@ -meta: - description: 10 US suburban homeowners with school-age children + Local school board - book ban decision - size: 10 - geography: US - agent_focus: suburban families with school-age children - created_at: '2026-02-15T23:12:13.154770' - version: '1.0' - persona_template: null - scenario_description: Local school board book ban decision - household_config: - age_brackets: - - - 25 - - 18-24 - - - 35 - - 25-34 - - - 45 - - 35-44 - - - 65 - - 45-64 - - - 999 - - 65+ - household_type_weights: - 18-24: - single: 0.28 - couple: 0.15 - single_parent: 0.14 - couple_with_kids: 0.15 - multi_generational: 0.28 - 25-34: - single: 0.19 - couple: 0.11 - single_parent: 0.19 - couple_with_kids: 0.38 - multi_generational: 0.13 - 35-44: - single: 0.1 - couple: 0.1 - single_parent: 0.18 - couple_with_kids: 0.52 - multi_generational: 0.1 - 45-64: - single: 0.26 - couple: 0.37 - single_parent: 0.06 - couple_with_kids: 0.21 - multi_generational: 0.1 - 65+: - single: 0.42 - couple: 0.44 - single_parent: 0.01 - couple_with_kids: 0.03 - multi_generational: 0.1 - same_group_rates: - white: 0.91 - black: 0.82 - hispanic: 0.75 - asian: 0.69 - default_same_group_rate: 0.8 - assortative_mating: - education_level: 0.65 - religious_affiliation: 0.7 - income_level: 0.58 - partner_age_gap_mean: 2.2 - partner_age_gap_std: 4.5 - min_adult_age: 18 - child_min_parent_offset: 18 - child_max_parent_offset: 45 - max_dependent_child_age: 18 - elderly_min_offset: 20 - elderly_max_offset: 50 - life_stages: - - max_age: 5 - label: preschool - - max_age: 11 - label: elementary - - max_age: 14 - label: middle_school - - max_age: 18 - label: high_school - adult_stage_label: adult - avg_household_size: 3.2 -grounding: - overall: medium - sources_count: 308 - strong_count: 17 - medium_count: 25 - low_count: 6 - sources: - - https://www.pewresearch.org/religion/2025/02/26/religion-and-views-on-lgbtq-issues-and-abortion/ - - https://www.dol.gov/agencies/wb/data/family-status - - https://fivethirtyeight.com/features/how-suburban-are-big-american-cities/ - - https://nces.ed.gov/programs/edge/Demographic/ACSchildren - - https://www.census.gov/library/stories/2018/07/interracial-marriages.html - - https://www.jstor.org/stable/10.1086/661778 - - https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3571&context=etd - - https://worldpopulationreview.com/state-rankings/median-home-price-by-state - - https://ourpublicservice.org/publications/state-of-trust-in-government-2024/ - - https://www.bls.gov/news.release/famee.nr0.htm - - https://worldpopulationreview.com/state-rankings/average-household-size-by-state - - https://academic.oup.com/pnasnexus/article/3/6/pgae197/7689238 - - https://www.jstor.org/stable/2095670 - - https://fred.stlouisfed.org/series/PAYEMS - - https://nces.ed.gov/surveys/frss/publications/96913/index.asp?sectionid=2 - - https://www.nar.realtor/research-and-statistics/housing-statistics/metropolitan-median-area-prices-and-affordability - - https://www.census.gov/data/tables/time-series/demo/families/families.html - - https://www.tandfonline.com/doi/full/10.1080/03004430.2025.2524637 - - https://www.census.gov/library/stories/2022/07/marriage-prevalence-for-black-adults-varies-by-state.html - - https://www.pewresearch.org/social-trends/2017/05/18/1-trends-and-patterns-in-intermarriage/ - - https://www.pewresearch.org/religion/2022/09/13/how-u-s-religious-composition-has-changed-in-recent-decades/ - - https://pen.org/memo-on-school-book-bans-2023-2024-school-year/ - - https://realestate.usnews.com/home-services/moving/median-home-prices-state - - https://www.cnbc.com/2026/02/11/jobs-report-january-2026-.html - - https://datacenter.kidscount.org/data/tables/101-child-population-by-age-group - - https://raleighrealty.com/blog/average-homeownership-length - - https://corridorbusiness.com/study-college-graduates-more-likely-to-own-their-own-home/ - - https://datacenter.aecf.org/data/tables/9450-public-library-usage - - https://www.bls.gov/news.release/pdf/empsit.pdf - - https://www.nmhc.org/research-insight/quick-facts-figures/quick-facts-resident-demographics/households-with-children/ - - https://pacificlegal.org/for-some-school-boards-parents-are-the-enemy/ - - https://pmc.ncbi.nlm.nih.gov/articles/PMC4850739/ - - https://pmc.ncbi.nlm.nih.gov/articles/PMC6927670/ - - https://www.aclu-wa.org/docs/guide-school-board-advocacy-washington - - https://www.lendingtree.com/home/mortgage/u-s-mortgage-market-statistics/ - - https://www.census.gov/content/dam/Census/library/publications/2022/demo/p70-174.pdf - - https://www.pewresearch.org/internet/2016/09/09/library-usage-and-engagement/ - - https://www.rubyhome.com/blog/average-length-homeownership/ - - https://www.childstats.gov/americaschildren23/family1.asp - - https://pubmed.ncbi.nlm.nih.gov/27818530/ - - https://www.statista.com/topics/12130/k-12-education-in-the-united-states/ - - https://www.census.gov/library/working-papers/2023/demo/SEHSD-WP2023-10.html - - https://www.everylibraryinstitute.org/demographics_of_book_bans - - https://www.pewresearch.org/short-reads/2025/08/28/how-the-audiences-of-30-major-news-sources-differ-by-age/ - - https://www.statista.com/statistics/679812/number-of-households-with-children-by-age/ - - https://www.consumeraffairs.com/finance/what-is-the-average-length-of-home-ownership.html - - https://www.urban.org/urban-wire/homeownership-has-fallen-further-out-reach-younger-families-lowest-incomes - - https://www.demandsage.com/average-us-income/ - - https://www.bls.gov/news.release/pdf/famee.pdf - - https://www.editorandpublisher.com/stories/how-the-audiences-of-30-major-news-sources-differ-by-age,257491 - - https://action.everylibrary.org/what_do_people_really_think_about_book_bans - - https://en.wikipedia.org/wiki/Age_disparity_in_sexual_relationships - - https://prri.org/research/lgbtq-rights-across-all-50-states-key-insights-2024-prri-american-values-atlas/ - - https://www.bls.gov/careeroutlook/2017/article/projections-laborforce.htm - - https://www.schoolstatus.com/blog/new-k-12-attendance-data-2024-25 - - https://pmc.ncbi.nlm.nih.gov/articles/PMC12545191/ - - https://www.bls.gov/opub/ted/2012/ted_20120427.htm - - http://blogs.edweek.org/edweek/parentsandthepublic/2013/09/national_survey_examines_parent_engagement_in_education.html - - https://read.dukeupress.edu/demography/article/61/5/1293/390842/Eight-Decades-of-Educational-Assortative-Mating-A - - https://nces.ed.gov/programs/coe/indicator/cba - - https://www.census.gov/data/tables/2022/demo/families/cps-2022.html - - https://www.statista.com/statistics/183657/average-size-of-a-family-in-the-us/ - - https://www.bls.gov/news.release/empsit.nr0.htm - - https://www.clasp.org/sites/default/files/public/resources-and-publications/states/0086.pdf - - https://datacenter.aecf.org/data/tables/4404-income--median-earnings-for-population-age-25-and-over-by-educational-attainment - - https://prri.org/research/2020-census-of-american-religion/ - - https://www.aamchealthjustice.org/news/polling/trust-trends - - https://www.pewresearch.org/short-reads/2022/08/30/more-so-than-adults-u-s-teens-value-people-feeling-safe-online-over-being-able-to-speak-freely/ - - https://www.stanmor.com/blog/the-numbers-behind-homeowners-exploring-homeownership-statistics - - https://www.hrc.org/news/parental-rights-and-involvement-in-students-reading-lives - - https://bookriot.com/book-censorship-trends-2025/ - - https://www2.census.gov/library/publications/2024/demo/p20-587.pdf - - https://www.newyorkfed.org/research/staff_reports/sr682.html - - https://www.quora.com/How-many-U-S-households-have-children-between-the-ages-of-3-and-10 - - https://www.pewresearch.org/internet/2013/05/01/parents-children-libraries-and-reading-3/ - - https://www.census.gov/library/publications/2025/demo/p70-204.html - - https://www.census.gov/data/tables/time-series/demo/income-poverty/cps-hinc/hinc-04.html - - https://www.census.gov/newsroom/press-releases/2022/americas-families-and-living-arrangements.html - - https://www.financialsamurai.com/the-median-homeownership-duration-is-too-short-to-build-real-wealth/ - - https://www.themortgagenote.org/more-americans-are-mortgage-free-homeowners/ - - https://www.edweek.org/leadership/does-parent-involvement-really-help-students-heres-what-the-research-says/2023/07 - - https://www.census.gov/data/tables/2019/demo/families/cps-2019.html - - https://datacenter.aecf.org/data/tables/101-child-population-by-age-group - - https://www.census.gov/library/stories/2023/08/same-sex-married-couples-age-race-ethnicity.html - - https://defendinged.org/resources/its-time-to-get-involved-in-your-school-board/ - - https://www.pewresearch.org/internet/2015/09/15/who-uses-libraries-and-what-they-do-at-their-libraries/ - - https://pen.org/report/the-normalization-of-book-banning/ - - https://www.statista.com/statistics/242028/number-of-married-couples-in-the-us-by-ethnic-group-and-combination/ - 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- https://www.pewresearch.org/religion/2025/02/26/age-race-education-and-other-demographic-traits-of-us-religious-groups/ - - https://www.huduser.gov/portal/pdredge/pdr-edge-frm-asst-sec-080320.html - - https://www.pewresearch.org/social-trends/2018/05/22/demographic-and-economic-trends-in-urban-suburban-and-rural-communities/ - - https://www.housingwire.com/articles/homeowners-are-staying-in-place-twice-as-long-as-they-did-20-years-ago-redfin/ - - https://www.axios.com/2025/11/16/mortgage-free-homes-map - - https://www.psychologytoday.com/us/blog/the-asymmetric-brain/202510/age-gaps-in-relationships-7-scientific-facts - - https://www.bls.gov/news.release/famee.htm - - https://www.nature.com/articles/s41598-017-15864-x -attributes: -- name: age - type: int - category: universal - description: Age of the individual in years - scope: individual - sampling: - strategy: independent - distribution: - type: normal - mean: 42.0 - std: 7.0 - min: 25.0 - max: 65.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: extrapolated - source: https://nces.ed.gov/programs/coe/pdf/Indicator_CCE/coe_cce_2016_05.pdf - note: Extrapolated from data showing parents of school-age children (5-17) tend - to be in their late 30s to mid-40s. Suburban homeowners tend to be slightly - older than average parents. - constraints: - - type: hard_min - value: 25.0 - expression: null - reason: Minimum realistic age for parent with school-age children - - type: hard_max - value: 65.0 - expression: null - reason: Maximum typical age for parent with K-12 children -- name: gender - type: categorical - category: universal - description: Gender identity of the individual - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - male - - female - weights: - - 0.48 - - 0.52 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: strong - method: researched - source: https://www.census.gov/library/stories/2024/11/family-households.html - note: General US population gender distribution, slightly more females in household - reference persons - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: state - type: categorical - category: universal - description: US state where the household is located - scope: household - sampling: - strategy: independent - distribution: - type: categorical - options: - - CA - - TX - - FL - - NY - - PA - - IL - - OH - - GA - - NC - - MI - - NJ - - VA - - WA - - AZ - - MA - - TN - - IN - - MO - - MD - - WI - - CO - - MN - - SC - - AL - - LA - - KY - - OR - - OK - - CT - - UT - - IA - - NV - - AR - - MS - - KS - - NM - - NE - - ID - - WV - - HI - - NH - - ME - - MT - - RI - - DE - - SD - - ND - - AK - - VT - - WY - weights: - - 0.118 - - 0.088 - - 0.065 - - 0.059 - - 0.039 - - 0.038 - - 0.035 - - 0.032 - - 0.032 - - 0.03 - - 0.027 - - 0.026 - - 0.023 - - 0.022 - - 0.021 - - 0.021 - - 0.02 - - 0.019 - - 0.018 - - 0.018 - - 0.017 - - 0.017 - - 0.016 - - 0.015 - - 0.014 - - 0.013 - - 0.013 - - 0.012 - - 0.011 - - 0.01 - - 0.01 - - 0.009 - - 0.009 - - 0.009 - - 0.008 - - 0.006 - - 0.006 - - 0.005 - - 0.004 - - 0.004 - - 0.004 - - 0.003 - - 0.003 - - 0.003 - - 0.003 - - 0.002 - - 0.002 - - 0.002 - - 0.002 - - 0.002 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: strong - method: researched - source: https://www.census.gov/data/tables/time-series/demo/families/households.html - note: Based on US population distribution by state from Census data - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: urban_rural - type: categorical - category: universal - description: Whether the household is in a suburban area (all suburban for this - population) - scope: household - sampling: - strategy: independent - distribution: - type: categorical - options: - - suburban - weights: - - 1.0 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: strong - method: researched - source: https://en.wikipedia.org/wiki/Homeownership_in_the_United_States - note: All agents in this population are suburban by specification. Three quarters - of suburban households are homeowners. - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: education_level - type: categorical - category: universal - description: Highest level of education completed (high school, associate, bachelor, - graduate) - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - high_school - - associate - - bachelor - - graduate - weights: - - 0.15 - - 0.25 - - 0.4 - - 0.2 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: strong - method: researched - source: https://www.point2homes.com/news/us-real-estate-news/homeownership-by-education-us.html - note: 'Based on homeowner education distribution: 70% have some college or more, - with bachelor''s degree holders at 40%. Suburban homeowners skew higher education - than general population.' - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: marital_status - type: categorical - category: universal - description: Marital status of the individual (married, divorced, single, widowed) - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - married - - divorced - - single - - widowed - weights: - - 0.75 - - 0.15 - - 0.08 - - 0.02 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: extrapolated - source: https://www.stanmor.com/blog/the-numbers-behind-homeowners-exploring-homeownership-statistics - note: Married couples have highest homeownership rates. Among families with school-age - children, married couples dominate, especially in suburban homeownership. - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: household_size - type: int - category: universal - description: Total number of people living in the household - scope: household - sampling: - strategy: independent - distribution: - type: normal - mean: 4.2 - std: 0.9 - min: 2.0 - max: 8.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: [] - modifiers: [] - grounding: - level: strong - method: researched - source: https://www.statista.com/statistics/183657/average-size-of-a-family-in-the-us/ - note: Average US household with children is larger than overall average (2.61). - Families with school-age children typically have 3-5 members. - constraints: - - type: hard_min - value: 2.0 - expression: null - reason: Minimum household size (parent + child) - - type: hard_max - value: 8.0 - expression: null - reason: Maximum typical household size - - type: expression - value: null - expression: num_children <= household_size - 1 - reason: At least one adult must be present with children -- name: employment_status - type: categorical - category: universal - description: Current employment status (full-time, part-time, self-employed, unemployed) - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - full_time - - part_time - - self_employed - - unemployed - weights: - - 0.72 - - 0.12 - - 0.12 - - 0.04 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: strong - method: researched - source: https://www.bls.gov/news.release/famee.nr0.htm - note: Based on BLS Employment Characteristics of Families 2024 data. Parents with - children have high labor force participation, with most employed full-time. - Homeownership requires stable income. - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: num_children - type: int - category: population_specific - description: Number of school-age children (K-12) in the household - scope: household - sampling: - strategy: independent - distribution: - type: normal - mean: 2.1 - std: 0.8 - min: 1.0 - max: 5.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: [] - modifiers: [] - grounding: - level: strong - method: researched - source: https://www.census.gov/library/stories/2024/11/family-households.html - note: Census data shows 19% of family households have 1 child under 18, 16% have - 2, and 9% have 3+. Average births per woman is 1.7, but families with school-age - children tend toward 2. - constraints: - - type: hard_min - value: 1.0 - expression: null - reason: Population specified as having school-age children - - type: hard_max - value: 5.0 - expression: null - reason: Maximum typical number of school-age children - - type: expression - value: null - expression: num_children <= household_size - 1 - reason: Number of children cannot exceed household size minus adults -- name: community_involvement - type: categorical - category: population_specific - description: Level of involvement in community/neighborhood activities (high, medium, - low, none) - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - high - - medium - - low - - none - weights: - - 0.25 - - 0.4 - - 0.25 - - 0.1 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: low - method: estimated - source: null - note: No specific data found for suburban parents' community involvement. Estimated - based on assumption that suburban homeowners with children have moderate to - high engagement in schools and community activities. - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: openness - type: float - category: personality - description: Big Five trait reflecting openness to experience and intellectual curiosity - scope: individual - sampling: - strategy: independent - distribution: - type: normal - mean: 3.5 - std: 0.7 - min: 1.0 - max: 5.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: extrapolated - source: null - note: Based on Big Five personality trait research showing general population - mean around 3.5 on 5-point scale. Suburban homeowners may be slightly more conventional, - but kept at population mean. - constraints: - - type: hard_min - value: 1.0 - expression: null - reason: Minimum Big Five scale value - - type: hard_max - value: 5.0 - expression: null - reason: Maximum Big Five scale value -- name: conscientiousness - type: float - category: personality - description: Big Five trait reflecting organization, dependability, and self-discipline - scope: individual - sampling: - strategy: independent - distribution: - type: normal - mean: 3.8 - std: 0.6 - min: 1.0 - max: 5.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: extrapolated - source: null - note: Homeownership and parenting typically correlate with higher conscientiousness. - Set slightly above population mean (3.5) to reflect responsibility and organization - required for homeownership. - constraints: - - type: hard_min - value: 1.0 - expression: null - reason: Minimum Big Five scale value - - type: hard_max - value: 5.0 - expression: null - reason: Maximum Big Five scale value -- name: extraversion - type: float - category: personality - description: Big Five trait reflecting sociability, assertiveness, and energy level - scope: individual - sampling: - strategy: independent - distribution: - type: normal - mean: 3.4 - std: 0.75 - min: 1.0 - max: 5.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: extrapolated - source: null - note: Based on Big Five population norms. Suburban parents show normal distribution - of extraversion, kept near population mean of 3.4-3.5. - constraints: - - type: hard_min - value: 1.0 - expression: null - reason: Minimum Big Five scale value - - type: hard_max - value: 5.0 - expression: null - reason: Maximum Big Five scale value -- name: agreeableness - type: float - category: personality - description: Big Five trait reflecting compassion, cooperativeness, and trust in - others - scope: individual - sampling: - strategy: independent - distribution: - type: normal - mean: 3.7 - std: 0.65 - min: 1.0 - max: 5.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: extrapolated - source: null - note: Parents and community-oriented individuals tend to score slightly higher - on agreeableness. Set above population mean (3.5) to reflect cooperative nature - of parenting and suburban community living. - constraints: - - type: hard_min - value: 1.0 - expression: null - reason: Minimum Big Five scale value - - type: hard_max - value: 5.0 - expression: null - reason: Maximum Big Five scale value -- name: neuroticism - type: float - category: personality - description: Big Five trait reflecting emotional stability and tendency toward anxiety - scope: individual - sampling: - strategy: independent - distribution: - type: normal - mean: 3.0 - std: 0.75 - min: 1.0 - max: 5.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: extrapolated - source: null - note: Based on Big Five population norms around 3.0-3.2. Homeowners with stable - employment may have slightly lower neuroticism, but kept near population mean - for realism. - constraints: - - type: hard_min - value: 1.0 - expression: null - reason: Minimum Big Five scale value - - type: hard_max - value: 5.0 - expression: null - reason: Maximum Big Five scale value -- name: risk_tolerance - type: categorical - category: personality - description: Individual's tolerance for financial and lifestyle risk (low, medium, - high) - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - low - - medium - - high - weights: - - 0.35 - - 0.5 - - 0.15 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: low - method: estimated - source: null - note: Estimated based on assumption that suburban homeowners with children tend - toward conservative financial approaches (mortgage, stable neighborhood) but - with normal variation. Skewed toward low-medium risk tolerance. - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: education_priority - type: categorical - category: personality - description: How highly the individual prioritizes children's education (low, medium, - high) - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - low - - medium - - high - weights: - - 0.05 - - 0.3 - - 0.65 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: extrapolated - source: https://nces.ed.gov/programs/coe/pdf/Indicator_CCE/coe_cce_2016_05.pdf - note: Suburban homeowners specifically choose neighborhoods for schools. This - population likely has high education priority given their investment in stable - housing and school districts. Strongly skewed toward high priority. - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: environmental_consciousness - type: categorical - category: personality - description: Level of concern for environmental issues and sustainability (low, - medium, high) - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - low - - medium - - high - weights: - - 0.25 - - 0.5 - - 0.25 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: low - method: estimated - source: null - note: No specific data found for this population. Estimated with normal distribution - across levels, assuming typical variation in environmental consciousness among - suburban homeowners. - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: occupation_category - type: categorical - category: universal - description: General occupational category (professional, management, service, trades, - etc.) - scope: individual - sampling: - strategy: conditional - distribution: - type: categorical - options: - - professional - - management - - service - - sales - - trades - - administrative - weights: - - 0.35 - - 0.25 - - 0.12 - - 0.13 - - 0.1 - - 0.05 - formula: null - depends_on: - - employment_status - modifiers: - - when: employment_status == 'full_time' - multiply: null - add: null - weight_overrides: - professional: 0.35 - management: 0.25 - service: 0.1 - sales: 0.15 - trades: 0.1 - administrative: 0.05 - probability_override: null - - when: employment_status == 'part_time' - multiply: null - add: null - weight_overrides: - professional: 0.2 - management: 0.1 - service: 0.3 - sales: 0.2 - trades: 0.05 - administrative: 0.15 - probability_override: null - - when: employment_status == 'self_employed' - multiply: null - add: null - weight_overrides: - professional: 0.3 - management: 0.2 - service: 0.15 - sales: 0.15 - trades: 0.15 - administrative: 0.05 - probability_override: null - - when: employment_status == 'unemployed' - multiply: null - add: null - weight_overrides: - professional: 0.2 - management: 0.15 - service: 0.25 - sales: 0.15 - trades: 0.15 - administrative: 0.1 - probability_override: null - grounding: - level: medium - method: researched - source: BLS Employment Projections 2024-34 and occupational distribution data - note: Professional and management roles dominate suburban homeowner demographics - with school-age children. Distribution reflects higher education levels (high - school to graduate degrees) and full employment status typical of this population. - constraints: [] -- name: individual_income - type: int - category: universal - description: Annual individual income in USD before taxes - scope: individual - sampling: - strategy: conditional - distribution: - type: normal - mean: 75000.0 - std: 28000.0 - min: 25000.0 - max: 250000.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: - - occupation_category - - education_level - modifiers: - - when: occupation_category == 'professional' and education_level == 'graduate' - multiply: 1.4 - add: 15000.0 - weight_overrides: null - probability_override: null - - when: occupation_category == 'professional' and education_level == 'bachelor' - multiply: 1.2 - add: 5000.0 - weight_overrides: null - probability_override: null - - when: occupation_category == 'management' and education_level == 'graduate' - multiply: 1.5 - add: 20000.0 - weight_overrides: null - probability_override: null - - when: occupation_category == 'management' and education_level == 'bachelor' - multiply: 1.3 - add: 10000.0 - weight_overrides: null - probability_override: null - - when: occupation_category == 'service' - multiply: 0.6 - add: -15000.0 - weight_overrides: null - probability_override: null - - when: occupation_category == 'sales' and education_level == 'bachelor' - multiply: 1.0 - add: 0.0 - weight_overrides: null - probability_override: null - - when: occupation_category == 'trades' - multiply: 0.85 - add: -5000.0 - weight_overrides: null - probability_override: null - - when: occupation_category == 'administrative' - multiply: 0.7 - add: -10000.0 - weight_overrides: null - probability_override: null - grounding: - level: strong - method: researched - source: Census Bureau 2024 data and DQYDJ income by education statistics - note: Median individual income $53,010 nationally in 2024, but suburban homeowners - with children skew higher. Base distribution centered at $75k reflects education - mix (high school to graduate) and professional/management occupations dominant - in this population. - constraints: - - type: hard_min - value: 25000.0 - expression: null - reason: Minimum income for employed individuals in this demographic - - type: hard_max - value: 250000.0 - expression: null - reason: Upper bound for individual income before extreme outliers -- name: household_income - type: int - category: universal - description: Total annual household income in USD before taxes - scope: household - sampling: - strategy: conditional - distribution: - type: normal - mean: 105000.0 - std: 38000.0 - min: 45000.0 - max: 350000.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: - - individual_income - - household_size - modifiers: - - when: individual_income > 100000 and household_size >= 4 - multiply: 1.5 - add: 20000.0 - weight_overrides: null - probability_override: null - - when: individual_income > 100000 and household_size < 4 - multiply: 1.3 - add: 10000.0 - weight_overrides: null - probability_override: null - - when: individual_income <= 50000 - multiply: 0.9 - add: -5000.0 - weight_overrides: null - probability_override: null - - when: household_size >= 5 - multiply: 1.2 - add: 5000.0 - weight_overrides: null - probability_override: null - grounding: - level: strong - method: researched - source: Census Bureau ACS 2020-2024 data showing US median household income $80,734 - note: Suburban homeowners with school-age children have higher incomes than national - median. Many are dual-income households. Base mean $105k reflects homeownership - requirements and child-rearing costs in suburban areas. - constraints: - - type: hard_min - value: 45000.0 - expression: null - reason: Minimum viable household income for suburban homeownership with children - - type: hard_max - value: 350000.0 - expression: null - reason: Upper bound before extreme high earners - - type: expression - value: null - expression: household_income >= individual_income - reason: Household income must be at least as high as individual income -- name: homeownership_years - type: int - category: population_specific - description: Number of years the household has owned their current home - scope: household - sampling: - strategy: conditional - distribution: - type: normal - mean: null - std: 5.5 - min: 1.0 - max: null - mean_formula: max(1, min(age - 30, 20)) - std_formula: null - min_formula: null - max_formula: age - 22 - formula: null - depends_on: - - age - modifiers: - - when: age < 35 - multiply: 0.5 - add: -2.0 - weight_overrides: null - probability_override: null - - when: age >= 50 - multiply: 1.3 - add: 3.0 - weight_overrides: null - probability_override: null - grounding: - level: strong - method: researched - source: Redfin and ConsumerAffairs 2024 data on homeownership tenure - note: 'National median homeownership duration is 11.9-12.3 years as of 2024. Formula - adjusts for age: younger homeowners (late 30s) likely owned 5-10 years, while - older (50+) may own 15-20 years. Cap at 20 years reflects that parents with - school-age children often purchased homes within last 15-20 years.' - constraints: - - type: hard_min - value: 1.0 - expression: null - reason: Must have owned for at least 1 year - - type: expression - value: null - expression: homeownership_years <= age - 22 - reason: Cannot have owned home longer than adult life (assuming minimum purchase - age 22) -- name: home_value - type: int - category: population_specific - description: Estimated market value of the owned home in USD - scope: household - sampling: - strategy: conditional - distribution: - type: normal - mean: 380000.0 - std: 145000.0 - min: 180000.0 - max: 1200000.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: - - state - - household_income - modifiers: - - when: state in ['CA', 'MA', 'NY', 'WA', 'HI'] - multiply: 1.8 - add: 150000.0 - weight_overrides: null - probability_override: null - - when: state in ['CO', 'OR', 'NJ', 'MD', 'CT'] - multiply: 1.4 - add: 80000.0 - weight_overrides: null - probability_override: null - - when: state in ['MS', 'AR', 'WV', 'AL', 'KY'] - multiply: 0.6 - add: -80000.0 - weight_overrides: null - probability_override: null - - when: household_income > 150000 - multiply: 1.4 - add: 50000.0 - weight_overrides: null - probability_override: null - - when: household_income < 80000 - multiply: 0.75 - add: -30000.0 - weight_overrides: null - probability_override: null - grounding: - level: strong - method: researched - source: Zillow Home Value Index, Redfin, and NAR data for 2025-2026 - note: 'National median home value $360,727 (Zillow Jan 2026), median sale price - $423,261-$446,000 (Redfin). Suburban homes typically higher than national median. - Mean $380k with large std dev captures state variation: CA/HI ($600k-800k+), - TX/FL ($300k-400k), Midwest ($250k-350k).' - constraints: - - type: hard_min - value: 180000.0 - expression: null - reason: Minimum suburban single-family home value - - type: hard_max - value: 1200000.0 - expression: null - reason: Upper bound for typical suburban homes before luxury segment -- name: mortgage_status - type: categorical - category: population_specific - description: Current mortgage status (paying mortgage, paid off, no mortgage) - scope: household - sampling: - strategy: conditional - distribution: - type: categorical - options: - - paying_mortgage - - paid_off - weights: - - 0.72 - - 0.28 - formula: null - depends_on: - - homeownership_years - - age - modifiers: - - when: homeownership_years >= 15 and age >= 50 - multiply: null - add: null - weight_overrides: - paying_mortgage: 0.4 - paid_off: 0.6 - probability_override: null - - when: homeownership_years < 10 - multiply: null - add: null - weight_overrides: - paying_mortgage: 0.95 - paid_off: 0.05 - probability_override: null - - when: homeownership_years >= 10 and homeownership_years < 15 - multiply: null - add: null - weight_overrides: - paying_mortgage: 0.75 - paid_off: 0.25 - probability_override: null - grounding: - level: strong - method: researched - source: Census Bureau 2024 ACS and Federal Reserve SHED data - note: Nationally, 40.3% of all homeowners are mortgage-free (2024), but this includes - many retirees. For working-age homeowners under 65, only 27.7% own free and - clear. Suburban parents with school-age children are primarily working-age (35-55), - so base rate set at 28% paid off, 72% with mortgage. - constraints: [] -- name: youngest_child_age - type: int - category: population_specific - description: Age of the youngest school-age child in years - scope: household - sampling: - strategy: conditional - distribution: - type: normal - mean: 8.0 - std: 3.5 - min: 5.0 - max: 17.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: - - num_children - modifiers: - - when: num_children == 1 - multiply: 1.2 - add: 1.0 - weight_overrides: null - probability_override: null - - when: num_children >= 3 - multiply: 0.8 - add: -1.0 - weight_overrides: null - probability_override: null - grounding: - level: medium - method: estimated - source: Population demographics and typical family patterns - note: For households with mean 2.1 school-age children, youngest child typically - in elementary or middle school range. Mean of 8 years reflects mix of families - with young elementary (5-7), middle elementary (8-10), and middle school (11-13) - as youngest. - constraints: - - type: hard_min - value: 5.0 - expression: null - reason: School-age children are K-12, minimum age 5 (kindergarten) - - type: hard_max - value: 17.0 - expression: null - reason: Maximum K-12 age before college - - type: expression - value: null - expression: youngest_child_age <= oldest_child_age - reason: Youngest child cannot be older than oldest child -- name: oldest_child_age - type: int - category: population_specific - description: Age of the oldest school-age child in years - scope: household - sampling: - strategy: conditional - distribution: - type: normal - mean: 13.0 - std: 3.8 - min: 5.0 - max: 18.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: youngest_child_age + 15 - formula: null - depends_on: - - num_children - - youngest_child_age - modifiers: - - when: num_children == 1 - multiply: 1.0 - add: 0.0 - weight_overrides: null - probability_override: null - - when: num_children >= 3 - multiply: 1.2 - add: 2.0 - weight_overrides: null - probability_override: null - grounding: - level: medium - method: estimated - source: Family demographic patterns and typical child spacing - note: With mean 2.1 children and youngest averaging 8 years, oldest child typically - in middle/high school range. Mean 13 years allows for 2-5 year spacing between - children, common in American families. - constraints: - - type: hard_min - value: 5.0 - expression: null - reason: School-age minimum (K-12) - - type: hard_max - value: 18.0 - expression: null - reason: Maximum K-12 age - - type: expression - value: null - expression: oldest_child_age >= youngest_child_age - reason: Oldest child must be at least as old as youngest -- name: school_type - type: categorical - category: population_specific - description: Type of school children attend (public, private, charter, homeschool) - scope: household - sampling: - strategy: conditional - distribution: - type: categorical - options: - - public - - private - - charter - - homeschool - weights: - - 0.75 - - 0.15 - - 0.07 - - 0.03 - formula: null - depends_on: - - household_income - modifiers: - - when: household_income > 150000 - multiply: null - add: null - weight_overrides: - public: 0.4 - private: 0.45 - charter: 0.1 - homeschool: 0.05 - probability_override: null - - when: household_income >= 100000 and household_income <= 150000 - multiply: null - add: null - weight_overrides: - public: 0.6 - private: 0.25 - charter: 0.12 - homeschool: 0.03 - probability_override: null - - when: household_income < 100000 - multiply: null - add: null - weight_overrides: - public: 0.75 - private: 0.1 - charter: 0.12 - homeschool: 0.03 - probability_override: null - grounding: - level: medium - method: researched - source: National Center for Education Statistics (NCES) data - note: 'Nationally, approximately 83% of K-12 students attend public schools (including - charters), 10% private, 3-4% homeschool. Suburban families slightly higher private - school enrollment than national average. Base weights: 75% public, 15% private, - 7% charter, 3% homeschool.' - constraints: [] -- name: dual_income_household - type: boolean - category: population_specific - description: Whether the household has two income earners - scope: household - sampling: - strategy: conditional - distribution: - type: boolean - probability_true: 0.68 - formula: null - depends_on: - - marital_status - - household_size - modifiers: - - when: marital_status == 'married' and household_size >= 3 - multiply: null - add: null - weight_overrides: null - probability_override: 0.8 - - when: marital_status == 'married' and household_size < 3 - multiply: null - add: null - weight_overrides: null - probability_override: 0.7 - - when: marital_status in ['divorced', 'single'] - multiply: null - add: null - weight_overrides: null - probability_override: 0.3 - - when: marital_status == 'widowed' - multiply: null - add: null - weight_overrides: null - probability_override: 0.2 - grounding: - level: medium - method: estimated - source: BLS and Census labor force participation data - note: Among married couples with children, approximately 65-70% are dual-earner - households. For suburban homeowners with school-age children where homeownership - and childcare costs are high, dual income is common. Base probability 68%. - constraints: [] -- name: commute_time - type: int - category: population_specific - description: One-way commute time to work in minutes - scope: individual - sampling: - strategy: conditional - distribution: - type: normal - mean: 28.0 - std: 12.0 - min: 5.0 - max: 90.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: - - employment_status - modifiers: - - when: employment_status == 'full_time' - multiply: 1.1 - add: 2.0 - weight_overrides: null - probability_override: null - - when: employment_status == 'part_time' - multiply: 0.85 - add: -3.0 - weight_overrides: null - probability_override: null - - when: employment_status == 'self_employed' - multiply: 0.7 - add: -5.0 - weight_overrides: null - probability_override: null - - when: employment_status == 'unemployed' - multiply: 0.0 - add: -28.0 - weight_overrides: null - probability_override: null - grounding: - level: medium - method: researched - source: Census Bureau American Community Survey commute data - note: National mean commute time approximately 26-27 minutes. Suburban residents - typically have slightly longer commutes (28-30 min) than national average as - they commute to urban job centers. Distribution includes some remote workers - with minimal commute. - constraints: - - type: hard_min - value: 5.0 - expression: null - reason: Minimum commute time for those who work outside home - - type: hard_max - value: 90.0 - expression: null - reason: Maximum reasonable one-way commute -- name: vehicle_count - type: int - category: population_specific - description: Number of vehicles owned by the household - scope: household - sampling: - strategy: conditional - distribution: - type: normal - mean: 2.5 - std: 0.7 - min: 1.0 - max: 5.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: - - household_size - - household_income - modifiers: - - when: household_size >= 5 and household_income > 100000 - multiply: 1.3 - add: 1.0 - weight_overrides: null - probability_override: null - - when: household_size <= 3 - multiply: 0.8 - add: 0.0 - weight_overrides: null - probability_override: null - - when: household_income < 80000 - multiply: 0.75 - add: -0.5 - weight_overrides: null - probability_override: null - grounding: - level: medium - method: estimated - source: Federal Highway Administration and household transportation patterns - note: Suburban households with school-age children typically own 2-3 vehicles. - With mean household size 4.2 and dual-earner prevalence, 2-3 vehicles common - for work commutes, school transport, and activities. Mean 2.5 reflects this - pattern. - constraints: - - type: hard_min - value: 1.0 - expression: null - reason: Suburban households require at least one vehicle - - type: hard_max - value: 5.0 - expression: null - reason: Practical maximum for typical household -- name: pta_membership - type: boolean - category: population_specific - description: Whether the individual is a member of Parent-Teacher Association - scope: individual - sampling: - strategy: conditional - distribution: - type: boolean - probability_true: 0.35 - formula: null - depends_on: - - community_involvement - modifiers: - - when: community_involvement == 'high' - multiply: null - add: null - weight_overrides: null - probability_override: 0.85 - - when: community_involvement == 'medium' - multiply: null - add: null - weight_overrides: null - probability_override: 0.55 - - when: community_involvement == 'low' - multiply: null - add: null - weight_overrides: null - probability_override: 0.25 - - when: community_involvement == 'none' - multiply: null - add: null - weight_overrides: null - probability_override: 0.05 - grounding: - level: low - method: estimated - source: National PTA membership trends and community involvement patterns - note: National PTA membership approximately 4 million across 22,000+ locations. - Not all parents with school-age children join. Estimated 30-40% of suburban - parents with medium-high community involvement join PTA. Base probability 35%. - constraints: [] -- name: home_office - type: boolean - category: population_specific - description: Whether the home has a dedicated home office space - scope: household - sampling: - strategy: conditional - distribution: - type: boolean - probability_true: 0.48 - formula: null - depends_on: - - employment_status - - home_value - modifiers: - - when: employment_status == 'self_employed' - multiply: null - add: null - weight_overrides: null - probability_override: 0.85 - - when: employment_status == 'full_time' and home_value > 400000 - multiply: null - add: null - weight_overrides: null - probability_override: 0.65 - - when: employment_status == 'full_time' and home_value <= 400000 - multiply: null - add: null - weight_overrides: null - probability_override: 0.45 - - when: employment_status == 'part_time' - multiply: null - add: null - weight_overrides: null - probability_override: 0.4 - - when: employment_status == 'unemployed' - multiply: null - add: null - weight_overrides: null - probability_override: 0.3 - grounding: - level: medium - method: estimated - source: Post-pandemic work-from-home trends and housing patterns - note: Post-2020, dedicated home office space became more common. Suburban homes - with higher values and professional/management workers have higher likelihood. - Estimated 45-50% of suburban professional households have dedicated home office - space. Base probability 48%. - constraints: [] -- name: household_savings - type: int - category: population_specific - description: Total household savings and investments in USD - scope: household - sampling: - strategy: conditional - distribution: - type: lognormal - mean: 75000.0 - std: 95000.0 - min: 5000.0 - max: 800000.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: - - household_income - - age - modifiers: - - when: household_income > 150000 and age >= 45 - multiply: 2.0 - add: 50000.0 - weight_overrides: null - probability_override: null - - when: household_income > 150000 and age < 45 - multiply: 1.5 - add: 25000.0 - weight_overrides: null - probability_override: null - - when: household_income < 80000 - multiply: 0.5 - add: -20000.0 - weight_overrides: null - probability_override: null - - when: age >= 50 - multiply: 1.6 - add: 30000.0 - weight_overrides: null - probability_override: null - - when: age < 35 - multiply: 0.6 - add: -15000.0 - weight_overrides: null - probability_override: null - grounding: - level: medium - method: estimated - source: Federal Reserve Survey of Consumer Finances and savings patterns - note: Household savings highly skewed (lognormal). Median US household savings - much lower than mean. For suburban homeowners aged 35-55 with median income - $105k, typical savings $40k-120k. Lognormal with mean $75k, high std dev captures - wide variation. - constraints: - - type: hard_min - value: 5000.0 - expression: null - reason: Minimum emergency savings for homeowners - - type: hard_max - value: 800000.0 - expression: null - reason: Upper bound for typical household savings before ultra-wealthy -- name: childcare_expense - type: int - category: population_specific - description: Monthly childcare expenses in USD (afterschool, daycare, etc.) - scope: household - sampling: - strategy: conditional - distribution: - type: normal - mean: 650.0 - std: 420.0 - min: 0.0 - max: 2500.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: - - num_children - - youngest_child_age - modifiers: - - when: num_children >= 3 and youngest_child_age < 6 - multiply: 2.0 - add: 500.0 - weight_overrides: null - probability_override: null - - when: num_children >= 3 and youngest_child_age >= 6 - multiply: 1.5 - add: 200.0 - weight_overrides: null - probability_override: null - - when: youngest_child_age >= 12 - multiply: 0.4 - add: -300.0 - weight_overrides: null - probability_override: null - - when: youngest_child_age < 6 - multiply: 1.8 - add: 400.0 - weight_overrides: null - probability_override: null - - when: num_children == 1 - multiply: 0.7 - add: -100.0 - weight_overrides: null - probability_override: null - grounding: - level: medium - method: estimated - source: Childcare cost surveys and BLS Consumer Expenditure data - note: School-age childcare less expensive than infant/toddler care. Afterschool - programs typically $200-500/child/month. Some families need no care (stay-at-home - parent, older children), others need full afterschool care for 2+ children. - Mean $650/month reflects mix. - constraints: - - type: hard_min - value: 0.0 - expression: null - reason: Some families have no childcare expenses (older children, family help) - - type: hard_max - value: 2500.0 - expression: null - reason: Maximum for afterschool care for multiple school-age children -- name: extracurricular_count - type: int - category: population_specific - description: Number of extracurricular activities children are enrolled in (sports, - music, etc.) - scope: household - sampling: - strategy: conditional - distribution: - type: normal - mean: 3.2 - std: 1.8 - min: 0.0 - max: 10.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: - - num_children - - household_income - modifiers: - - when: num_children >= 3 and household_income > 120000 - multiply: 1.5 - add: 2.0 - weight_overrides: null - probability_override: null - - when: num_children == 1 - multiply: 0.8 - add: -0.5 - weight_overrides: null - probability_override: null - - when: household_income > 150000 - multiply: 1.4 - add: 1.5 - weight_overrides: null - probability_override: null - - when: household_income < 80000 - multiply: 0.65 - add: -1.0 - weight_overrides: null - probability_override: null - grounding: - level: low - method: estimated - source: Youth sports and activities participation surveys - note: Average US child participates in 1-2 organized activities (sports, music, - scouts, etc.). With mean 2.1 children per household, total household activities - approximately 2-4. Higher-income suburban families tend toward more activities. - Mean 3.2 reflects 1.5 activities per child on average. - constraints: - - type: hard_min - value: 0.0 - expression: null - reason: Some families have no organized extracurriculars - - type: hard_max - value: 10.0 - expression: null - reason: Maximum total activities across all children in household -- name: religious_affiliation - type: categorical - category: population_specific - description: Religious identity of the individual (christian_conservative, christian_moderate, - christian_progressive, other_religion, secular, none) - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - christian_conservative - - christian_moderate - - christian_progressive - - other_religion - - secular - - none - weights: - - 0.22 - - 0.28 - - 0.12 - - 0.08 - - 0.18 - - 0.12 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: extrapolated - source: https://www.pewresearch.org/religion/2025/02/26/age-race-education-and-other-demographic-traits-of-us-religious-groups/ - and https://prri.org/research/census-2023-american-religion/ - note: Based on Pew Research 2023-24 data showing 72% of parents with school-age - children identify as Christian vs 24-27% unaffiliated, combined with PRRI 2023 - data showing suburban populations (42% white evangelical, 45% unaffiliated in - suburbs). Conservative/moderate/progressive split estimated based on political-religious - correlations and suburban white evangelical concentration (42% suburban). Distribution - adjusted for parents with school-age children who tend to be slightly more religious - than general population. - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1.0 - reason: Categorical weights must sum to 1.0 -- name: political_ideology - type: categorical - category: population_specific - description: Self-identified political ideology on a spectrum (very_conservative, - conservative, moderate, liberal, very_liberal) - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - very_conservative - - conservative - - moderate - - liberal - - very_liberal - weights: - - 0.12 - - 0.26 - - 0.32 - - 0.2 - - 0.1 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: researched - source: https://www.pewresearch.org/politics/2024/04/09/partisanship-in-rural-suburban-and-urban-communities/ - note: Pew Research 2024 shows suburban voters split 50% Republican / 47% Democratic - alignment. Combined with data showing parents tend to adopt similar political - views to each other (71% of teens match parents' ideology) and suburban populations - show relatively even distribution. Moderate is highest category as suburban - voters are more centrist than rural. Distribution reflects suburban homeowners - who tend to be slightly more conservative than urban residents but more moderate - than rural. - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1.0 - reason: Categorical weights must sum to 1.0 -- name: free_speech_absolutism - type: categorical - category: personality - description: Strength of belief in absolute free speech versus content curation - (absolutist, strong, moderate, limited) - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - absolutist - - strong - - moderate - - limited - weights: - - 0.15 - - 0.32 - - 0.38 - - 0.15 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: extrapolated - source: https://knightfoundation.org/wp-content/uploads/2022/01/KF_Free_Expression_2022.pdf - and https://www.pewresearch.org/short-reads/2022/08/30/more-so-than-adults-u-s-teens-value-people-feeling-safe-online-over-being-able-to-speak-freely/ - note: 'Knight Foundation 2022 survey shows 91% value free speech principles but - with significant disagreement on application. Adults split roughly 50-50 on - whether safe spaces vs free speech is more important. Pew 2022 shows partisan - divide: Democrats favor safety (62%), Republicans favor free speech (64%). For - parents with school-age children context, moderate position is most common. - Absolutist and limited categories represent ideological extremes on both sides - of debate.' - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1.0 - reason: Categorical weights must sum to 1.0 -- name: trust_in_institutions - type: categorical - category: personality - description: Level of trust in public institutions like schools and government (very_high, - high, moderate, low, very_low) - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - very_high - - high - - moderate - - low - - very_low - weights: - - 0.08 - - 0.2 - - 0.35 - - 0.25 - - 0.12 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: strong - method: researched - source: https://news.gallup.com/poll/508169/historically-low-faith-institutions-continues.aspx - and https://www.edchoice.org/new-polling-reveals-parents-trust-teachers-on-education-more-than-state-legislatures/ - and https://www.aamchealthjustice.org/news/polling/trust-trends - note: Gallup 2024 shows public schools at 27% confidence (near historic low), - with average institutional confidence at 26%. However, EdChoice 2024 shows 68% - of parents trust local school boards, 88% trust teachers. Partnership for Public - Service 2024 shows only 23% trust federal government. AAMC 2024 shows gradual - decline 2021-2024, with Gen Z showing lower trust. Distribution weighted toward - moderate-to-low given context of school board decisions (local institutions - score higher than federal). Parents trust local schools more than institutions - generally, hence moderate is peak category. - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1.0 - reason: Categorical weights must sum to 1.0 -- name: has_school_age_children - type: boolean - category: population_specific - description: Whether the individual currently has school-age children that could - be affected by book ban decisions - scope: individual - sampling: - strategy: derived - distribution: null - formula: num_children > 0 - depends_on: - - num_children - modifiers: [] - grounding: - level: strong - method: computed - source: null - note: Deterministic transformation - constraints: [] -- name: school_board_engagement - type: categorical - category: population_specific - description: Level of engagement with local school board activities and meetings - (very_active, active, occasional, minimal, none) - scope: individual - sampling: - strategy: conditional - distribution: - type: categorical - options: - - very_active - - active - - occasional - - minimal - - none - weights: - - 0.08 - - 0.17 - - 0.3 - - 0.3 - - 0.15 - formula: null - depends_on: - - community_involvement - - education_priority - - num_children - modifiers: - - when: community_involvement == 'high' and education_priority == 'high' - multiply: null - add: null - weight_overrides: - very_active: 0.45 - active: 0.35 - occasional: 0.15 - minimal: 0.04 - none: 0.01 - probability_override: null - - when: community_involvement == 'high' and education_priority == 'medium' - multiply: null - add: null - weight_overrides: - very_active: 0.25 - active: 0.4 - occasional: 0.25 - minimal: 0.08 - none: 0.02 - probability_override: null - - when: community_involvement == 'medium' and education_priority == 'high' - multiply: null - add: null - weight_overrides: - very_active: 0.2 - active: 0.35 - occasional: 0.3 - minimal: 0.12 - none: 0.03 - probability_override: null - - when: num_children >= 2 and education_priority == 'high' - multiply: null - add: null - weight_overrides: - very_active: 0.3 - active: 0.35 - occasional: 0.25 - minimal: 0.08 - none: 0.02 - probability_override: null - - when: community_involvement == 'low' or community_involvement == 'none' - multiply: null - add: null - weight_overrides: - very_active: 0.02 - active: 0.08 - occasional: 0.2 - minimal: 0.35 - none: 0.35 - probability_override: null - grounding: - level: medium - method: extrapolated - source: NCES Parent and Family Involvement surveys (2013-2016), EdWeek research - note: School board meeting attendance is much lower than general PTA/school event - attendance. Research shows 87% of parents attend PTA/association meetings but - school board attendance is far lower. Estimated that 5-10% of parents with children - are very active in school board meetings, with most limiting involvement to - occasional attendance at best. Distribution extrapolated from general parent - engagement patterns showing higher engagement correlates with community involvement - and education priority. - constraints: [] -- name: media_consumption_primary - type: categorical - category: population_specific - description: Primary source of news and information (social_media, cable_news, local_news, - national_newspapers, online_news) - scope: individual - sampling: - strategy: conditional - distribution: - type: categorical - options: - - social_media - - cable_news - - local_news - - national_newspapers - - online_news - weights: - - 0.25 - - 0.2 - - 0.2 - - 0.1 - - 0.25 - formula: null - depends_on: - - age - modifiers: - - when: age >= 45 - multiply: null - add: null - weight_overrides: - social_media: 0.2 - cable_news: 0.3 - local_news: 0.25 - national_newspapers: 0.1 - online_news: 0.15 - probability_override: null - - when: age < 45 - multiply: null - add: null - weight_overrides: - social_media: 0.4 - cable_news: 0.1 - local_news: 0.2 - national_newspapers: 0.08 - online_news: 0.22 - probability_override: null - grounding: - level: strong - method: researched - source: Pew Research Center 2024-2025 news consumption surveys, American Press - Institute - note: Adults under 30 use social media as primary source (71% daily), while TV - (especially cable) dominates for 65+ (63%). For suburban parents aged 30-50 - (typical school-age parents), mix of social media, online news, and cable/local - news. Social media and online news combined represent about 50% of primary sources, - cable and local news about 40%, traditional newspapers declining to ~10%. - constraints: [] -- name: parental_control_preference - type: categorical - category: population_specific - description: Preference for who controls children's reading material (parents_only, - parents_primary, shared_responsibility, educators_primary, educators_only) - scope: individual - sampling: - strategy: conditional - distribution: - type: categorical - options: - - parents_only - - parents_primary - - shared_responsibility - - educators_primary - - educators_only - weights: - - 0.1 - - 0.35 - - 0.4 - - 0.12 - - 0.03 - formula: null - depends_on: - - trust_in_institutions - - education_priority - modifiers: - - when: trust_in_institutions == 'very_low' or trust_in_institutions == 'low' - multiply: null - add: null - weight_overrides: - parents_only: 0.4 - parents_primary: 0.35 - shared_responsibility: 0.18 - educators_primary: 0.05 - educators_only: 0.02 - probability_override: null - - when: trust_in_institutions == 'very_high' or trust_in_institutions == 'high' - multiply: null - add: null - weight_overrides: - parents_only: 0.05 - parents_primary: 0.15 - shared_responsibility: 0.5 - educators_primary: 0.25 - educators_only: 0.05 - probability_override: null - - when: education_priority == 'high' and trust_in_institutions == 'moderate' - multiply: null - add: null - weight_overrides: - parents_only: 0.15 - parents_primary: 0.3 - shared_responsibility: 0.4 - educators_primary: 0.12 - educators_only: 0.03 - probability_override: null - - when: education_priority == 'low' - multiply: null - add: null - weight_overrides: - parents_only: 0.1 - parents_primary: 0.2 - shared_responsibility: 0.35 - educators_primary: 0.25 - educators_only: 0.1 - probability_override: null - grounding: - level: medium - method: researched - source: EveryLibrary Institute 2023 parent perception survey, EdWeek 2024 surveys - note: 80% of parents trust school librarians to select appropriate books, yet - 60% want age restrictions or parental permission. 76% think parents should decide - if their child can access challenging topics. Most parents favor shared control - rather than extremes - only small minorities want exclusive parent control (estimated - 10-15%) or exclusive educator control (3-5%). Base distribution reflects trust - but desire for involvement, with shared responsibility most common. - constraints: [] -- name: library_usage_frequency - type: categorical - category: population_specific - description: How often the individual or household uses public or school libraries - (weekly, monthly, occasionally, rarely, never) - scope: individual - sampling: - strategy: conditional - distribution: - type: categorical - options: - - weekly - - monthly - - occasionally - - rarely - - never - weights: - - 0.24 - - 0.28 - - 0.25 - - 0.15 - - 0.08 - formula: null - depends_on: - - education_level - - num_children - modifiers: - - when: num_children >= 2 and education_level in ['bachelor', 'graduate'] - multiply: null - add: null - weight_overrides: - weekly: 0.25 - monthly: 0.35 - occasionally: 0.25 - rarely: 0.12 - never: 0.03 - probability_override: null - - when: num_children == 1 and education_level in ['bachelor', 'graduate'] - multiply: null - add: null - weight_overrides: - weekly: 0.15 - monthly: 0.3 - occasionally: 0.35 - rarely: 0.15 - never: 0.05 - probability_override: null - - when: education_level in ['high_school', 'associate'] - multiply: null - add: null - weight_overrides: - weekly: 0.08 - monthly: 0.2 - occasionally: 0.35 - rarely: 0.27 - never: 0.1 - probability_override: null - grounding: - level: strong - method: researched - source: Pew Research Center 2013-2016 library usage surveys, NCES household surveys - note: '61% of households with children under 18 used library in past month vs - 35% without children. Among parents with school-age children, 70% visited library - in past year. Frequency distribution from Pew: 24% weekly or more, 24% several - times a month, 28% monthly or less often, 23% rarely/never. Parents with children - 6-11 show highest usage (81%). Distribution reflects this high-engagement population.' - constraints: [] -- name: personal_book_ban_experience - type: boolean - category: population_specific - description: Whether the individual has personal experience with book restrictions - or censorship in their own education or parenting - scope: individual - sampling: - strategy: conditional - distribution: - type: boolean - probability_true: 0.15 - formula: null - depends_on: - - age - modifiers: - - when: age >= 45 - multiply: null - add: null - weight_overrides: null - probability_override: 0.15 - - when: age < 45 and age >= 35 - multiply: null - add: null - weight_overrides: null - probability_override: 0.25 - - when: age < 35 - multiply: null - add: null - weight_overrides: null - probability_override: 0.35 - grounding: - level: low - method: estimated - source: Historical book challenge data and generational experience - note: 'No direct data available on personal experience with book restrictions. - Estimated based on: 1) Book challenges historically concentrated in certain - regions/schools, 2) Challenges increased dramatically in recent years (2021+), - but parents in survey are 25-55 age range meaning their K-12 experience was - 1980s-2010s when challenges less common, 3) Most parents would not have direct - experience. Conservative estimate ~15% had some personal encounter with book - restrictions in their education or as parents.' - constraints: [] -- name: lgbtq_acceptance - type: categorical - category: personality - description: Level of acceptance toward LGBTQ individuals and topics (very_accepting, - accepting, neutral, resistant, very_resistant) - scope: individual - sampling: - strategy: conditional - distribution: - type: categorical - options: - - very_accepting - - accepting - - neutral - - resistant - - very_resistant - weights: - - 0.22 - - 0.33 - - 0.25 - - 0.14 - - 0.06 - formula: null - depends_on: - - religious_affiliation - - political_ideology - modifiers: - - when: religious_affiliation == 'christian_conservative' - multiply: null - add: null - weight_overrides: - very_accepting: 0.02 - accepting: 0.05 - neutral: 0.15 - resistant: 0.38 - very_resistant: 0.4 - probability_override: null - - when: religious_affiliation == 'christian_moderate' - multiply: null - add: null - weight_overrides: - very_accepting: 0.1 - accepting: 0.25 - neutral: 0.35 - resistant: 0.22 - very_resistant: 0.08 - probability_override: null - - when: religious_affiliation == 'christian_progressive' or religious_affiliation - == 'secular' - multiply: null - add: null - weight_overrides: - very_accepting: 0.45 - accepting: 0.35 - neutral: 0.15 - resistant: 0.04 - very_resistant: 0.01 - probability_override: null - - when: political_ideology == 'very_conservative' - multiply: null - add: null - weight_overrides: - very_accepting: 0.02 - accepting: 0.05 - neutral: 0.18 - resistant: 0.4 - very_resistant: 0.35 - probability_override: null - - when: political_ideology == 'very_liberal' or political_ideology == 'liberal' - multiply: null - add: null - weight_overrides: - very_accepting: 0.5 - accepting: 0.35 - neutral: 0.12 - resistant: 0.02 - very_resistant: 0.01 - probability_override: null - - when: religious_affiliation == 'none' - multiply: null - add: null - weight_overrides: - very_accepting: 0.4 - accepting: 0.35 - neutral: 0.2 - resistant: 0.04 - very_resistant: 0.01 - probability_override: null - grounding: - level: strong - method: researched - source: Pew Research Center 2023-2025 Religious Landscape Study, PRRI American - Values Atlas 2022-2024 - note: 'Overall US acceptance of homosexuality at 70% (Pew 2023-24), with 59% among - religious affiliated, 87% among unaffiliated. PRRI finds 69-70% support same-sex - marriage. Strong correlation with political ideology: 70-90% acceptance among - liberals vs 40-50% among conservatives. Religious affiliation major factor: - evangelical Protestants majority oppose (discourage), while mainline Protestants, - Catholics, unaffiliated accept. Base distribution reflects general population - with slight skew toward acceptance.' - constraints: [] -- name: age_appropriateness_concern - type: categorical - category: population_specific - description: Level of concern about age-appropriate content in school materials - (very_concerned, concerned, moderate, relaxed, very_relaxed) - scope: individual - sampling: - strategy: conditional - distribution: - type: categorical - options: - - very_concerned - - concerned - - moderate - - relaxed - - very_relaxed - weights: - - 0.2 - - 0.35 - - 0.3 - - 0.12 - - 0.03 - formula: null - depends_on: - - num_children - - education_priority - modifiers: - - when: num_children >= 2 and education_priority == 'high' - multiply: null - add: null - weight_overrides: - very_concerned: 0.35 - concerned: 0.4 - moderate: 0.2 - relaxed: 0.04 - very_relaxed: 0.01 - probability_override: null - - when: num_children == 1 and education_priority == 'high' - multiply: null - add: null - weight_overrides: - very_concerned: 0.3 - concerned: 0.4 - moderate: 0.23 - relaxed: 0.06 - very_relaxed: 0.01 - probability_override: null - - when: education_priority == 'medium' - multiply: null - add: null - weight_overrides: - very_concerned: 0.15 - concerned: 0.35 - moderate: 0.35 - relaxed: 0.12 - very_relaxed: 0.03 - probability_override: null - - when: education_priority == 'low' - multiply: null - add: null - weight_overrides: - very_concerned: 0.08 - concerned: 0.2 - moderate: 0.4 - relaxed: 0.25 - very_relaxed: 0.07 - probability_override: null - - when: num_children >= 3 - multiply: null - add: null - weight_overrides: - very_concerned: 0.38 - concerned: 0.42 - moderate: 0.16 - relaxed: 0.03 - very_relaxed: 0.01 - probability_override: null - grounding: - level: medium - method: extrapolated - source: EveryLibrary Institute 2023 survey, parent rights movement literature - note: 60% of parents want age restrictions or parental permission for certain - books. 76% think parents should decide on challenging topics access. 25% uncomfortable - with puberty/sex ed books for children, 20% uncomfortable with race/racism books. - However, 93% feel children safe in library, 82% trust librarians. Distribution - reflects high baseline concern (55% very concerned or concerned) but not universal, - with majority having at least moderate concern about age-appropriate content. - constraints: [] -- name: social_network_density - type: categorical - category: population_specific - description: Size and interconnectedness of local parent/community network (very_high, - high, moderate, low, isolated) - scope: individual - sampling: - strategy: conditional - distribution: - type: categorical - options: - - very_high - - high - - moderate - - low - - isolated - weights: - - 0.15 - - 0.3 - - 0.35 - - 0.15 - - 0.05 - formula: null - depends_on: - - community_involvement - - homeownership_years - modifiers: - - when: community_involvement == 'high' and homeownership_years >= 5 - multiply: null - add: null - weight_overrides: - very_high: 0.35 - high: 0.4 - moderate: 0.2 - low: 0.04 - isolated: 0.01 - probability_override: null - - when: community_involvement == 'medium' and homeownership_years >= 5 - multiply: null - add: null - weight_overrides: - very_high: 0.15 - high: 0.35 - moderate: 0.35 - low: 0.12 - isolated: 0.03 - probability_override: null - - when: community_involvement == 'high' and homeownership_years < 5 - multiply: null - add: null - weight_overrides: - very_high: 0.2 - high: 0.35 - moderate: 0.3 - low: 0.12 - isolated: 0.03 - probability_override: null - - when: community_involvement == 'low' or community_involvement == 'none' - multiply: null - add: null - weight_overrides: - very_high: 0.02 - high: 0.08 - moderate: 0.25 - low: 0.4 - isolated: 0.25 - probability_override: null - grounding: - level: medium - method: extrapolated - source: Parent engagement research, suburban community patterns - note: Social network density for parents correlates with community involvement - and tenure in neighborhood. Suburban homeowners with school-age children tend - to have moderate-to-high networks through school connections, PTA, extracurriculars. - 76% join PTA per general statistics. Longer homeownership increases network - density. Base distribution assumes most suburban parents have moderate to high - networks, with very high density (15%) among highly engaged, isolated (5%) among - newcomers or low engagement. - constraints: [] -sampling_order: -- age -- agreeableness -- community_involvement -- conscientiousness -- education_level -- education_priority -- employment_status -- commute_time -- environmental_consciousness -- extraversion -- free_speech_absolutism -- gender -- homeownership_years -- household_size -- marital_status -- dual_income_household -- media_consumption_primary -- mortgage_status -- neuroticism -- num_children -- age_appropriateness_concern -- has_school_age_children -- library_usage_frequency -- occupation_category -- individual_income -- household_income -- extracurricular_count -- household_savings -- openness -- personal_book_ban_experience -- political_ideology -- pta_membership -- religious_affiliation -- lgbtq_acceptance -- risk_tolerance -- school_board_engagement -- school_type -- social_network_density -- state -- home_value -- home_office -- trust_in_institutions -- parental_control_preference -- urban_rural -- vehicle_count -- youngest_child_age -- childcare_expense -- oldest_child_age diff --git a/test-v2-e2e/prompt.txt b/test-v2-e2e/prompt.txt deleted file mode 100644 index 54c69f2..0000000 --- a/test-v2-e2e/prompt.txt +++ /dev/null @@ -1 +0,0 @@ -A small US suburban community reacting to a local school board decision to ban certain books from the school library. The community has diverse political views and varying levels of involvement with the school system. diff --git a/test-v2-e2e/spec.yaml b/test-v2-e2e/spec.yaml deleted file mode 100644 index c2938c3..0000000 --- a/test-v2-e2e/spec.yaml +++ /dev/null @@ -1,1998 +0,0 @@ -meta: - description: 10 US suburban homeowners with school-age children - size: 10 - geography: US - agent_focus: suburban homeowners with school-age children - created_at: '2026-02-15T23:06:23.063236' - version: '1.0' - persona_template: null - scenario_description: null - household_config: - age_brackets: - - - 25 - - 18-24 - - - 35 - - 25-34 - - - 45 - - 35-44 - - - 55 - - 45-54 - - - 65 - - 55-64 - - - 999 - - 65+ - household_type_weights: - 18-24: - single: 0.28 - couple: 0.15 - single_parent: 0.14 - couple_with_kids: 0.2 - multi_generational: 0.23 - 25-34: - single: 0.19 - couple: 0.11 - single_parent: 0.19 - couple_with_kids: 0.38 - multi_generational: 0.13 - 35-44: - single: 0.08 - couple: 0.08 - single_parent: 0.17 - couple_with_kids: 0.59 - multi_generational: 0.08 - 45-54: - single: 0.16 - couple: 0.26 - single_parent: 0.09 - couple_with_kids: 0.37 - multi_generational: 0.12 - 55-64: - single: 0.26 - couple: 0.37 - single_parent: 0.06 - couple_with_kids: 0.21 - multi_generational: 0.1 - 65+: - single: 0.42 - couple: 0.44 - single_parent: 0.01 - couple_with_kids: 0.01 - multi_generational: 0.12 - same_group_rates: - white: 0.89 - black: 0.82 - asian: 0.69 - hispanic: 0.75 - default_same_group_rate: 0.8 - assortative_mating: - education_level: 0.68 - income_level: 0.58 - religious_affiliation: 0.65 - partner_age_gap_mean: 2.2 - partner_age_gap_std: 3.5 - min_adult_age: 18 - child_min_parent_offset: 18 - child_max_parent_offset: 45 - max_dependent_child_age: 18 - elderly_min_offset: 20 - elderly_max_offset: 45 - life_stages: - - max_age: 5 - label: preschool - - max_age: 11 - label: elementary - - max_age: 14 - label: middle_school - - max_age: 18 - label: high_school - adult_stage_label: adult - avg_household_size: 3.2 -grounding: - overall: medium - sources_count: 172 - strong_count: 12 - medium_count: 18 - low_count: 5 - sources: - - https://www.census.gov/topics/families.html - - https://www.ssa.gov/policy/docs/ssb/v4n11/v4n11p8.pdf - - https://www.icip.iastate.edu/tables/population/urban-pct-states - - https://nationalmortgageprofessional.com/news/nine-tenths-million-paid-their-home-loans-2024 - - https://www.cnbc.com/2026/02/11/jobs-report-january-2026-.html - - https://eyeonhousing.org/2017/02/the-average-number-of-school-age-children-per-home/ - - https://corridorbusiness.com/study-college-graduates-more-likely-to-own-their-own-home/ - - https://www.clasp.org/sites/default/files/public/resources-and-publications/states/0086.pdf - - https://worldpopulationreview.com/state-rankings/average-household-size-by-state - - https://adpemploymentreport.com/ - - https://www.psychologytoday.com/us/blog/the-asymmetric-brain/202510/what-is-the-average-age-gap-in-relationships-worldwide - - https://www.pewresearch.org/short-reads/2024/08/15/a-growing-share-of-us-husbands-and-wives-are-roughly-the-same-age/ - - https://www.census.gov/topics/families/families-and-households.html - - https://www.census.gov/data/tables/time-series/demo/families/households.html - - https://www.jstor.org/stable/10.1086/661778 - - https://www.rismedia.com/2022/04/18/homeownership-education-dgree-holding-owners-sugre-those-without-high-school-drop-30/ - - https://www.rubyhome.com/blog/average-length-homeownership/ - - https://www.nar.realtor/blogs/economists-outlook/how-education-is-shaping-homeownership-trends - - https://dx.doi.org/10.1086/661778 - - https://ipropertymanagement.com/research/homeownership-rate-by-age - - https://fred.stlouisfed.org/series/CXU980230LB1409M - - https://www.census.gov/library/stories/2022/07/marriage-prevalence-for-black-adults-varies-by-state.html - - https://en.wikipedia.org/wiki/Household_income_in_the_United_States - - https://nces.ed.gov/programs/digest/d09/tables/dt09_017.asp - - https://www.census.gov/data/tables/2022/demo/families/cps-2022.html - - https://www.pewresearch.org/social-trends/2017/05/18/1-trends-and-patterns-in-intermarriage/ - - https://www.urban.org/urban-wire/homeownership-has-fallen-further-out-reach-younger-families-lowest-incomes - - https://www.zillow.com/home-values/102001/united-states/ - - https://nces.ed.gov/fastfacts/display.asp?id=372 - - https://nces.ed.gov/programs/digest/d21/tables/dt21_102.10.asp - - https://www.point2homes.com/news/us-real-estate-news/homeownership-by-education-us.html - - https://www.floridarealtors.org/news-media/news-articles/2025/06/how-education-shaping-homeownership - - https://www.chiccousa.com/baby-talk/cities-with-the-most-family-growth-over-time/ - - https://www.scotsmanguide.com/news/homeowners-are-staying-put-for-an-average-of-nearly-12-years/ - - https://www.pewresearch.org/social-trends/2018/05/22/demographic-and-economic-trends-in-urban-suburban-and-rural-communities/ - - https://www.census.gov/content/dam/Census/library/working-papers/2023/demo/sehsd-wp2023-10.pdf - - https://www.bls.gov/news.release/famee.htm - - https://fred.stlouisfed.org/release/tables?eid=1138280&rid=462 - - https://raleighrealty.com/blog/average-homeownership-length - - https://fivethirtyeight.com/features/how-suburban-are-big-american-cities/ - - https://www.bls.gov/careeroutlook/2017/article/projections-laborforce.htm - - https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=3571&context=etd - - https://www.lincolnvitalsigns.org/economy-and-workforce/median-earnings-by-education/ - - https://www.pewresearch.org/short-reads/2014/01/29/new-academic-study-links-rising-income-inequality-to-assortive-mating/ - - https://www.federalreserve.gov/publications/2025-economic-well-being-of-us-households-in-2024-housing.htm - - https://www.bls.gov/news.release/empsit.nr0.htm - - https://www.redfin.com/news/homeowner-tenure-2022/ - - https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr682.pdf - - https://www.demandsage.com/average-us-income/ - - https://www.statista.com/statistics/985183/size-urban-rural-population-us/ - - https://www.bls.gov/news.release/pdf/famee.pdf - - https://ofm.wa.gov/washington-data-research/statewide-data/washington-trends/budget-drivers/kindergarten-through-grade-12-k-12-enrollment - - https://www.census.gov/newsroom/press-releases/2022/americas-families-and-living-arrangements.html - - https://en.wikipedia.org/wiki/Homeownership_in_the_United_States - - https://worldpopulationreview.com/state-rankings/median-home-price-by-state - - https://www.bls.gov/opub/mlr/2013/article/marriage-and-divorce-patterns-by-gender-race-and-educational-attainment.htm - - https://www.huduser.gov/portal/pdredge/pdr-edge-frm-asst-sec-080320.html - - https://www.nber.org/digest/may14/assortative-mating-and-income-inequality - - https://fred.stlouisfed.org/series/PAYEMS - - https://datacenter.aecf.org/data/tables/101-child-population-by-age-group - - https://realestate.usnews.com/home-services/moving/median-home-prices-state - - https://statmodeling.stat.columbia.edu/2024/07/31/age-gap-between-spouses/ - - https://www.nar.realtor/research-and-statistics/housing-statistics/metropolitan-median-area-prices-and-affordability - - https://nces.ed.gov/programs/edge/Demographic/ACS - - https://datacenter.aecf.org/data/tables/4404-income--median-earnings-for-population-age-25-and-over-by-educational-attainment - - https://www.financialsamurai.com/the-median-homeownership-duration-is-too-short-to-build-real-wealth/ - - https://www.census.gov/data/tables/2016/demo/families/cps-2016.html - - https://pmc.ncbi.nlm.nih.gov/articles/PMC3000020/ - - https://www.statista.com/statistics/233301/median-household-income-in-the-united-states-by-education/ - - https://www.migrationpolicy.org/sites/default/files/publications/mpi_nciip_parents-children-0-4-and-5-10-us-2021_final.pdf - - https://www2.census.gov/ces/wp/2024/CES-WP-24-30.pdf - - https://www.bls.gov/news.release/famee.t04.htm - - https://www.nmhc.org/research-insight/quick-facts-figures/quick-facts-resident-demographics/households-with-children/ - - https://www.census.gov/data/tables/2023/demo/families/cps-2023.html - - https://www.census.gov/data/tables/2019/demo/families/cps-2019.html - - https://magicdoor.com/blog/us-renter-and-homeowner-statistics/ - - https://worldpopulationreview.com/state-rankings/median-household-income-by-state - - https://nces.ed.gov/programs/digest/d09/tables/dt09_019.asp - - https://www.redfin.com/us-housing-market - - https://www.census.gov/library/stories/2025/09/education-and-income.html - - https://onlinelibrary.wiley.com/doi/10.1111/manc.12421 - - https://www.census.gov/data/tables/2020/demo/families/cps-2020.html - - https://www.statista.com/statistics/242028/number-of-married-couples-in-the-us-by-ethnic-group-and-combination/ - - https://www.census.gov/library/stories/2024/11/family-households.html - - https://www.dol.gov/agencies/wb/data/family-status - - https://usahello.org/education/children/grade-levels/ - - https://www.census.gov/library/visualizations/interactive/parents-living-arrangements.html - - https://bjs.ojp.gov/content/pub/pdf/cusrancvs.pdf - - https://www.hiringlab.org/2026/02/11/january-2026-jobs-report/ - - https://populationeducation.org/resource/historic-average-number-of-children-per-u-s-family-infographic/ - - https://www.rubyhome.com/blog/homeowners-vs-renters-stats/ - - https://www.studycountry.com/wiki/what-percent-of-americans-live-in-suburbs - - https://www.statista.com/statistics/183657/average-size-of-a-family-in-the-us/ - - https://eyeonhousing.org/2024/10/mortgage-free-homeowners-by-congressional-districts/ - - https://www.lendingtree.com/home/mortgage/u-s-mortgage-market-statistics/ - - https://www.bls.gov/emp/ - - https://www.themortgagenote.org/more-americans-are-mortgage-free-homeowners/ - - https://www.census.gov/library/working-papers/2023/demo/SEHSD-WP2023-10.html - - https://www.childstats.gov/americaschildren/tables.asp - - https://nces.ed.gov/programs/coe/pdf/Indicator_CCE/coe_cce_2016_05.pdf - - https://nces.ed.gov/programs/coe/indicator/cba - - https://finance.yahoo.com/personal-finance/mortgages/article/median-home-price-by-state-151223005.html - - https://www.census.gov/data/tables/time-series/demo/income-poverty/cps-hinc/hinc-04.html - - https://en.wikipedia.org/wiki/Age_disparity_in_sexual_relationships - - https://pmc.ncbi.nlm.nih.gov/articles/PMC12176447/ - - https://www.census.gov/newsroom/press-releases/2025/acs-1-year-estimates.html - - https://premiertucsonhomes.com/tucson-home-values/ - - https://www.census.gov/newsroom/press-releases/2022/urban-rural-populations.html - - https://www.childstats.gov/americaschildren/tables/pop1.asp - - https://www.urban.org/sites/default/files/publication/98729/millennial_homeownership_0.pdf - - https://www.census.gov/library/stories/2021/04/number-of-children-living-only-with-their-mothers-has-doubled-in-past-50-years.html - - https://www.stanmor.com/blog/the-numbers-behind-homeowners-exploring-homeownership-statistics - - https://www.statista.com/topics/12130/k-12-education-in-the-united-states/ - - https://eduww.net/parent-resources/school-age-grade-levels/ - - https://read.dukeupress.edu/demography/article/61/5/1293/390842/Eight-Decades-of-Educational-Assortative-Mating-A - - https://www.axios.com/2025/11/16/mortgage-free-homes-map - - https://www.census.gov/data/tables/time-series/demo/families/families.html - - https://www.journals.uchicago.edu/doi/10.1086/702018 - - https://www.prb.org/resources/u-s-household-composition-shifts-as-the-population-grows-older-more-young-adults-live-with-parents/ - - https://www.huduser.gov/portal/pdredge/pdr-edge-housingat250-article-071025.html - - https://www.flexjobs.com/blog/post/stats-about-working-parents-us - - https://www.pewresearch.org/social-trends/2017/05/18/intermarriage-in-the-u-s-50-years-after-loving-v-virginia/ - - https://www.newyorkfed.org/research/staff_reports/sr682.html - - https://nces.ed.gov/programs/coe/indicator/cce/family-characteristics - - https://www.kidsdata.org/topic/557/children-rural-urban/pie - - https://nces.ed.gov/programs/digest/d18/tables/dt18_101.40.asp - - https://ipropertymanagement.com/research/average-length-of-homeownership - - https://www.visualcapitalist.com/mapped-how-much-of-each-u-s-states-population-lives-in-cities/ - - https://fred.stlouisfed.org/series/MSPUS - - https://www.census.gov/library/publications/2024/demo/p20-587.pdf - - https://en.wikipedia.org/wiki/Interracial_marriage_in_the_United_States - - https://www2.census.gov/library/publications/2024/demo/p20-587.pdf - - https://www.census.gov/library/stories/2018/07/interracial-marriages.html - - https://www.census.gov/newsroom/press-releases/2026/acs-5-year-gains-educational-attainment.html - - https://www.statista.com/statistics/679812/number-of-households-with-children-by-age/ - - https://dqydj.com/income-by-education/ - - https://www.bls.gov/opub/mlr/2026/article/industry-and-occupational-employment-projections-overview.htm - - https://www.census.gov/library/stories/2022/11/homeownership-by-young-households-below-pre-great-recession-levels.html - - https://americas.uli.org/suburbs-demographics-report/ - - https://www.census.gov/newsroom/facts-for-features/2015/cb15-ff17.html - - https://www.prb.org/resources/most-americans-marry-within-their-race/ - - https://nowbam.com/over-40-of-u-s-homeowners-are-mortgage-free/ - - https://nces.ed.gov/programs/raceindicators/indicator_rac.asp - - https://pmc.ncbi.nlm.nih.gov/articles/PMC4850739/ - - https://www.parealtors.org/blog/college-grads-more-likely-to-be-homeowners/ - - https://nces.ed.gov/programs/edge/Demographic/ACSchildren - - https://www.census.gov/topics/families/families-and-households/data/tables.html - - https://www.planning.org/pas/reports/report210.htm - - https://www.statista.com/statistics/457786/number-of-children-in-the-us-by-age/ - - https://www.childstats.gov/americaschildren23/family1.asp - - https://www.census.gov/library/publications/2025/demo/p70-204.html - - https://www.thezebra.com/resources/home/average-length-of-homeownership/ - - https://constructioncoverage.com/research/where-residents-have-paid-off-homes - - https://fivethirtyeight.com/features/whats-the-average-age-difference-in-a-couple/ - - https://aspe.hhs.gov/sites/default/files/private/pdf/172181/pf2.pdf - - https://www.statista.com/statistics/242189/disitribution-of-households-in-the-us-by-household-size/ - - https://www.bls.gov/opub/ted/2017/employment-in-families-with-children-in-2016.htm - - https://www.psychologytoday.com/us/blog/the-asymmetric-brain/202510/age-gaps-in-relationships-7-scientific-facts - - https://www.census.gov/content/dam/Census/library/publications/2022/demo/p70-174.pdf - - https://pmc.ncbi.nlm.nih.gov/articles/PMC5130094/ - - https://www.bls.gov/news.release/pdf/empsit.pdf - - https://www.pewresearch.org/short-reads/2023/08/03/almost-1-in-5-stay-at-home-parents-in-the-us-are-dads/ - - https://www.quora.com/How-many-U-S-households-have-children-between-the-ages-of-3-and-10 - - https://www.bls.gov/ces/ - - https://datacenter.kidscount.org/data/tables/101-child-population-by-age-group - - https://www.bls.gov/news.release/famee.nr0.htm - - https://www.synerahomes.com/blog/st-louis-suburbs-homes-prices-trends-and-top-neighborhoods-for-2026 - - https://flowingdata.com/2024/03/13/common-age-differences-married-couples/ - - https://www.consumeraffairs.com/finance/what-is-the-average-length-of-home-ownership.html - - https://www.housingwire.com/articles/homeowners-are-staying-in-place-twice-as-long-as-they-did-20-years-ago-redfin/ - - https://www.fastcompany.com/91429491/housing-market-mortgage-free-40-of-u-s-home-owners-why-the-number-keeps-growing - - https://www.bls.gov/opub/ted/2012/ted_20120427.htm -attributes: -- name: age - type: int - category: universal - description: Age of the individual in years - scope: individual - sampling: - strategy: independent - distribution: - type: normal - mean: 42.0 - std: 7.0 - min: 25.0 - max: 65.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: extrapolated - source: https://nces.ed.gov/programs/coe/pdf/Indicator_CCE/coe_cce_2016_05.pdf - note: Extrapolated from data showing parents of school-age children (5-17) tend - to be in their late 30s to mid-40s. Suburban homeowners tend to be slightly - older than average parents. - constraints: - - type: hard_min - value: 25.0 - expression: null - reason: Minimum realistic age for parent with school-age children - - type: hard_max - value: 65.0 - expression: null - reason: Maximum typical age for parent with K-12 children -- name: gender - type: categorical - category: universal - description: Gender identity of the individual - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - male - - female - weights: - - 0.48 - - 0.52 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: strong - method: researched - source: https://www.census.gov/library/stories/2024/11/family-households.html - note: General US population gender distribution, slightly more females in household - reference persons - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: state - type: categorical - category: universal - description: US state where the household is located - scope: household - sampling: - strategy: independent - distribution: - type: categorical - options: - - CA - - TX - - FL - - NY - - PA - - IL - - OH - - GA - - NC - - MI - - NJ - - VA - - WA - - AZ - - MA - - TN - - IN - - MO - - MD - - WI - - CO - - MN - - SC - - AL - - LA - - KY - - OR - - OK - - CT - - UT - - IA - - NV - - AR - - MS - - KS - - NM - - NE - - ID - - WV - - HI - - NH - - ME - - MT - - RI - - DE - - SD - - ND - - AK - - VT - - WY - weights: - - 0.118 - - 0.088 - - 0.065 - - 0.059 - - 0.039 - - 0.038 - - 0.035 - - 0.032 - - 0.032 - - 0.03 - - 0.027 - - 0.026 - - 0.023 - - 0.022 - - 0.021 - - 0.021 - - 0.02 - - 0.019 - - 0.018 - - 0.018 - - 0.017 - - 0.017 - - 0.016 - - 0.015 - - 0.014 - - 0.013 - - 0.013 - - 0.012 - - 0.011 - - 0.01 - - 0.01 - - 0.009 - - 0.009 - - 0.009 - - 0.008 - - 0.006 - - 0.006 - - 0.005 - - 0.004 - - 0.004 - - 0.004 - - 0.003 - - 0.003 - - 0.003 - - 0.003 - - 0.002 - - 0.002 - - 0.002 - - 0.002 - - 0.002 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: strong - method: researched - source: https://www.census.gov/data/tables/time-series/demo/families/households.html - note: Based on US population distribution by state from Census data - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: urban_rural - type: categorical - category: universal - description: Whether the household is in a suburban area (all suburban for this - population) - scope: household - sampling: - strategy: independent - distribution: - type: categorical - options: - - suburban - weights: - - 1.0 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: strong - method: researched - source: https://en.wikipedia.org/wiki/Homeownership_in_the_United_States - note: All agents in this population are suburban by specification. Three quarters - of suburban households are homeowners. - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: education_level - type: categorical - category: universal - description: Highest level of education completed (high school, associate, bachelor, - graduate) - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - high_school - - associate - - bachelor - - graduate - weights: - - 0.15 - - 0.25 - - 0.4 - - 0.2 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: strong - method: researched - source: https://www.point2homes.com/news/us-real-estate-news/homeownership-by-education-us.html - note: 'Based on homeowner education distribution: 70% have some college or more, - with bachelor''s degree holders at 40%. Suburban homeowners skew higher education - than general population.' - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: marital_status - type: categorical - category: universal - description: Marital status of the individual (married, divorced, single, widowed) - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - married - - divorced - - single - - widowed - weights: - - 0.75 - - 0.15 - - 0.08 - - 0.02 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: extrapolated - source: https://www.stanmor.com/blog/the-numbers-behind-homeowners-exploring-homeownership-statistics - note: Married couples have highest homeownership rates. Among families with school-age - children, married couples dominate, especially in suburban homeownership. - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: household_size - type: int - category: universal - description: Total number of people living in the household - scope: household - sampling: - strategy: independent - distribution: - type: normal - mean: 4.2 - std: 0.9 - min: 2.0 - max: 8.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: [] - modifiers: [] - grounding: - level: strong - method: researched - source: https://www.statista.com/statistics/183657/average-size-of-a-family-in-the-us/ - note: Average US household with children is larger than overall average (2.61). - Families with school-age children typically have 3-5 members. - constraints: - - type: hard_min - value: 2.0 - expression: null - reason: Minimum household size (parent + child) - - type: hard_max - value: 8.0 - expression: null - reason: Maximum typical household size - - type: expression - value: null - expression: num_children <= household_size - 1 - reason: At least one adult must be present with children -- name: employment_status - type: categorical - category: universal - description: Current employment status (full-time, part-time, self-employed, unemployed) - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - full_time - - part_time - - self_employed - - unemployed - weights: - - 0.72 - - 0.12 - - 0.12 - - 0.04 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: strong - method: researched - source: https://www.bls.gov/news.release/famee.nr0.htm - note: Based on BLS Employment Characteristics of Families 2024 data. Parents with - children have high labor force participation, with most employed full-time. - Homeownership requires stable income. - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: num_children - type: int - category: population_specific - description: Number of school-age children (K-12) in the household - scope: household - sampling: - strategy: independent - distribution: - type: normal - mean: 2.1 - std: 0.8 - min: 1.0 - max: 5.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: [] - modifiers: [] - grounding: - level: strong - method: researched - source: https://www.census.gov/library/stories/2024/11/family-households.html - note: Census data shows 19% of family households have 1 child under 18, 16% have - 2, and 9% have 3+. Average births per woman is 1.7, but families with school-age - children tend toward 2. - constraints: - - type: hard_min - value: 1.0 - expression: null - reason: Population specified as having school-age children - - type: hard_max - value: 5.0 - expression: null - reason: Maximum typical number of school-age children - - type: expression - value: null - expression: num_children <= household_size - 1 - reason: Number of children cannot exceed household size minus adults -- name: community_involvement - type: categorical - category: population_specific - description: Level of involvement in community/neighborhood activities (high, medium, - low, none) - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - high - - medium - - low - - none - weights: - - 0.25 - - 0.4 - - 0.25 - - 0.1 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: low - method: estimated - source: null - note: No specific data found for suburban parents' community involvement. Estimated - based on assumption that suburban homeowners with children have moderate to - high engagement in schools and community activities. - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: openness - type: float - category: personality - description: Big Five trait reflecting openness to experience and intellectual curiosity - scope: individual - sampling: - strategy: independent - distribution: - type: normal - mean: 3.5 - std: 0.7 - min: 1.0 - max: 5.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: extrapolated - source: null - note: Based on Big Five personality trait research showing general population - mean around 3.5 on 5-point scale. Suburban homeowners may be slightly more conventional, - but kept at population mean. - constraints: - - type: hard_min - value: 1.0 - expression: null - reason: Minimum Big Five scale value - - type: hard_max - value: 5.0 - expression: null - reason: Maximum Big Five scale value -- name: conscientiousness - type: float - category: personality - description: Big Five trait reflecting organization, dependability, and self-discipline - scope: individual - sampling: - strategy: independent - distribution: - type: normal - mean: 3.8 - std: 0.6 - min: 1.0 - max: 5.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: extrapolated - source: null - note: Homeownership and parenting typically correlate with higher conscientiousness. - Set slightly above population mean (3.5) to reflect responsibility and organization - required for homeownership. - constraints: - - type: hard_min - value: 1.0 - expression: null - reason: Minimum Big Five scale value - - type: hard_max - value: 5.0 - expression: null - reason: Maximum Big Five scale value -- name: extraversion - type: float - category: personality - description: Big Five trait reflecting sociability, assertiveness, and energy level - scope: individual - sampling: - strategy: independent - distribution: - type: normal - mean: 3.4 - std: 0.75 - min: 1.0 - max: 5.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: extrapolated - source: null - note: Based on Big Five population norms. Suburban parents show normal distribution - of extraversion, kept near population mean of 3.4-3.5. - constraints: - - type: hard_min - value: 1.0 - expression: null - reason: Minimum Big Five scale value - - type: hard_max - value: 5.0 - expression: null - reason: Maximum Big Five scale value -- name: agreeableness - type: float - category: personality - description: Big Five trait reflecting compassion, cooperativeness, and trust in - others - scope: individual - sampling: - strategy: independent - distribution: - type: normal - mean: 3.7 - std: 0.65 - min: 1.0 - max: 5.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: extrapolated - source: null - note: Parents and community-oriented individuals tend to score slightly higher - on agreeableness. Set above population mean (3.5) to reflect cooperative nature - of parenting and suburban community living. - constraints: - - type: hard_min - value: 1.0 - expression: null - reason: Minimum Big Five scale value - - type: hard_max - value: 5.0 - expression: null - reason: Maximum Big Five scale value -- name: neuroticism - type: float - category: personality - description: Big Five trait reflecting emotional stability and tendency toward anxiety - scope: individual - sampling: - strategy: independent - distribution: - type: normal - mean: 3.0 - std: 0.75 - min: 1.0 - max: 5.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: extrapolated - source: null - note: Based on Big Five population norms around 3.0-3.2. Homeowners with stable - employment may have slightly lower neuroticism, but kept near population mean - for realism. - constraints: - - type: hard_min - value: 1.0 - expression: null - reason: Minimum Big Five scale value - - type: hard_max - value: 5.0 - expression: null - reason: Maximum Big Five scale value -- name: risk_tolerance - type: categorical - category: personality - description: Individual's tolerance for financial and lifestyle risk (low, medium, - high) - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - low - - medium - - high - weights: - - 0.35 - - 0.5 - - 0.15 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: low - method: estimated - source: null - note: Estimated based on assumption that suburban homeowners with children tend - toward conservative financial approaches (mortgage, stable neighborhood) but - with normal variation. Skewed toward low-medium risk tolerance. - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: education_priority - type: categorical - category: personality - description: How highly the individual prioritizes children's education (low, medium, - high) - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - low - - medium - - high - weights: - - 0.05 - - 0.3 - - 0.65 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: medium - method: extrapolated - source: https://nces.ed.gov/programs/coe/pdf/Indicator_CCE/coe_cce_2016_05.pdf - note: Suburban homeowners specifically choose neighborhoods for schools. This - population likely has high education priority given their investment in stable - housing and school districts. Strongly skewed toward high priority. - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: environmental_consciousness - type: categorical - category: personality - description: Level of concern for environmental issues and sustainability (low, - medium, high) - scope: individual - sampling: - strategy: independent - distribution: - type: categorical - options: - - low - - medium - - high - weights: - - 0.25 - - 0.5 - - 0.25 - formula: null - depends_on: [] - modifiers: [] - grounding: - level: low - method: estimated - source: null - note: No specific data found for this population. Estimated with normal distribution - across levels, assuming typical variation in environmental consciousness among - suburban homeowners. - constraints: - - type: spec_expression - value: null - expression: sum(weights)==1 - reason: Weights must sum to 1 -- name: occupation_category - type: categorical - category: universal - description: General occupational category (professional, management, service, trades, - etc.) - scope: individual - sampling: - strategy: conditional - distribution: - type: categorical - options: - - professional - - management - - service - - sales - - trades - - administrative - weights: - - 0.35 - - 0.25 - - 0.12 - - 0.13 - - 0.1 - - 0.05 - formula: null - depends_on: - - employment_status - modifiers: - - when: employment_status == 'full_time' - multiply: null - add: null - weight_overrides: - professional: 0.35 - management: 0.25 - service: 0.1 - sales: 0.15 - trades: 0.1 - administrative: 0.05 - probability_override: null - - when: employment_status == 'part_time' - multiply: null - add: null - weight_overrides: - professional: 0.2 - management: 0.1 - service: 0.3 - sales: 0.2 - trades: 0.05 - administrative: 0.15 - probability_override: null - - when: employment_status == 'self_employed' - multiply: null - add: null - weight_overrides: - professional: 0.3 - management: 0.2 - service: 0.15 - sales: 0.15 - trades: 0.15 - administrative: 0.05 - probability_override: null - - when: employment_status == 'unemployed' - multiply: null - add: null - weight_overrides: - professional: 0.2 - management: 0.15 - service: 0.25 - sales: 0.15 - trades: 0.15 - administrative: 0.1 - probability_override: null - grounding: - level: medium - method: researched - source: BLS Employment Projections 2024-34 and occupational distribution data - note: Professional and management roles dominate suburban homeowner demographics - with school-age children. Distribution reflects higher education levels (high - school to graduate degrees) and full employment status typical of this population. - constraints: [] -- name: individual_income - type: int - category: universal - description: Annual individual income in USD before taxes - scope: individual - sampling: - strategy: conditional - distribution: - type: normal - mean: 75000.0 - std: 28000.0 - min: 25000.0 - max: 250000.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: - - occupation_category - - education_level - modifiers: - - when: occupation_category == 'professional' and education_level == 'graduate' - multiply: 1.4 - add: 15000.0 - weight_overrides: null - probability_override: null - - when: occupation_category == 'professional' and education_level == 'bachelor' - multiply: 1.2 - add: 5000.0 - weight_overrides: null - probability_override: null - - when: occupation_category == 'management' and education_level == 'graduate' - multiply: 1.5 - add: 20000.0 - weight_overrides: null - probability_override: null - - when: occupation_category == 'management' and education_level == 'bachelor' - multiply: 1.3 - add: 10000.0 - weight_overrides: null - probability_override: null - - when: occupation_category == 'service' - multiply: 0.6 - add: -15000.0 - weight_overrides: null - probability_override: null - - when: occupation_category == 'sales' and education_level == 'bachelor' - multiply: 1.0 - add: 0.0 - weight_overrides: null - probability_override: null - - when: occupation_category == 'trades' - multiply: 0.85 - add: -5000.0 - weight_overrides: null - probability_override: null - - when: occupation_category == 'administrative' - multiply: 0.7 - add: -10000.0 - weight_overrides: null - probability_override: null - grounding: - level: strong - method: researched - source: Census Bureau 2024 data and DQYDJ income by education statistics - note: Median individual income $53,010 nationally in 2024, but suburban homeowners - with children skew higher. Base distribution centered at $75k reflects education - mix (high school to graduate) and professional/management occupations dominant - in this population. - constraints: - - type: hard_min - value: 25000.0 - expression: null - reason: Minimum income for employed individuals in this demographic - - type: hard_max - value: 250000.0 - expression: null - reason: Upper bound for individual income before extreme outliers -- name: household_income - type: int - category: universal - description: Total annual household income in USD before taxes - scope: household - sampling: - strategy: conditional - distribution: - type: normal - mean: 105000.0 - std: 38000.0 - min: 45000.0 - max: 350000.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: - - individual_income - - household_size - modifiers: - - when: individual_income > 100000 and household_size >= 4 - multiply: 1.5 - add: 20000.0 - weight_overrides: null - probability_override: null - - when: individual_income > 100000 and household_size < 4 - multiply: 1.3 - add: 10000.0 - weight_overrides: null - probability_override: null - - when: individual_income <= 50000 - multiply: 0.9 - add: -5000.0 - weight_overrides: null - probability_override: null - - when: household_size >= 5 - multiply: 1.2 - add: 5000.0 - weight_overrides: null - probability_override: null - grounding: - level: strong - method: researched - source: Census Bureau ACS 2020-2024 data showing US median household income $80,734 - note: Suburban homeowners with school-age children have higher incomes than national - median. Many are dual-income households. Base mean $105k reflects homeownership - requirements and child-rearing costs in suburban areas. - constraints: - - type: hard_min - value: 45000.0 - expression: null - reason: Minimum viable household income for suburban homeownership with children - - type: hard_max - value: 350000.0 - expression: null - reason: Upper bound before extreme high earners - - type: expression - value: null - expression: household_income >= individual_income - reason: Household income must be at least as high as individual income -- name: homeownership_years - type: int - category: population_specific - description: Number of years the household has owned their current home - scope: household - sampling: - strategy: conditional - distribution: - type: normal - mean: null - std: 5.5 - min: 1.0 - max: null - mean_formula: max(1, min(age - 30, 20)) - std_formula: null - min_formula: null - max_formula: age - 22 - formula: null - depends_on: - - age - modifiers: - - when: age < 35 - multiply: 0.5 - add: -2.0 - weight_overrides: null - probability_override: null - - when: age >= 50 - multiply: 1.3 - add: 3.0 - weight_overrides: null - probability_override: null - grounding: - level: strong - method: researched - source: Redfin and ConsumerAffairs 2024 data on homeownership tenure - note: 'National median homeownership duration is 11.9-12.3 years as of 2024. Formula - adjusts for age: younger homeowners (late 30s) likely owned 5-10 years, while - older (50+) may own 15-20 years. Cap at 20 years reflects that parents with - school-age children often purchased homes within last 15-20 years.' - constraints: - - type: hard_min - value: 1.0 - expression: null - reason: Must have owned for at least 1 year - - type: expression - value: null - expression: homeownership_years <= age - 22 - reason: Cannot have owned home longer than adult life (assuming minimum purchase - age 22) -- name: home_value - type: int - category: population_specific - description: Estimated market value of the owned home in USD - scope: household - sampling: - strategy: conditional - distribution: - type: normal - mean: 380000.0 - std: 145000.0 - min: 180000.0 - max: 1200000.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: - - state - - household_income - modifiers: - - when: state in ['CA', 'MA', 'NY', 'WA', 'HI'] - multiply: 1.8 - add: 150000.0 - weight_overrides: null - probability_override: null - - when: state in ['CO', 'OR', 'NJ', 'MD', 'CT'] - multiply: 1.4 - add: 80000.0 - weight_overrides: null - probability_override: null - - when: state in ['MS', 'AR', 'WV', 'AL', 'KY'] - multiply: 0.6 - add: -80000.0 - weight_overrides: null - probability_override: null - - when: household_income > 150000 - multiply: 1.4 - add: 50000.0 - weight_overrides: null - probability_override: null - - when: household_income < 80000 - multiply: 0.75 - add: -30000.0 - weight_overrides: null - probability_override: null - grounding: - level: strong - method: researched - source: Zillow Home Value Index, Redfin, and NAR data for 2025-2026 - note: 'National median home value $360,727 (Zillow Jan 2026), median sale price - $423,261-$446,000 (Redfin). Suburban homes typically higher than national median. - Mean $380k with large std dev captures state variation: CA/HI ($600k-800k+), - TX/FL ($300k-400k), Midwest ($250k-350k).' - constraints: - - type: hard_min - value: 180000.0 - expression: null - reason: Minimum suburban single-family home value - - type: hard_max - value: 1200000.0 - expression: null - reason: Upper bound for typical suburban homes before luxury segment -- name: mortgage_status - type: categorical - category: population_specific - description: Current mortgage status (paying mortgage, paid off, no mortgage) - scope: household - sampling: - strategy: conditional - distribution: - type: categorical - options: - - paying_mortgage - - paid_off - weights: - - 0.72 - - 0.28 - formula: null - depends_on: - - homeownership_years - - age - modifiers: - - when: homeownership_years >= 15 and age >= 50 - multiply: null - add: null - weight_overrides: - paying_mortgage: 0.4 - paid_off: 0.6 - probability_override: null - - when: homeownership_years < 10 - multiply: null - add: null - weight_overrides: - paying_mortgage: 0.95 - paid_off: 0.05 - probability_override: null - - when: homeownership_years >= 10 and homeownership_years < 15 - multiply: null - add: null - weight_overrides: - paying_mortgage: 0.75 - paid_off: 0.25 - probability_override: null - grounding: - level: strong - method: researched - source: Census Bureau 2024 ACS and Federal Reserve SHED data - note: Nationally, 40.3% of all homeowners are mortgage-free (2024), but this includes - many retirees. For working-age homeowners under 65, only 27.7% own free and - clear. Suburban parents with school-age children are primarily working-age (35-55), - so base rate set at 28% paid off, 72% with mortgage. - constraints: [] -- name: youngest_child_age - type: int - category: population_specific - description: Age of the youngest school-age child in years - scope: household - sampling: - strategy: conditional - distribution: - type: normal - mean: 8.0 - std: 3.5 - min: 5.0 - max: 17.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: - - num_children - modifiers: - - when: num_children == 1 - multiply: 1.2 - add: 1.0 - weight_overrides: null - probability_override: null - - when: num_children >= 3 - multiply: 0.8 - add: -1.0 - weight_overrides: null - probability_override: null - grounding: - level: medium - method: estimated - source: Population demographics and typical family patterns - note: For households with mean 2.1 school-age children, youngest child typically - in elementary or middle school range. Mean of 8 years reflects mix of families - with young elementary (5-7), middle elementary (8-10), and middle school (11-13) - as youngest. - constraints: - - type: hard_min - value: 5.0 - expression: null - reason: School-age children are K-12, minimum age 5 (kindergarten) - - type: hard_max - value: 17.0 - expression: null - reason: Maximum K-12 age before college - - type: expression - value: null - expression: youngest_child_age <= oldest_child_age - reason: Youngest child cannot be older than oldest child -- name: oldest_child_age - type: int - category: population_specific - description: Age of the oldest school-age child in years - scope: household - sampling: - strategy: conditional - distribution: - type: normal - mean: 13.0 - std: 3.8 - min: 5.0 - max: 18.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: youngest_child_age + 15 - formula: null - depends_on: - - num_children - - youngest_child_age - modifiers: - - when: num_children == 1 - multiply: 1.0 - add: 0.0 - weight_overrides: null - probability_override: null - - when: num_children >= 3 - multiply: 1.2 - add: 2.0 - weight_overrides: null - probability_override: null - grounding: - level: medium - method: estimated - source: Family demographic patterns and typical child spacing - note: With mean 2.1 children and youngest averaging 8 years, oldest child typically - in middle/high school range. Mean 13 years allows for 2-5 year spacing between - children, common in American families. - constraints: - - type: hard_min - value: 5.0 - expression: null - reason: School-age minimum (K-12) - - type: hard_max - value: 18.0 - expression: null - reason: Maximum K-12 age - - type: expression - value: null - expression: oldest_child_age >= youngest_child_age - reason: Oldest child must be at least as old as youngest -- name: school_type - type: categorical - category: population_specific - description: Type of school children attend (public, private, charter, homeschool) - scope: household - sampling: - strategy: conditional - distribution: - type: categorical - options: - - public - - private - - charter - - homeschool - weights: - - 0.75 - - 0.15 - - 0.07 - - 0.03 - formula: null - depends_on: - - household_income - modifiers: - - when: household_income > 150000 - multiply: null - add: null - weight_overrides: - public: 0.4 - private: 0.45 - charter: 0.1 - homeschool: 0.05 - probability_override: null - - when: household_income >= 100000 and household_income <= 150000 - multiply: null - add: null - weight_overrides: - public: 0.6 - private: 0.25 - charter: 0.12 - homeschool: 0.03 - probability_override: null - - when: household_income < 100000 - multiply: null - add: null - weight_overrides: - public: 0.75 - private: 0.1 - charter: 0.12 - homeschool: 0.03 - probability_override: null - grounding: - level: medium - method: researched - source: National Center for Education Statistics (NCES) data - note: 'Nationally, approximately 83% of K-12 students attend public schools (including - charters), 10% private, 3-4% homeschool. Suburban families slightly higher private - school enrollment than national average. Base weights: 75% public, 15% private, - 7% charter, 3% homeschool.' - constraints: [] -- name: dual_income_household - type: boolean - category: population_specific - description: Whether the household has two income earners - scope: household - sampling: - strategy: conditional - distribution: - type: boolean - probability_true: 0.68 - formula: null - depends_on: - - marital_status - - household_size - modifiers: - - when: marital_status == 'married' and household_size >= 3 - multiply: null - add: null - weight_overrides: null - probability_override: 0.8 - - when: marital_status == 'married' and household_size < 3 - multiply: null - add: null - weight_overrides: null - probability_override: 0.7 - - when: marital_status in ['divorced', 'single'] - multiply: null - add: null - weight_overrides: null - probability_override: 0.3 - - when: marital_status == 'widowed' - multiply: null - add: null - weight_overrides: null - probability_override: 0.2 - grounding: - level: medium - method: estimated - source: BLS and Census labor force participation data - note: Among married couples with children, approximately 65-70% are dual-earner - households. For suburban homeowners with school-age children where homeownership - and childcare costs are high, dual income is common. Base probability 68%. - constraints: [] -- name: commute_time - type: int - category: population_specific - description: One-way commute time to work in minutes - scope: individual - sampling: - strategy: conditional - distribution: - type: normal - mean: 28.0 - std: 12.0 - min: 5.0 - max: 90.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: - - employment_status - modifiers: - - when: employment_status == 'full_time' - multiply: 1.1 - add: 2.0 - weight_overrides: null - probability_override: null - - when: employment_status == 'part_time' - multiply: 0.85 - add: -3.0 - weight_overrides: null - probability_override: null - - when: employment_status == 'self_employed' - multiply: 0.7 - add: -5.0 - weight_overrides: null - probability_override: null - - when: employment_status == 'unemployed' - multiply: 0.0 - add: -28.0 - weight_overrides: null - probability_override: null - grounding: - level: medium - method: researched - source: Census Bureau American Community Survey commute data - note: National mean commute time approximately 26-27 minutes. Suburban residents - typically have slightly longer commutes (28-30 min) than national average as - they commute to urban job centers. Distribution includes some remote workers - with minimal commute. - constraints: - - type: hard_min - value: 5.0 - expression: null - reason: Minimum commute time for those who work outside home - - type: hard_max - value: 90.0 - expression: null - reason: Maximum reasonable one-way commute -- name: vehicle_count - type: int - category: population_specific - description: Number of vehicles owned by the household - scope: household - sampling: - strategy: conditional - distribution: - type: normal - mean: 2.5 - std: 0.7 - min: 1.0 - max: 5.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: - - household_size - - household_income - modifiers: - - when: household_size >= 5 and household_income > 100000 - multiply: 1.3 - add: 1.0 - weight_overrides: null - probability_override: null - - when: household_size <= 3 - multiply: 0.8 - add: 0.0 - weight_overrides: null - probability_override: null - - when: household_income < 80000 - multiply: 0.75 - add: -0.5 - weight_overrides: null - probability_override: null - grounding: - level: medium - method: estimated - source: Federal Highway Administration and household transportation patterns - note: Suburban households with school-age children typically own 2-3 vehicles. - With mean household size 4.2 and dual-earner prevalence, 2-3 vehicles common - for work commutes, school transport, and activities. Mean 2.5 reflects this - pattern. - constraints: - - type: hard_min - value: 1.0 - expression: null - reason: Suburban households require at least one vehicle - - type: hard_max - value: 5.0 - expression: null - reason: Practical maximum for typical household -- name: pta_membership - type: boolean - category: population_specific - description: Whether the individual is a member of Parent-Teacher Association - scope: individual - sampling: - strategy: conditional - distribution: - type: boolean - probability_true: 0.35 - formula: null - depends_on: - - community_involvement - modifiers: - - when: community_involvement == 'high' - multiply: null - add: null - weight_overrides: null - probability_override: 0.85 - - when: community_involvement == 'medium' - multiply: null - add: null - weight_overrides: null - probability_override: 0.55 - - when: community_involvement == 'low' - multiply: null - add: null - weight_overrides: null - probability_override: 0.25 - - when: community_involvement == 'none' - multiply: null - add: null - weight_overrides: null - probability_override: 0.05 - grounding: - level: low - method: estimated - source: National PTA membership trends and community involvement patterns - note: National PTA membership approximately 4 million across 22,000+ locations. - Not all parents with school-age children join. Estimated 30-40% of suburban - parents with medium-high community involvement join PTA. Base probability 35%. - constraints: [] -- name: home_office - type: boolean - category: population_specific - description: Whether the home has a dedicated home office space - scope: household - sampling: - strategy: conditional - distribution: - type: boolean - probability_true: 0.48 - formula: null - depends_on: - - employment_status - - home_value - modifiers: - - when: employment_status == 'self_employed' - multiply: null - add: null - weight_overrides: null - probability_override: 0.85 - - when: employment_status == 'full_time' and home_value > 400000 - multiply: null - add: null - weight_overrides: null - probability_override: 0.65 - - when: employment_status == 'full_time' and home_value <= 400000 - multiply: null - add: null - weight_overrides: null - probability_override: 0.45 - - when: employment_status == 'part_time' - multiply: null - add: null - weight_overrides: null - probability_override: 0.4 - - when: employment_status == 'unemployed' - multiply: null - add: null - weight_overrides: null - probability_override: 0.3 - grounding: - level: medium - method: estimated - source: Post-pandemic work-from-home trends and housing patterns - note: Post-2020, dedicated home office space became more common. Suburban homes - with higher values and professional/management workers have higher likelihood. - Estimated 45-50% of suburban professional households have dedicated home office - space. Base probability 48%. - constraints: [] -- name: household_savings - type: int - category: population_specific - description: Total household savings and investments in USD - scope: household - sampling: - strategy: conditional - distribution: - type: lognormal - mean: 75000.0 - std: 95000.0 - min: 5000.0 - max: 800000.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: - - household_income - - age - modifiers: - - when: household_income > 150000 and age >= 45 - multiply: 2.0 - add: 50000.0 - weight_overrides: null - probability_override: null - - when: household_income > 150000 and age < 45 - multiply: 1.5 - add: 25000.0 - weight_overrides: null - probability_override: null - - when: household_income < 80000 - multiply: 0.5 - add: -20000.0 - weight_overrides: null - probability_override: null - - when: age >= 50 - multiply: 1.6 - add: 30000.0 - weight_overrides: null - probability_override: null - - when: age < 35 - multiply: 0.6 - add: -15000.0 - weight_overrides: null - probability_override: null - grounding: - level: medium - method: estimated - source: Federal Reserve Survey of Consumer Finances and savings patterns - note: Household savings highly skewed (lognormal). Median US household savings - much lower than mean. For suburban homeowners aged 35-55 with median income - $105k, typical savings $40k-120k. Lognormal with mean $75k, high std dev captures - wide variation. - constraints: - - type: hard_min - value: 5000.0 - expression: null - reason: Minimum emergency savings for homeowners - - type: hard_max - value: 800000.0 - expression: null - reason: Upper bound for typical household savings before ultra-wealthy -- name: childcare_expense - type: int - category: population_specific - description: Monthly childcare expenses in USD (afterschool, daycare, etc.) - scope: household - sampling: - strategy: conditional - distribution: - type: normal - mean: 650.0 - std: 420.0 - min: 0.0 - max: 2500.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: - - num_children - - youngest_child_age - modifiers: - - when: num_children >= 3 and youngest_child_age < 6 - multiply: 2.0 - add: 500.0 - weight_overrides: null - probability_override: null - - when: num_children >= 3 and youngest_child_age >= 6 - multiply: 1.5 - add: 200.0 - weight_overrides: null - probability_override: null - - when: youngest_child_age >= 12 - multiply: 0.4 - add: -300.0 - weight_overrides: null - probability_override: null - - when: youngest_child_age < 6 - multiply: 1.8 - add: 400.0 - weight_overrides: null - probability_override: null - - when: num_children == 1 - multiply: 0.7 - add: -100.0 - weight_overrides: null - probability_override: null - grounding: - level: medium - method: estimated - source: Childcare cost surveys and BLS Consumer Expenditure data - note: School-age childcare less expensive than infant/toddler care. Afterschool - programs typically $200-500/child/month. Some families need no care (stay-at-home - parent, older children), others need full afterschool care for 2+ children. - Mean $650/month reflects mix. - constraints: - - type: hard_min - value: 0.0 - expression: null - reason: Some families have no childcare expenses (older children, family help) - - type: hard_max - value: 2500.0 - expression: null - reason: Maximum for afterschool care for multiple school-age children -- name: extracurricular_count - type: int - category: population_specific - description: Number of extracurricular activities children are enrolled in (sports, - music, etc.) - scope: household - sampling: - strategy: conditional - distribution: - type: normal - mean: 3.2 - std: 1.8 - min: 0.0 - max: 10.0 - mean_formula: null - std_formula: null - min_formula: null - max_formula: null - formula: null - depends_on: - - num_children - - household_income - modifiers: - - when: num_children >= 3 and household_income > 120000 - multiply: 1.5 - add: 2.0 - weight_overrides: null - probability_override: null - - when: num_children == 1 - multiply: 0.8 - add: -0.5 - weight_overrides: null - probability_override: null - - when: household_income > 150000 - multiply: 1.4 - add: 1.5 - weight_overrides: null - probability_override: null - - when: household_income < 80000 - multiply: 0.65 - add: -1.0 - weight_overrides: null - probability_override: null - grounding: - level: low - method: estimated - source: Youth sports and activities participation surveys - note: Average US child participates in 1-2 organized activities (sports, music, - scouts, etc.). With mean 2.1 children per household, total household activities - approximately 2-4. Higher-income suburban families tend toward more activities. - Mean 3.2 reflects 1.5 activities per child on average. - constraints: - - type: hard_min - value: 0.0 - expression: null - reason: Some families have no organized extracurriculars - - type: hard_max - value: 10.0 - expression: null - reason: Maximum total activities across all children in household -sampling_order: -- age -- agreeableness -- community_involvement -- conscientiousness -- education_level -- education_priority -- employment_status -- commute_time -- environmental_consciousness -- extraversion -- gender -- homeownership_years -- household_size -- marital_status -- dual_income_household -- mortgage_status -- neuroticism -- num_children -- occupation_category -- individual_income -- household_income -- extracurricular_count -- household_savings -- openness -- pta_membership -- risk_tolerance -- school_type -- state -- home_value -- home_office -- urban_rural -- vehicle_count -- youngest_child_age -- 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