PSSimulation/input.txt at master · vksh224/PSSimulation · GitHub

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# 1 : Debug Mode
# 2 : Debug true Event
# 3 : Debug False Event
# 4 : Not Debug Mode - Main output
# 5 : Plot mode
# 6 : Generate files for plotting
# 7 : Decision making
# 8 : Published/Not Published - Old Code


isDebugMode = 6
sizeOfPopulation=1
noOfEpochs = 201
noOfPassiveUsers = 0
percOfSelfishReporters = 0.2
scenarioWidth = 100
#48 - 2400
#50 - 3600
#45 - 1200
eventWidth = 50
scoreWindowSize = 100
userMovementRange = 2
eventRegionRadius = 50

noOfUsers = 2400
noOfReporters = 800
noOfRaters = 1600
percOfMalReporters = 0.15
positiveRatingsPercForTrueEvent = 0.70
negativeRatingsPercForTrueEvent = 0.25
positiveRatingsPercForFalseEvent = 0.0
negativeRatingsPercForFalseEvent = 0.70

#percentage of honest reporters who report the event truly
#trueReporterPercAfterLearning = 0.7

#wrong perception
probOfHonestReportersReportTrueEventTruly = 0.5
probOfSelReportersReportTrueEventTruly = 0.75
probOfMalReportersReportTrueEventTruly = 0.75

#It means, whatever false event type was generated, reporters reported that exactly
probOfHonestReportersReportFalseEventTruly = 1
probOfSelReportersReportFalseEventTruly = 1
probOfMalReportersReportFalseEventTruly = 1


#probability of honest reporters to report the true event (when in range of event)
probOfHonestReportersWhoReportTrueEvent = 0.95

#probability of malicious reporters to report the true event
probOfMalReportersWhoReportTrueEvent = 0.05

#probability of selfish reporters to report the true event
probOfSelfishReportersWhoReportTrueEvent = 0.60

#probability of selfish reporters to report the false event
probOfSelfishReportersWhoReportFalseEvent = 0.50


#Recently added (In IQ2S and CNS paper, there were not there)
probOfHonestReportersWhoReportFalseEvent = 0.01
probOfMalReportersWhoReportFalseEvent = 1


thresholdN = 40

uncertainWeightMax = 0.5
uncertainA = 20
uncertainB = 0.08

beliefA = 20
beliefB = 0.08
beliefWeightMax = 1

#certain percentage of reporters will rate
simulationCase = 1

# Perc. of raters in the range of event who actually report
percOfRaters = 0.9

# If the reporter has reported NoOfTrueEvent > positiveRatingRatio * NoOfFalseEvent, then he is a honest reporter
positiveRatingRatioThres = 0.8

#percentage of ratings required out of maximum ratings received by any event to be considered for comparision
percOfTotalRatings = 0.0

#QOI of event for which the system considers it as a true event
qoiThreshold = 0

# we consider only those reporters who reported more than certain number of events
percOfEventsThreshold = 0.0

phi = 0.2

#prospect theory parameters for asymmetric QoI shape
alpha = 2.5
beta = 0.6
lambda = 3
referencePoint = 0.5
reward = 1000

#Decision making
weightReport = 0.5
weightReputation = 0.5

#report confidence
noOfReportsThresLow = 5
noOfReportsThresHigh = 10
reportScaleParamC = 1
reportShapeParamB = 1
reportVarphiParam = 0.3

#reputation confidence
reputationScaleParamC = 1
reputationShapeParamB = 1

#Event Type History
eventTheta = 0.88
eventPhi = 0.88
eventLambda = 2.25
eventDelta = 0.61

fracOfActualReportersThres = 0
fracOfActualReportersReputationThres = 0