Biases-in-the-Risk-Cube-Incose在風(fēng)險的立方體根據(jù)偏差課件

CognitiveBiases

inthe

RiskMatrixWilliamSiefert,M.S.

EricD.SmithBoeingSystemsEngineeringGraduateProgramMissouriUniversityofScienceandTechnology?2019SmithWilliamSiefert,M.S.“Fearofharmoughttobeproportionalnotmerelytothegravityoftheharm,butalsototheprobabilityoftheevent.”Logic,ortheArtofThinkingAntoineArnould,1662ConsequencexLikelihood=RiskRiskgraphingHyperboliccurvesHyperboliccurvesinlog-loggraphiso-risklines

5x5Risk“Cube”O(jiān)riginalCurrentObjectivevs.Subjectivedata"Campfireconversation"piecePresentSituationRiskmatricesarerecognizedbyindustryasthebestwayto:consistentlyquantifyrisks,aspartofarepeatableandquantifiableriskmanagementprocessRiskmatricesinvolvehuman:NumericaljudgmentCalibration–location,gradationRounding,CensoringDataupdatingoftenapproachedwithunderconfidenceoftendistrustedbydecisionmakersGoalRiskManagementimprovementandbetteruseoftheriskmatrixConfidenceincorrectassessmentofprobabilityandvalueAvoidanceofspecificmistakesRecommendedactionsHeuristicsandBiasesDanielKahnemanwontheNobelPrizeinEconomicsin2019"forhavingintegratedinsightsfrompsychologicalresearchintoeconomicscience,especiallyconcerninghumanjudgmentanddecision-makingunderuncertainty.“Similaritiesbetweencognitivebiasexperimentsandtheriskmatrixaxesshowthatriskmatricesaresusceptibletohumanbiases.AnchoringFirstimpressiondominatesallfurtherthought1-100wheeloffortunespunNumberofAfricannationsintheUnitedNations?Smallnumber,like12,thesubjectsunderestimatedLargenumber,like92,thesubjectsoverestimatedObviatingexpertopinionTheanalystholdsacircularbeliefthatexpertopinionorreviewisnotnecessarybecausenoevidencefortheneedofexpertopinionispresent.HeuristicsandBiasesPresenceofcognitivebiases–eveninextensiveandvettedanalyses–canneverberuledout.Innatehumanbiases,andexteriorcircumstances,suchastheframingorcontextofaquestion,cancompromiseestimates,judgmentsanddecisions.Itisimportanttonotethatsubjectsoftenmaintainastrongsensethattheyareactingrationallywhileexhibitingbiases.TerminologySubjectiveParametersLikelihood(L)Consequence(C)SubjectiveProbability,π(p)Utility(negative),U-(v)Shownon:Ordinate,YaxisAbscissa,XaxisObjectiveParametersObjectiveProbability,pObjectiveValue,v5x5Risk“Cube”O(jiān)riginalObjectivevs.Subjectivedata"Campfireconversation"pieceLikelihoodFrequency

ofoccurrenceisobjective,discreteProbabilityiscontinuous,fiction"Humansjudgeprobabilitiespoorly"[CosmidesandTooby,2019]Likelihoodisasubjectivejudgment (unlessmathematical)'Exposure'byprojectmanagertimelessConsequence,CObjectiveConsequencedeterminationiscostlyRangeofconsequenceTotallife-cyclecostMil-Std882d$damageHumanimpactEnvironmentLawCatastrophic>$1MDeath,DisabilityirreversibledamageViolateCritical:$1M-$200KHospitalizationto>=3personnelReversibledamageViolateMarginal:$200K-$10KLossofworkdays;injuryMitigationdamageNegligible:$10K-$2KNolostworkday;injuryMinimaldamageCaseStudyIndustryriskmatrixdata1412originalandcurrentriskpoints(665)TimeoffirstentryknownTimeoflastupdateknownCost,ScheduleandTechnicalknownSubjectmatternotknownBiasesrevealedLikelihoodandconsequencejudgmentMagnitudevs.Reliability[GriffinandTversky,1992]MagnitudeperceivedmorevalidDatawithoutstandingmagnitudesbutpoorreliabilityarelikelytobechosenandusedSuggestion:DatawithuniformsourcereliabilitySpeciousnessofdataObservation:riskmatricesaremagnitudedriven,withoutregardtoreliabilityExpectedDistributionfororiginalriskpointsinRiskMatrix?BivariateNormalUniform:1.EstimationinaPre-DefineScaleBias

Responsescaleeffectsjudgment[Schwarz,1990]Twoquestions,random50%ofsubjects:Pleaseestimatetheaveragenumberofhoursyouwatchtelevisionperweek:__________X_____________1-45-89-1213-1617-20MorePleaseestimatetheaveragenumberofhoursyouwatchtelevisionperweek:__________X_____________1-2 3-45-67-89-10MoreLikelihoodMarginalDistributionofOriginalPoints123455827275428840Normaldistributionwithμ=3.0,σ=0.783833067633038?=actual–normal20-5878-422(Χ2=22,LogisticΧ2>~10rejectH0H0=NormalEffectofEstimationinaPre-DefinedScale

‘Peopleestimateprobabilitiespoorly’[CosmidesandTooby,2019]Consequence/SeverityamplifiersEffectofEstimationinaPre-DefinedScale

‘Peopleestimateprobabilitiespoorly’[CosmidesandTooby,2019]Consequence/SeverityamplifiersSeverityAmplifiersLackofcontrolLackofchoiceLackoftrustLackofwarningLackofunderstandingManmadeNewnessDreadfulnessPersonalizationRecallabilityImminency5x5RiskMatrixSituationassessment5x5RiskMatricesseektoincreaseriskestimationconsistencyHypothesis:CognitiveBiasinformationcanhelpimprovethevalidityandsensitivityofriskmatrixanalysisProspectTheoryDecision-makingdescribedwithsubjectiveassessmentof:ProbabilitiesValuesandcombinationsingamblesProspectTheorybreakssubjectivedecisionmakinginto:preliminary‘screening’stage,probabilitiesandvaluesaresubjectivelyassessedsecondary‘evaluation’stagecombinesthesubjectiveprobabilitiesandutilitiesHumansjudgeprobabilitiespoorly*SubjectiveProbability,π(p)

smallprobabilitiesoverestimatedlargeprobabilitiesunderestimatedπ(p)=

(pδ)/[pδ+(1-p)δ](1/δ) p=objectiveprob. 0<δ≤1Whenδ=1,π(p)=p=objectiveprobabilityusualvalueforδ:

δ=0.69forlosses

δ=0.61forgainsGainsandlossesarenotequal*SubjectiveUtility

Valuesconsideredfromreferencepointestablishedbythesubject’swealthandperspectiveFramingGainsandlossesare subjectivelyvalued1-to-2ratio.Forgains:U+(v)=Ln(1+v)Forlosses:U-(v)=-(μ)Ln(1–cv) μ=2.5

c=constant

v=objectivevalue

ImplicationofProspectTheoryfortheRiskMatrixANALYSESANDOBSERVATIONS

OFINITIALDATA

Impedimentsfortheappearanceofcognitivebiasesintheindustrydata:IndustrydataaregranularwhilethepredictionsofProspectTheoryareforcontinuousdataQualitativedescriptionsof5rangesoflikelihoodandconsequencenon-linearinfluenceintheplacementofriskdatumpoints Nevertheless,theevidenceofcognitivebiasesemergesfromthedata2.DiagonalBiasAnticipationoflatermovingofriskpointstowardtheoriginRiskpointswithdrawnfromtheoriginupwardandrightwardalongthediagonalRegressionon1412OriginalPointsInterceptSlopeR2.20.220.223.ProbabilityCenteringBias

LikelihoodsarepushedtowardL=3SymmetrictoafirstorderImplicationofProspectTheoryfortheRiskMatrix3a.AsymmetricalProbabilityBiasSubjectiveprobabilitytransformationπ(p)predictsthatlikelihooddatawillbepushedtowardL=3LargeprobabilitiestranslateddownmorethansmallprobabilitiesaretranslatedupReducedamountoflargesubjectiveprobabilities,comparatively1234558272754288404.ConsequenceBias

ConsequenceispushedhigherEngineeridentifieswithincreasedrisktoentirecorporation'Personal'corporateriskStatisticalEvidenceforConsequenceBias

MaxatC=4C=1significantlylessthanC=5counts C=2significantlylessthanC=4ConsequenceOriginalDataPoints1234520145538599110Normaldistributioncomparison:χ2=600,df=40.0probabilityConsequencesmoothedConsequenceincreased,→,byAmplifiersH0=NormalConsequencetranslationLikelihoodmitigationrecommendationsEngineersandManagementTechnicalriskhighestpriorityScheduleriskcommunicatedwellbymanagementCostrisklikelihoodlessfrequentlycommunicatedbymanagement.

HighercognizanceofcostriskwillbevaluableattheengineeringlevelLikelihoodmitigation1.Technical2.Schedule3.CostConsequenceMitigationEngineers:ScheduleconsequenceseffectcareersTechnicalconsequenceseffectjobperformancereviewsCostconsequencesareremoteandassociatedwithmanagementHighercognizanceofcostriskwillbevaluableattheengineeringlevelConsequencemitigation1.Schedule2.Technical3.CostCONCLUSIONFirsttimethattheeffectsofcognitivebiaseshavebeendocumentedwithintheriskmatrixClearevidencethatprobabilityandvaluetranslations,aslikelihoodandconsequencejudgments,arepresentinindustryriskmatrixdataSteps1)thetranslationswerepredictedbyprospecttheory,2)historicaldataconfirmedpredictionsRiskmatricesarenotobjectivenumbergridsSubjective,albeituseful,meanstoverifythatriskitemshavereceivedrisk-mitigatingattention.DataCollectionImprovementsContinuumofdatafromRiskmanagementto(Issuemanagement)OpportunitymanagementDifferentdatabasesyearsofdataineachTimeWaterfallRiskchartsSuggestionsforriskmanagementimprovementObjectivebasisofrisk:FrequencydataforProbability$forConsequenceLong-term,institutionalrationalityTeamapproachIterationsPublicreviewExpertreviewBiasesanderrorsawarenessFutureworkConfirmationofthepresenceofprobabilitybiases,andvaluebiasesinriskdatafromotherindustrie