Publication | Open Access
Decisions from experience and <i>statistical probabilities</i>: Why they trigger different choices than a priori probabilities
135
Citations
45
References
2009
Year
Bayesian Decision TheoryBehavioral Decision MakingDecision AnalysisCognitionIndividual Decision MakingPsychologySocial SciencesCognitive BiasesExperimental Decision MakingBiasRisk-taking BehaviorPriori ProbabilitiesManagementProbabilistic ReasoningBayesian ModelingSingle‐event ProbabilitiesCognitive Bias MitigationMemory LimitationsProbabilistic ModelingDecision TheoryStatisticsCognitive ScienceBehavioral SciencesHigh UncertaintyProbability TheoryStatistical ProbabilitiesExperimental PsychologyBehavioral EconomicsDifferent ChoicesImprecise ProbabilityDecision ScienceRisk DecisionsFinancial Risk
Abstract The distinction between risk and uncertainty is deeply entrenched in psychologists' and economists' thinking. Knight ( 1921 ), to whom it is frequently attributed, however, went beyond this dichotomy. Within the domain of risk, he set apart a priori and statistical probabilities, a distinction that maps onto that between decisions from description and experience, respectively. We argue this distinction is important because risky choices based on a priori (described) and statistical (experienced) probabilities can substantially diverge. To understand why, we examine various possible contributing factors to the description–experience gap. We find that payoff variability and memory limitations play only a small role in the emergence of the gap. In contrast, the presence of rare events and their representation as either natural frequencies in decisions from experience or single‐event probabilities in decisions from description appear relevant for the gap. Copyright © 2009 John Wiley & Sons, Ltd.
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