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Decisions from experience and statistical probabilities : Why they trigger different choices than a priori probabilities
Author(s) -
Hau Robin,
Pleskac Timothy J.,
Hertwig Ralph
Publication year - 2010
Publication title -
journal of behavioral decision making
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 76
eISSN - 1099-0771
pISSN - 0894-3257
DOI - 10.1002/bdm.665
Subject(s) - a priori and a posteriori , set (abstract data type) , representation (politics) , rare events , event (particle physics) , stochastic game , econometrics , psychology , mathematical economics , cognitive psychology , computer science , epistemology , statistics , economics , mathematics , philosophy , political science , physics , quantum mechanics , politics , law , programming language
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.