Decisions from Experience and the Effect of Rare Events in Risky Choice

When people have access to information sources such as newspaper weather forecasts, drug-package inserts, and mutual-fund brochures, all of which provide convenient descriptions of risky prospects, they can make decisions from description. When people must decide whether to back up their computer's hard drive, cross a busy street, or go out on a date, however, they typically do not have any summary description of the possible outcomes or their likelihoods. For such decisions, people can call only on their own encounters with such prospects, making decisions from experience. Decisions from experience and decisions from description can lead to dramatically different choice behavior. In the case of decisions from description, people make choices as if they overweight the probability of rare events, as described by prospect theory. We found that in the case of decisions from experience, in contrast, people make choices as if they underweight the probability of rare events, and we explored the impact of two possible causes of this underweighting—reliance on relatively small samples of information and overweighting of recently sampled information. We conclude with a call for two different theories of risky choice.

[1]  E. Rowland Theory of Games and Economic Behavior , 1946, Nature.

[2]  J. Koehler The base rate fallacy reconsidered: Descriptive, normative, and methodological challenges , 1996, Behavioral and Brain Sciences.

[3]  R. Hertwig,et al.  Experimental practices in economics: A methodological challenge for psychologists? , 2001, Behavioral and Brain Sciences.

[4]  Colin Camerer Prospect Theory In The Wild: Evidence From The Field , 1998 .

[5]  A. Tversky,et al.  Prospect theory: analysis of decision under risk , 1979 .

[6]  Erev,et al.  Organizational Behavior and Human Decision Processes Accidents and Decision Making under Uncertainty: a Comparison of Four Models , 2022 .

[7]  J. Neumann,et al.  Theory of games and economic behavior , 1945, 100 Years of Math Milestones.

[8]  I. Erev,et al.  Signal detection by human observers: a cutoff reinforcement learning model of categorization decisions under uncertainty. , 1998, Psychological review.

[9]  Yun-Peng Chu,et al.  The Subsidence of Preference Reversals in Simplified and Marketlike Experimental Settings: A Note , 1990 .

[10]  M. Allais Le comportement de l'homme rationnel devant le risque : critique des postulats et axiomes de l'ecole americaine , 1953 .

[11]  E. Weber,et al.  Predicting Risk-Sensitivity in Humans and Lower Animals: Risk as Variance or Coefficient of Variation , 2004, Psychological review.

[12]  A. Tversky,et al.  Advances in prospect theory: Cumulative representation of uncertainty , 1992 .

[13]  R. Hertwig,et al.  The role of information sampling in risky choice , 2006 .

[14]  Y. Kareev,et al.  On the misperception of variability. , 2002, Journal of experimental psychology. General.

[15]  R. Hogarth,et al.  Order effects in belief updating: The belief-adjustment model , 1992, Cognitive Psychology.

[16]  K. Fiedler Beware of samples! A cognitive-ecological sampling approach to judgment biases. , 2000, Psychological review.

[17]  I. Erev,et al.  Small feedback‐based decisions and their limited correspondence to description‐based decisions , 2003 .

[18]  Y. Kareev Seven (indeed, plus or minus two) and the detection of correlations. , 2000, Psychological review.

[19]  E. Weber,et al.  Determinants of diagnostic hypothesis generation: effects of information, base rates, and experience. , 1993, Journal of experimental psychology. Learning, memory, and cognition.

[20]  L A Real,et al.  Animal choice behavior and the evolution of cognitive architecture , 1991, Science.