Experience and rationality under risk: re-examining the impact of sampling experience
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Experience and rationality under risk: re‑examining the impact of sampling experience Ilke Aydogan1,2 · Yu Gao3 Received: 7 March 2018 / Revised: 8 December 2019 / Accepted: 20 December 2019 © The Author(s) 2020
Abstract A recent strand of the literature on decision-making under uncertainty has pointed to an intriguing behavioral gap between decisions made from description and decisions made from experience. This study reinvestigates this description-experience gap to understand the impact that sampling experience has on decisions under risk. Our study adopts a complete sampling paradigm to address the lack of control over experienced probabilities by requiring complete sampling without replacement. We also address the roles of utilities and ambiguity, which are central in most current decision models in economics. Thus, our experiment identifies the deviations from expected utility due to over- (or under-) weighting of probabilities. Our results confirm the existence of the behavioral gap, but they provide no evidence for the underweighting of small probabilities within the complete sampling treatment. We find that sampling experience attenuates rather than reverses the inverse S-shaped probability weighting under risk. Keywords Decisions from experience · Decisions under risk · Probability weighting · Rare outcomes JEL Classifications D81 · D83 · C91
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s1068 3-019-09641-y) contains supplementary material, which is available to authorized users. * Yu Gao [email protected] Ilke Aydogan [email protected] 1
IESEG School of Management, Lille, France
2
Department of Economics, Bocconi University, Milan, Italy
3
Department of Applied Economics, Guanghua School of Management (GSM), Peking University, Beijing 100871, China
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I. Aydogan, Y. Gao
The traditional paradigm of decisions from description (DFD), which uses explicit descriptions of probability distributions over outcomes, has served for decades as a useful tool for studying decision-making under risk in the laboratory. This paradigm has led to important empirical findings on systematic deviations from expected utility theory (EU) (von Neumann and Morgenstern 1944; Allais 1953; Tversky and Kahneman 1981) and has given rise to significant theoretical developments, including prospect theory (PT) (Kahneman and Tversky 1979; Tversky and Kahneman 1992). Among these developments, non-linearity of decision weights in probabilities has been acknowledged as one of the most important deviations from EU. The famous inverse S-shaped probability weighting, which captures the tendency to overweight rare and extreme outcomes in prospects, is the most commonly documented pattern in numerous laboratory studies. It also provides a useful framework for understanding and predicting field behavior in financial, insurance, and betting markets that cannot be explained by EU (Fehr-Duda and Epper 2012). The predominant view on inverse S-shaped probabil
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