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Data for CPC2015 : a choice prediction competition for decisions under risk, under ambiguity, and from experience
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Erev, Ido, Ert, Eyal and Plonsky, Ori (2017) Data for CPC2015 : a choice prediction competition for decisions under risk, under ambiguity, and from experience. [Dataset]
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Official URL: https://wrap.warwick.ac.uk/134782
Abstract
Experimental studies of choice behavior document distinct, and sometimes contradictory, deviations from maximization. For example, people tend to overweight rare events in 1-shot decisions under risk, and to exhibit the opposite bias when they rely on past experience. The common explanations of these results assume that the contradicting anomalies reflect situation-specific processes that involve the weighting of subjective values and the use of simple heuristics. The current article analyzes 14 choice anomalies that have been described by different models, including the Allais, St. Petersburg, and Ellsberg paradoxes, and the reflection effect. Next, it uses a choice prediction competition methodology to clarify the interaction between the different anomalies. It focuses on decisions under risk (known payoff distributions) and under ambiguity (unknown probabilities), with and without feedback concerning the outcomes of past choices. The results demonstrate that it is not necessary to assume situation-specific processes. The distinct anomalies can be captured by assuming high sensitivity to the expected return and 4 additional tendencies: pessimism, bias toward equal weighting, sensitivity to payoff sign, and an effort to minimize the probability of immediate regret. Importantly, feedback increases sensitivity to probability of regret. Simple abstractions of these assumptions, variants of the model Best Estimate and Sampling Tools (BEAST), allow surprisingly accurate ex ante predictions of behavior. Unlike the popular models, BEAST does not assume subjective weighting functions or cognitive shortcuts. Rather, it assumes the use of sampling tools and reliance on small samples, in addition to the estimation of the expected values.
Item Type: | Dataset | |||||||||
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Alternative Title: | Data for From anomalies to forecasts : toward a descriptive model of decisions under risk, under ambiguity, and from experience | |||||||||
Subjects: | H Social Sciences > HB Economic Theory | |||||||||
Divisions: | Faculty of Social Sciences > Warwick Business School > Behavioural Science | |||||||||
Type of Data: | Experimental data | |||||||||
Library of Congress Subject Headings (LCSH): | Risk -- Mathematical models, Uncertainty -- Mathematical models, Decision making -- Mathematical models | |||||||||
Publisher: | University of Warwick, Warwick Business School | |||||||||
Official Date: | 22 February 2017 | |||||||||
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Status: | Not Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Media of Output (format): | .csv | |||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||
Copyright Holders: | University of Warwick | |||||||||
Description: | Data record consists of 3 data files in .csv format. Data files are named according to the corresponding experiment in the related paper. |
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