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Are People Bayesian? Uncovering Behavioral Strategies

El-Gamal, Mahmoud A. and Grether, David M. (1995) Are People Bayesian? Uncovering Behavioral Strategies. Journal of the American Statistical Association, 90 (432). pp. 1137-1145. ISSN 0162-1459.

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Economists and psychologists have recently been developing new theories of decision making under uncertainty that can accommodate the observed violations of standard statistical decision theoretic axioms by experimental subjects. We propose a procedure that finds a collection of decision rules that best explain the behavior of experimental subjects. The procedure is a combination of maximum likelihood estimation of the rules together with an implicit classification of subjects to the various rules and a penalty for having too many rules. We apply our procedure to data on probabilistic updating by subjects in four different universities. We get remarkably robust results showing that the most important rules used by the subjects (in order of importance) are Bayes's rule, a representativeness rule (ignoring the prior), and, to a lesser extent, conservatism (overweighting the prior).

Item Type:Article
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ContributionOther Contributors NameIdentifierPersonID (may be blank)
EditorLittle, R. J. A.Little-R-J-AUNSPECIFIED
Additional Information:© 1995 American Statistical Association. Received 01 Apr 1994. Financial support was provided by National Science Foundation Grant SBR-9320497 to the California Institute of Technology. The authors thank the previous editor (R. J. A. Little) and an anonymous associate editor, as well an an anonymous referee for valuable comments and suggestions. They also thank participants at the ESA meetings and the Classification Society of North America meetings, and at the econometric workshops at Arizona, Caltech, Minnesota, Northwestern, Wisconsin, Rochester, SMU, and Texas A&M for many useful comments. Formerly SSWP 919.
Funding AgencyGrant Number
Subject Keywords:Classification, Learning, Mixture models, Probability assessments
Series Name:Applications & Case Studies
Issue or Number:432
Record Number:CaltechAUTHORS:20171107-135529984
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:83038
Deposited By: Jacquelyn Bussone
Deposited On:07 Nov 2017 23:25
Last Modified:03 Oct 2019 19:01

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