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Cited by 367

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Are People Bayesian? Uncovering Behavioral Strategies

El-Gamal, Mahmoud A., Grether, David M.·1995·Journal of the American Statistical Association

Abstract

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).

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Metadata

Authors
El-Gamal, M. A., & Grether, D. M.
Venue
Journal of the American Statistical Association
Published
1995-12
Volume
90
Issue
432
Pages
1137-1145
ISSN
  • 0162-1459 (ISSN-L)
  • 1537-274X
OpenAlex
W1998054917
Cited by
367
References
6
FWCI
2.57
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Are People Bayesian? Uncovering Behavioral Strategies. https://app.cochat.ai/papers/10.1080/01621459.1995.10476620
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El-Gamal, M. A., & Grether, D. M. (1995). Are People Bayesian? Uncovering Behavioral Strategies. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.1995.10476620

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.