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A Bayesian Method for Characterizing Population Heterogeneity

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  • Dale O. Stahl

    (Department of Economics, University of Texas at Austin, Austin, TX 78712, USA)

Abstract

A stylized fact from laboratory experiments is that there is much heterogeneity in human behavior. We present and demonstrate a computationally practical non-parametric Bayesian method for characterizing this heterogeneity. In addition, we define the concept of behaviorally distinguishable parameter vectors, and use the Bayesian posterior to say what proportion of the population lies in meaningful regions. These methods are then demonstrated using laboratory data on lottery choices and the rank-dependent expected utility model. In contrast to other analyses, we find that 79% of the subject population is not behaviorally distinguishable from the ordinary expected utility model.

Suggested Citation

  • Dale O. Stahl, 2019. "A Bayesian Method for Characterizing Population Heterogeneity," Games, MDPI, vol. 10(4), pages 1-12, October.
    • Handle: RePEc:gam:jgames:v:10:y:2019:i:4:p:40-:d:274517
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    References listed on IDEAS

    as
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    6. Wilcox, Nathaniel T., 2011. "'Stochastically more risk averse:' A contextual theory of stochastic discrete choice under risk," Journal of Econometrics, Elsevier, vol. 162(1), pages 89-104, May.
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    9. John D. Hey & Chris Orme, 2018. "Investigating Generalizations Of Expected Utility Theory Using Experimental Data," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 3, pages 63-98, World Scientific Publishing Co. Pte. Ltd..
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