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A Bayesian Method for Characterizing Population Heterogeneity

Dale O. Stahl ()
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Dale O. Stahl: Department of Economics, University of Texas at Austin, Austin, TX 78712, USA

Games, 2019, vol. 10, issue 4, 1-12

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.

Keywords: Bayesian methods; population heterogeneity; identifying types; behavioral distinguishability; rank-dependent expected utility (search for similar items in EconPapers)
JEL-codes: C C7 C70 C71 C72 C73 (search for similar items in EconPapers)
Date: 2019
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