Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/219263 
Authors: 
Year of Publication: 
2019
Citation: 
[Journal:] Games [ISSN:] 2073-4336 [Volume:] 10 [Issue:] 4 [Publisher:] MDPI [Place:] Basel [Year:] 2019 [Pages:] 1-12
Publisher: 
MDPI, Basel
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.
Subjects: 
Bayesian methods
behavioral distinguishability
identifying types
population heterogeneity
rank-dependent expected utility
JEL: 
C11
C12
D81
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
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