Individual evolutionary learning in repeated beauty contest games

Mikhail Anufriev, John Duffy and Valentyn Panchenko

Journal of Economic Behavior & Organization, 2024, vol. 218, issue C, 550-567

Abstract: The Individual Evolutionary Learning (IEL) algorithm was proposed as a portable learning model for games with large strategy spaces. In principle, IEL benchmark simulations could substitute or supplement expensive experiments with human subjects. We evaluate the ability of the IEL model to replicate experimental findings observed in repeated Keynesian Beauty Contest (KBC) games, which have a large strategy space. The IEL specification with standard parameter values is able to capture major dynamical features and differences between treatments in both one-dimensional (Nagel, 1995; Duffy and Nagel, 1997) and two-dimensional (Anufriev et al., 2022b) versions of KBC games. We compare IEL with some other simple learning models and find that it performs relatively better across multiple treatments. We also use IEL to predict behavior in repeated KBC games that have not yet been conducted experimentally.

Keywords: Beauty contest game; Learning; Evolutionary dynamics; Testbed; Agent-based model (search for similar items in EconPapers)
JEL-codes: C63 C72 C73 D83 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jeborg:v:218:y:2024:i:c:p:550-567

DOI: 10.1016/j.jebo.2023.12.010

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