Individual evolutionary learning in repeated beauty contest games

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.