Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jet.2005.12.008
Title: Self-tuning experience weighted attraction learning in games
Authors: Ho, T.H.
Camerer, C.F.
Chong, J.-K. 
Keywords: Experience weighted attraction
Fictitious play
Learning
Quantal response equilibrium
Reinforcement learning
Issue Date: 2007
Source: Ho, T.H., Camerer, C.F., Chong, J.-K. (2007). Self-tuning experience weighted attraction learning in games. Journal of Economic Theory 133 (1) : 177-198. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jet.2005.12.008
Abstract: Self-tuning experience weighted attraction (EWA) is a one-parameter theory of learning in games. It addresses a criticism that an earlier model (EWA) has too many parameters, by fixing some parameters at plausible values and replacing others with functions of experience so that they no longer need to be estimated. Consequently, it is econometrically simpler than the popular weighted fictitious play and reinforcement learning models. The functions of experience which replace free parameters "self-tune" over time, adjusting in a way that selects a sensible learning rule to capture subjects' choice dynamics. For instance, the self-tuning EWA model can turn from a weighted fictitious play into an averaging reinforcement learning as subjects equilibrate and learn to ignore inferior foregone payoffs. The theory was tested on seven different games, and compared to the earlier parametric EWA model and a one-parameter stochastic equilibrium theory (QRE). Self-tuning EWA does as well as EWA in predicting behavior in new games, even though it has fewer parameters, and fits reliably better than the QRE equilibrium benchmark. © 2006 Elsevier Inc. All rights reserved.
Source Title: Journal of Economic Theory
URI: http://scholarbank.nus.edu.sg/handle/10635/43829
ISSN: 00220531
DOI: 10.1016/j.jet.2005.12.008
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