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The Economics of Learning Models: A Self-tuning Theory of Learning in Games

Ho, Teck H. and Camerer, Colin F. and Chong, Juin-Kuan (2004) The Economics of Learning Models: A Self-tuning Theory of Learning in Games. California Institute of Technology , Pasadena, CA. (Unpublished)

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Self-tuning experience weighted attraction (EWA) is a one-parameter theory of learning in games. It replaces the key parameters in an earlier model (EWA) with functions of experience that “self-tune” over time. The theory was tested on seven different games, and compared to the earlier model and a one-parameter stochastic equilibrium theory. The more parsimonious self-tuning EWA does as well as EWA in predicting behavior in new games, and reliably better than an equilibrium benchmark. The economic value of a learning theory is measured by how much more subjects would have earned in an experimental session if they followed the theory’s recommendations. Economic values for several learning and equilibrium theories were estimated (controlled for boomerang effects of following a model’s advice in one period, on future earnings). Most models have economic value. Self-tuning EWA adds the most value.

Item Type:Report or Paper (Report)
Ho, Teck H.0000-0001-5210-4977
Camerer, Colin F.0000-0003-4049-1871
Chong, Juin-Kuan0000-0002-5187-8652
Additional Information:Thanks to participants in the 2000 Southern Economics Association meetings, the Wharton School Decision Processes Workshop, the University of Pittsburgh, the Berkeley Marketing Workshop, the Nobel Symposium on Behavioral and Experimental Economics (December 2001) and C. Mónica Capra, David Cooper, Vince Crawford, Ido Erev, and Guillaume Frechette for helpful comments.
Record Number:CaltechAUTHORS:20160303-102321736
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:65002
Deposited By: Katherine Johnson
Deposited On:03 Mar 2016 18:24
Last Modified:03 Oct 2019 09:43

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