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Functional EWA: A One-parameter Theory of Learning in Games

Camerer, Colin F. and Ho, Teck H. and Chong, Juin-Kuan (2002) Functional EWA: A One-parameter Theory of Learning in Games. California Institute of Technology , Pasadena, CA. http://resolver.caltech.edu/CaltechAUTHORS:20111021-135120019

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Abstract

Functional experience weighted attraction (fEWA) is a one-parameter theory of learning in games. It approximates the free parameters in an earlier model (EWA) with functions of experience. The theory was originally tested on seven different games and compared to four other learning and equilibrium theories, then three more games were added. Generally fEWA or parameterized EWA predict best out-of-sample, but one kind of reinforcement learning predicts well in games with mixed-strategy equilibrium. Of the learning models, belief learning models fit worst but fit better than noisy (quantal response) equilibrium models. The economic value of a theory is measured by how much more subjects would have earned if they followed the theory's recommendations. Most learning theories add value (though equilibrium theories often subtract value) and fEWA and EWA usually add the most value.


Item Type:Report or Paper (Report)
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, Guillaume Frechette and two referees for comments.
Record Number:CaltechAUTHORS:20111021-135120019
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20111021-135120019
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:27355
Collection:CaltechAUTHORS
Deposited By: Shirley Johnson
Deposited On:21 Oct 2011 20:59
Last Modified:26 Dec 2012 14:18

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