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A learning-based model of repeated games with incomplete information

Chong, Juin-Kuan and Camerer, Colin F. and Ho, Teck H. (2006) A learning-based model of repeated games with incomplete information. Games and Economic Behavior, 55 (2). pp. 340-371. ISSN 0899-8256. doi:10.1016/j.geb.2005.03.009.

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This paper tests a learning-based model of strategic teaching in repeated games with incomplete information. The repeated game has a long-run player whose type is unknown to a group of short-run players. The proposed model assumes a fraction of ‘short-run’ players follow a one-parameter learning model (self-tuning EWA). In addition, some ‘long-run’ players are myopic while others are sophisticated and rationally anticipate how short-run players adjust their actions over time and “teach” the short-run players to maximize their long-run payoffs. All players optimize noisily. The proposed model nests an agent-based quantal-response equilibrium (AQRE) and the standard equilibrium models as special cases. Using data from 28 experimental sessions of trust and entry repeated games, including 8 previously unpublished sessions, the model fits substantially better than chance and much better than standard equilibrium models. Estimates show that most of the long-run players are sophisticated, and short-run players become more sophisticated with experience.

Item Type:Article
Related URLs:
URLURL TypeDescription DOIArticle
Chong, Juin-Kuan0000-0002-5187-8652
Camerer, Colin F.0000-0003-4049-1871
Ho, Teck H.0000-0001-5210-4977
Additional Information:© 2005 Elsevier Inc. Received 17 October 2002. Available online 18 July 2005. This research was supported by NSF Grant SES-0078911. Thanks to John Kagel for rapidly supplying data and to Pierpaolo Battigalli, Drew Fudenberg, Qi-Zheng Ho, David Hsia, XinWang, and four referees, for discussions and help. Useful comments were received from seminar participants at Berkeley, Caltech, Chicago, Harvard, Hong Kong UST, New York University, Pittsburgh, Princeton, and Wharton. We would also like to thank the editors and two anonymous reviewers for their helpful comments and suggestions.
Funding AgencyGrant Number
Subject Keywords:repeated games; self-tuning experience-weighted attraction learning; quantal response equilibrium
Issue or Number:2
Classification Code:JEL classification codes: C72; C92; D83
Record Number:CaltechAUTHORS:20110204-081727561
Persistent URL:
Official Citation:Juin-Kuan Chong, Colin F. Camerer, Teck H. Ho, A learning-based model of repeated games with incomplete information, Games and Economic Behavior, Volume 55, Issue 2, Mini Special Issue: Electronic Market Design, May 2006, Pages 340-371, ISSN 0899-8256, DOI: 10.1016/j.geb.2005.03.009.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:22002
Deposited By: Tony Diaz
Deposited On:08 Feb 2011 04:02
Last Modified:09 Nov 2021 16:02

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