Hostname: page-component-5cf477f64f-54txb Total loading time: 0 Render date: 2025-03-26T09:03:43.546Z Has data issue: false hasContentIssue false
  • English
  • Français

A Model of Behavior in Coordination Game Experiments

Published online by Cambridge University Press:  14 March 2025

Martin Sefton
Martin Sefton*
Affiliation:
Department of Economics, University of Newcastle, Newcastle-upon-Tyne, NEI 7RU, United Kingdom
*
email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper constructs a structural model for behavior in expeiments where subjects play a simple coordination game repeatedly under a rotating partner scheme. The model assumes subjects’ actions are stochastic best responses to beliefs about opponents’ choices, and these beliefs update as subjects observe actual choices during the experiment. The model accounts for heterogeneity across subjects by regarding prior beliefs as random effects and estimating their distribution. Maximum likelihood estimates from experimental data suggest that distributions of initial beliefs vary across games, but in all games studied imply a convergence dynamic toward risk-dominant equilibrium.

Keywords

coordination gamesfictitious playunobserved heterogeneity

JEL classification

C9: Design of Experiments C1: Econometric and Statistical Methods and Methodology: General
Type
Research Article
Information
Experimental Economics , Volume 2 , Issue 2 , December 1999 , pp. 151 - 164
Copyright
Copyright © 1999 Economic Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Battalio, R., Samuelson, L., and Van Huyck, J. (1997). “Risk Dominance, Payoff Dominance and Probabilistic Choice Learning.” Working Paper, Texas A&M University.Google Scholar
Boylan, R.T. and El-Gamal, M.A. (1993). “Fictitious Play: A Study of Multiple Economic Experiments.” Games and Economic Behavior. 5, 205–222.CrossRefGoogle Scholar
Camerer, C.F. and Ho, T.H. (1997). “Experience-Weighted Attraction Learning in Normal Form Games.” Social Science Working Paper 1003, California Institute of Technology.Google Scholar
Cheung, Y.-W. and Friedman, D. (1997). “Individual Learning in Normal Form Games: Some Laboratory Results.”. Games and Economic Behavior. 19, 46–76.CrossRefGoogle Scholar
Clark, K., Kay, S., and Sefton, M. (in press). “When are Nash Equilibria Self-Enforcing? An Experimental Analysis.” International Journal of Game Theory.Google Scholar
Clark, K. and Sefton, M. (1999). “Matching Protocols in Experimental Games.” Working Paper, University of Newcastle.Google Scholar
Cooper, R., DeJong, D.V., Forsythe, R., and Ross, T.W. (1992). “Communication in Coordination Games.” Quarterly Journal of Economics. 53, 739–771.CrossRefGoogle Scholar
DeGroot, M. (1986). Probability and Statistics. Reading, Mass.: Addison-Wesley.Google Scholar
Fudenberg, D. and Levine, D. (1998). The Theory of Learning in Games. MIT Press.Google Scholar
Harless, D. and Camerer, C.F. (1994). “The Predictive Utility of Generalized Expected Utility Theories.” Econometrica. 62, 1251–1289.CrossRefGoogle Scholar
Kamecke, U. (1997). “Rotations: Matching Schemes that Efficiently Preserve the Best Reply Structure of a One Shot Game.” International Journal of Game Theory. 26, 409.CrossRefGoogle Scholar
Offerman, T., Schram, A., and Sonnemans, J. (1998). “Quantal Response Models in Step-Level Public Good Games.” European Journal of Political Economy. 14, 89–100.CrossRefGoogle Scholar
Roth, A.E. and Erev, I. (1995). “Learning in Extensive-Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Term.” Games and Economic Behavior. 8, 164–212.CrossRefGoogle Scholar
Van Huyck, J.B., Battalio, R.C., and Rankin, F.W. (1997). “On the Origin of Convention: Evidence from Coordination Games.” Economic Journal. 107(442), 576–596.CrossRefGoogle Scholar