Predicting How People Play Games: A Simple Dynamic Model of Choice
AbstractWe use the model developed in Sarin and Vahid (1999, GEB) to explain the experiments reported in Erev and Roth (1998, AER). The model supposes that players maximize subject to their "beliefs" which are non-probabilistic and scalar-valued. They are intended to describe the payoffs the players subjectively assess they will obtain from a strategy. In an earlier paper (Sarin and Vahid (1997) we showed that the model predicted behaviour in repeated coordination games remarkably well, and better than equilibrium theory of reinforcement learning models. In this paper we show that the same one-parameter model can also explain behaviour in games with a unique mixed strategy Nash equilibrium better than alternative models. Hence, we obtain further support for the simple dynamic model.
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Bibliographic InfoArticle provided by Elsevier in its journal Games and Economic Behavior.
Volume (Year): 34 (2001)
Issue (Month): 1 (January)
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Web page: http://www.elsevier.com/locate/inca/622836
Other versions of this item:
- Sarin, R. & Vahid, F., 1999. "Predicting how People Play Games: a Simple Dynamic Model of Choice," Monash Econometrics and Business Statistics Working Papers 12/99, Monash University, Department of Econometrics and Business Statistics.
- C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
- C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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- Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
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