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Predicting how People Play Games: a Simple Dynamic Model of Choice

  • Sarin, R.
  • Vahid, F.

We 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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File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/1999/wp12-99.pdf
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Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 12/99.

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Length: 19 pages
Date of creation: Oct 1999
Date of revision:
Publication status: Published in Games and Economic Behavior (2001), 34, 104-122.
Handle: RePEc:msh:ebswps:1999-12
Contact details of provider: Postal: PO Box 11E, Monash University, Victoria 3800, Australia
Phone: +61-3-9905-2489
Fax: +61-3-9905-5474
Web page: http://www.buseco.monash.edu.au/depts/ebs/
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References listed on IDEAS
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  1. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
  2. Itzhak Gilboa & David Schmeidler, 1992. "Case-Based Decision Theory," Discussion Papers 994, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
  3. Tilman B�rgers & Rajiv Sarin, . "Naive Reinforcement Learning With Endogenous Aspiration," ELSE working papers 037, ESRC Centre on Economics Learning and Social Evolution.
  4. T. Borgers & R. Sarin, 2010. "Learning Through Reinforcement and Replicator Dynamics," Levine's Working Paper Archive 380, David K. Levine.
  5. Sarin, Rajiv & Vahid, Farshid, 1999. "Payoff Assessments without Probabilities: A Simple Dynamic Model of Choice," Games and Economic Behavior, Elsevier, vol. 28(2), pages 294-309, August.
  6. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-81, September.
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