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Unobserved Heterogeneity and Equilibrium: An Experimental Study of Bayesian and Adaptive Learning in Normal Form Games

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Jason Shachat
Mark Walker

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Abstract

We describe an experiment based on a repeated two-person game of incomplete information designed so that Jordan's Bayesian model of learning in games and the best response model make completely opposite predictions. Econometric analysis of the experimental data, using the maximum likelihood procedure introduced by El Gamal and Grether, reveals clear heterogeneity in the subjects' learning behavior. The heterogeneity is not diffuse, however: the subjects follow only a few decision rules for basing their play on their information, and the decision rules have simple cognitive interpretations. We show that the heterogeneity can be explained as an equilibrium of the repeated game. Although the repeated game has many equilibria, including a unique pure strategy equilibrium, we find that the only equilibrium consistent with the data is one of the mixed strategy equilibria. This equilibrium is shown, surprisingly, to be consistent with Jordan's Bayesian model, in a "representative player" sense, each subject using a pure strategy, but the distribution of strategies among subjects coinciding with the mixed strategy equilibrium.

* University of Arizona

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Paper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number 97-33.

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Date of creation: Dec 1997
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Handle: RePEc:cdl:ucsdec:97-33

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  1. Milgrom, Paul & Roberts, John, 1991. "Adaptive and sophisticated learning in normal form games," Games and Economic Behavior, Elsevier, vol. 3(1), pages 82-100, February. [Downloadable!] (restricted)
  2. Barry Sopher & Dilip Mookherjee, 1997. "Learning and Decision Costs in Experimental Constant Sum Games," Departmental Working Papers 199527, Rutgers University, Department of Economics.
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  1. Miguel Costa-Gomes & Vincent Crawford & Bruno Broseta, 1998. "Cognition and Behavior in Normal-Form Games: An Experimental Study," University of California at San Diego, Economics Working Paper Series 1998-22, Department of Economics, UC San Diego. [Downloadable!]
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  2. Daniel Houser & Michael Keane & Kevin McCabe, 2002. "Behavior in a dynamic decision problem: An analysis of experimental evidence using a bayesian type classification algorithm," Experimental 0211001, EconWPA. [Downloadable!]
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