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Economic Value of EWA Lite: A Functional Theory of Learning in Games

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  • Ho, Teck-Hua
  • Camerer, Colin F.
  • Chong, Juin-Kuan

Abstract

EWA Lite is a one-parameter theory of learning in normal-form games. It approximates the free parameters in an earlier model (EWA) with functions of experience. The theory is tested on seven different games and compared to other learning and equilibrium theories. Either EWA Lite or parameterized EWA predict best, but one kind of reinforcement learning predicts well in games with mixed-strategy equilibrium. Belief learning models fit worst. The economic value of theories is measured by howmuch more subjects would have earned if they followed theory recommendations. EWA Lite and EWA add the most economic value in every game but one.

Suggested Citation

  • Ho, Teck-Hua & Camerer, Colin F. & Chong, Juin-Kuan, 2001. "Economic Value of EWA Lite: A Functional Theory of Learning in Games," Working Papers 1122, California Institute of Technology, Division of the Humanities and Social Sciences.
  • Handle: RePEc:clt:sswopa:1122
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    File URL: http://www.hss.caltech.edu/SSPapers/wp1122.pdf
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    Cited by:

    1. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics,in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011 Elsevier.
    2. Francesco Feri & Bernd Irlenbusch & Matthias Sutter, 2010. "Efficiency Gains from Team-Based Coordination—Large-Scale Experimental Evidence," American Economic Review, American Economic Association, vol. 100(4), pages 1892-1912, September.
    3. Yan Chen & Robert Gazzale, 2004. "When Does Learning in Games Generate Convergence to Nash Equilibria? The Role of Supermodularity in an Experimental Setting," American Economic Review, American Economic Association, vol. 94(5), pages 1505-1535, December.
    4. Teck H Ho & Colin Camerer & Juin-Kuan Chong, 2003. "Functional EWA: A one-parameter theory of learning in games," Levine's Working Paper Archive 506439000000000514, David K. Levine.

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