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Experience-Weighted Attraction Learning in Games: A Unifying Approach

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Author Info

  • Camerer, Colin
  • Ho, Teck-Hua

Abstract

We describe a general model, 'experience-weighted attraction' (EWA) learning, which includes reinforcement learning and a class of weighted fictitious play belief models as special cases. In EWA, strategies have attractions which reflect prior predispositions, are updated based on payoff experience, and determine choice probabilities according to some rule (e.g., logit). A key feature is a parameter δ which weights the strength of hypothetical reinforcement of strategies which were not chosen according to the payoff they would have yielded. When δ = 0 choice reinforcement results. When δ = 1, levels of reinforcement of strategies are proportional to expected payoffs given beliefs based on past history. Another key feature is the growth rates of attractions. The EWA model controls the growth rates by two decay parameters, φ and ρ, which depreciate attractions and amount of experience separately. When φ = ρ belief-based models result; when ρ = 0 choice reinforcement results. Using three data sets, parameter estimates of the model were calibrated on part of the data and used to predict the rest. Estimates of δ are generally around .50, φ around 1, and ρ varies from 0 to φ. Choice reinforcement models often outperform belief-based models in the calibration phase and underperform in out-of-sample validation. Both special cases are generally rejected in favor of EWA, though sometimes belief models do better. EWA is able to combine the best features of both approaches, allowing attractions to begin and grow exibly as choice reinforcement does, but reinforcing unchosen strategies substantially as belief-based models implicitly do.

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File URL: http://www.hss.caltech.edu/SSPapers/wp1003.pdf
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Bibliographic Info

Paper provided by California Institute of Technology, Division of the Humanities and Social Sciences in its series Working Papers with number 1003.

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Length: 42 pages
Date of creation: Mar 1997
Date of revision:
Handle: RePEc:clt:sswopa:1003

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Postal: Working Paper Assistant, Division of the Humanities and Social Sciences, 228-77, Caltech, Pasadena CA 91125
Phone: 626 395-4065
Fax: 626 405-9841
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Web page: http://www.hss.caltech.edu/ss

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Postal: Working Paper Assistant, Division of the Humanities and Social Sciences, 228-77, Caltech, Pasadena CA 91125
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Related research

Keywords: Learning; behavioral game theory; reinforcement learning; fictitious play;

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Citations

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Cited by:
  1. M. Bigoni & M. Fort, 2013. "Information and Learning in Oligopoly: an Experiment," Working Papers wp860, Dipartimento Scienze Economiche, Universita' di Bologna.
  2. Charness, Gary & Levin, Dan, 2003. "When Optimal Choices Feel Wrong: A Laboratory Study of Bayesian Updating, Complexity, and Affect," University of California at Santa Barbara, Economics Working Paper Series qt7g63k28w, Department of Economics, UC Santa Barbara.
  3. A. Shorrocks & T. Hens & H. Gottinger & S. Reichelstein & B. Kuon & M. Frenkel & R. Braun & R. Noll & Y. Xu, 1997. "Book Reviews," Journal of Economics, Springer, vol. 66(3), pages 308-328, October.
    • T. Hutchison & I. Pellengahr & K. Podczeck & R. Noll & I. Vogelsang & B. Mitchell & S. Martin & J. Mairesse, 1994. "Book review," Journal of Economics, Springer, vol. 59(3), pages 325-349, October.
  4. Richard, Jean-François, 2000. "Conférence François-Albert Angers (1999). Enchères : théorie économique et réalité," L'Actualité Economique, Société Canadienne de Science Economique, vol. 76(2), pages 173-198, juin.
  5. Antonio Cabrales & Rosemarie Nagel & Roc Armenter, 2007. "Equilibrium selection through incomplete information in coordination games: an experimental study," Experimental Economics, Springer, vol. 10(3), pages 221-234, September.
  6. Blume, A. & DeJong, D.V. & Neumann, G. & Savin, N.E., 2000. "Learning and Communication in Sender-Reciever Games: An Economic Investigation," Discussion Paper 2000-09, Tilburg University, Center for Economic Research.
  7. Brit Grosskopf, 2003. "Reinforcement and Directional Learning in the Ultimatum Game with Responder Competition," Experimental Economics, Springer, vol. 6(2), pages 141-158, October.

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