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Experience-Weighted Attraction Learning in Games: A Unifying Approach


Author Info

  • Camerer, Colin
  • Ho, Teck-Hua


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

Related research

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


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Cited by:
  1. Gary Charness & Dan Levin, 2005. "When Optimal Choices Feel Wrong: A Laboratory Study of Bayesian Updating, Complexity, and Affect," American Economic Review, American Economic Association, vol. 95(4), pages 1300-1309, September.
  2. 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.
  3. Bigoni, Maria & Fort, Margherita, 2013. "Information and learning in oligopoly: An experiment," Games and Economic Behavior, Elsevier, vol. 81(C), pages 192-214.
  4. 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.
  5. Rosemarie Nagel & Antonio Cabrales & Roc Armenter, 2002. "Equilibrium selection through incomplete information in coordination games: An experimental study," Economics Working Papers 601, Department of Economics and Business, Universitat Pompeu Fabra.
  6. 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.
  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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