The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Individual Learning in Games

  • Teck H. Ho
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_2442

Abstract

This article reviews individual models of learning in games. We show that the experience-weighted attraction (EWA) learning nests different forms of reinforcement and belief learning, and that belief learning is mathematically equivalent to generalized reinforcement, where even unchosen strategies are reinforced. Many studies consisting of thousands of observations suggest that the EWA model predicts behaviour out-of-sample better than its special cases. We also describe a generalization of EWA learning to investigate anticipation by some players that others are learning. This generalized framework links equilibrium and learning models, and improves predictive performance when players are experienced and sophisticated.

Keywords

Belief learning Curse of knowledge Equilibrium Experience-weighted attraction (EWA) learning Extensive-form games Fictitious play Forgone payoffs Individual learning in games Individual models of learning Maximum likelihood Mixed-strategy equilibrium Noise Overconfidence Population models of learning Quantal response equilibrium Reinforcement learning Signalling Social calibration Sophisticated players 

JEL Classifications

C9 
This is a preview of subscription content, log in to check access.

Bibliography

  1. Anderson, C., and C. Camerer. 2000. Experience-weighted attraction learning in sender-receiver signaling games. Economic Theory 16: 689–718.Google Scholar
  2. Brown, G. 1951. Iterative solution of games by fictitious play. In Activity analysis of production and allocation. New York: Wiley.Google Scholar
  3. Camerer, C.F., and T.-H. Ho. 1999. Experience-weighted attraction learning in normal-form games. Econometrica 67: 827–874.CrossRefGoogle Scholar
  4. Camerer, C., T.-H. Ho, and J.-K. Chong. 2002. Sophisticated learning and strategic teaching. Journal of Economic Theory 104: 137–118.CrossRefGoogle Scholar
  5. Camerer, C.F., T.-H. Ho, and J.-K. Chong. 2004. A cognitive hierarchy model of one-shot games. Quarterly Journal of Economics 119: 861–898.CrossRefGoogle Scholar
  6. Chong, J.-K., C. Camerer, and T.-H. Ho. 2006. A learning-based model of repeated games with incomplete information. Games and Economic Behavior 55: 340–371.CrossRefGoogle Scholar
  7. Cooper, D., and Kagel, J. 2004. Learning and transfer in signaling games. Working paper. Ohio State University.Google Scholar
  8. Cournot, A. 1960. Recherches sur les principes mathématiques de la théorie des richesses. Trans. N. Bacon as researches in the mathematical principles of the theory of wealth. Haffner: London.Google Scholar
  9. Erev, I., and A. Roth. 1998. Modelling predicting how people play games: Reinforcement learning in experimental games with unique, mixed-strategy equilibria. American Economic Review 88: 848–881.Google Scholar
  10. Friedman, D. 1991. Evolutionary games in economics. Econometrica 59: 637–666.CrossRefGoogle Scholar
  11. Fudenberg, D., and D. Levine. 1998. The theory of learning in games. Cambridge, MA: MIT Press.Google Scholar
  12. Harley, C. 1981. Learning the evolutionary stable strategies. Journal of Theoretical Biology 89: 611–633.CrossRefGoogle Scholar
  13. Ho, T.-H., C. Camerer, and K. Weigelt. 1998. Iterated dominance and iterated best-response in p-beauty contests. American Economic Review 88: 947–969.Google Scholar
  14. Ho, T.-H., C. Camerer, and J.-K. Chong. 2007. Self-tuning experience-weighted attraction learning in games. Journal of Economic Theory 133: 177–198.CrossRefGoogle Scholar
  15. Hopkins, E. 2002. Two competing models of how people learn in games. Econometrica 70: 2141–2166.CrossRefGoogle Scholar
  16. McKelvey, R., and T. Palfrey. 1995. Quantal response equilibria for normal form games. Games and Economic Behavior 10: 6–38.CrossRefGoogle Scholar
  17. Mookerjhee, D., and B. Sopher. 1994. Learning behavior in an experimental matching pennies game. Games and Economic Behavior 7: 62–91.CrossRefGoogle Scholar
  18. Mookerjee, D., and B. Sopher. 1997. Learning and decision costs in experimental constant-sum games. Games and Economic Behavior 19: 97–132.CrossRefGoogle Scholar
  19. Roth, A.E., and I. Erev. 1995. Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term. Games and Economic Behavior 8: 164–212.CrossRefGoogle Scholar
  20. Selten, R., and R. Stoecker. 1986. End behavior in sequences of finite Prisoner’s Dilemma supergames: A learning theory approach. Journal of Economic Behavior and Organization 7: 47–70.CrossRefGoogle Scholar
  21. Stahl, D. 2000. Rule learning in symmetric normal-form games: Theory and evidence. Games and Economic Behavior 32: 105–138.CrossRefGoogle Scholar
  22. Weibull, J. 1995. Evolutionary game theory. Cambridge, MA: MIT Press.Google Scholar

Copyright information

© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Teck H. Ho
    • 1
  1. 1.