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Learning Strategies

  • Nobuyuki Hanaki

    (Columbia University)

  • Rajiv Sethi

    (Barnard College, Columbia University)

  • Ido Erev

    (Technion)

  • Alexander Peterhansl

    (Columbia University)

Adaptive learning models that have been tested against experimental data typically share two features: (i) initial attractions (or beliefs) are given exogenously, and (ii) learning is based on the performance of stage-game actions rather than repeated game strategies. We develop a model of learning which endogenizes initial attractions and allows for the learning of repeated game strategies. Learning occurs in two phases. In an initial long-run `pre-experimental' phase, we allow players to explore a complete set of repeated game strategies that satisfy a complexity constraint. The limiting attractions from the first phase are then used as initial attractions in the second, short-run phase, which can be tested against experimental data. We find that, relative to existing adaptive models, we are better able to account for the behavior of subjects in environments where fairness and reciprocity appear to play a significant role.

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Paper provided by EconWPA in its series Game Theory and Information with number 0211004.

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Date of creation: 09 Nov 2002
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Handle: RePEc:wpa:wuwpga:0211004
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  1. Binmore, Kenneth G. & Samuelson, Larry, 1992. "Evolutionary stability in repeated games played by finite automata," Journal of Economic Theory, Elsevier, vol. 57(2), pages 278-305, August.
  2. Drew Fudenberg & David K. Levine, 1998. "Learning in Games," Levine's Working Paper Archive 2222, David K. Levine.
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  8. Barry Sopher & Dilip Mookherjee, 2000. "Learning and Decision Costs in Experimental Constant Sum Games," Departmental Working Papers 199625, Rutgers University, Department of Economics.
  9. Miller, John H., 1996. "The coevolution of automata in the repeated Prisoner's Dilemma," Journal of Economic Behavior & Organization, Elsevier, vol. 29(1), pages 87-112, January.
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  12. Arifovic, Jasmina & McKelvey, Richard D. & Pevnitskaya, Svetlana, 2006. "An initial implementation of the Turing tournament to learning in repeated two-person games," Games and Economic Behavior, Elsevier, vol. 57(1), pages 93-122, October.
  13. Cheung, Yin-Wong & Friedman, Daniel, 1997. "Individual Learning in Normal Form Games: Some Laboratory Results," Games and Economic Behavior, Elsevier, vol. 19(1), pages 46-76, April.
  14. Charness, Gary & Rabin, Matthew, 2002. "Understanding Social Preferences with Simple Tests," Department of Economics, Working Paper Series qt3d04q5sm, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
  15. David K Levine, 1997. "Modeling Altruism and Spitefulness in Experiments," Levine's Working Paper Archive 2047, David K. Levine.
  16. Dale O. Stahl, 1997. "Rule Learning in Symmetric Normal-Form Games: Theory and Evidence," CARE Working Papers 9710, The University of Texas at Austin, Center for Applied Research in Economics.
  17. Fudenberg, Drew & Maskin, Eric, 1990. "Evolution and Cooperation in Noisy Repeated Games," American Economic Review, American Economic Association, vol. 80(2), pages 274-79, May.
  18. Herbert Gintis, 2000. "Strong Reciprocity and Human Sociality," UMASS Amherst Economics Working Papers 2000-02, University of Massachusetts Amherst, Department of Economics.
  19. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, June.
  20. Axel Ockenfels & Gary E. Bolton, 2000. "ERC: A Theory of Equity, Reciprocity, and Competition," American Economic Review, American Economic Association, vol. 90(1), pages 166-193, March.
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  22. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-81, September.
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