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Learning Strategies

Author

Listed:
  • Nobuyuki Hanaki

    (Columbia University)

  • Rajiv Sethi

    (Barnard College, Columbia University)

  • Ido Erev

    (Technion)

  • Alexander Peterhansl

    (Columbia University)

Abstract

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.

Suggested Citation

  • Nobuyuki Hanaki & Rajiv Sethi & Ido Erev & Alexander Peterhansl, 2002. "Learning Strategies," Game Theory and Information 0211004, EconWPA.
  • Handle: RePEc:wpa:wuwpga:0211004 Note: Type of Document - pdf; prepared on windows; to print on hp;
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    File URL: http://econwpa.repec.org/eps/game/papers/0211/0211004.pdf
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Eric Guerci & Nobuyuki Hanaki & Naoki Watanabe, 2015. "Meaningful Learning in Weighted Voting Games: An Experiment," Working Papers halshs-01216244, HAL.
    2. Bednar, Jenna & Chen, Yan & Liu, Tracy Xiao & Page, Scott, 2012. "Behavioral spillovers and cognitive load in multiple games: An experimental study," Games and Economic Behavior, Elsevier, vol. 74(1), pages 12-31.
    3. Eric Guerci & Nobuyuki Hanaki & Naoki Watanabe, 2017. "Meaningful learning in weighted voting games: an experiment," Theory and Decision, Springer, vol. 83(1), pages 131-153, June.
    4. Josephson, Jens, 2008. "A numerical analysis of the evolutionary stability of learning rules," Journal of Economic Dynamics and Control, Elsevier, vol. 32(5), pages 1569-1599, May.
    5. Takashi Yamada & Nobuyuki Hanaki, 2016. "An Experiment on Lowest Unique Integer Games," Post-Print halshs-01204814, HAL.
    6. Nobuyuki Hanaki, 2007. "Individual and Social Learning," Computational Economics, Springer;Society for Computational Economics, vol. 29(3), pages 421-421, May.
    7. Arifovic, Jasmina & Ledyard, John, 2011. "A behavioral model for mechanism design: Individual evolutionary learning," Journal of Economic Behavior & Organization, Elsevier, vol. 78(3), pages 374-395, May.
    8. 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.
    9. Ioannou, Christos A. & Romero, Julian, 2014. "A generalized approach to belief learning in repeated games," Games and Economic Behavior, Elsevier, vol. 87(C), pages 178-203.
    10. Yamada, Takashi & Hanaki, Nobuyuki, 2016. "An experiment on Lowest Unique Integer Games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 88-102.
    11. Golosnoy, Vasyl & Okhrin, Yarema, 2008. "General uncertainty in portfolio selection: A case-based decision approach," Journal of Economic Behavior & Organization, Elsevier, vol. 67(3-4), pages 718-734, September.
    12. Christos A. Ioannou & Julian Romero, 2012. "Strategic Learning With Finite Automata Via The EWA-Lite Model," Purdue University Economics Working Papers 1269, Purdue University, Department of Economics.

    More about this item

    Keywords

    reinforcement learning; repeated game strategies;

    JEL classification:

    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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