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A generalized approach to belief learning in repeated games

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  • Ioannou, Christos A.
  • Romero, Julian
Abstract
We propose a methodology that is generalizable to a broad class of repeated games in order to facilitate operability of belief-learning models with repeated-game strategies. The methodology consists of (1) a generalized repeated-game strategy space, (2) a mapping between histories and repeated-game beliefs, and (3) asynchronous updating of repeated-game strategies. We implement the proposed methodology by building on three proven action-learning models. Their predictions with repeated-game strategies are then validated with data from experiments with human subjects in four, symmetric 2×2 games: Prisoner's Dilemma, Battle of the Sexes, Stag-Hunt, and Chicken. The models with repeated-game strategies approximate subjects' behavior substantially better than their respective models with action learning. Additionally, inferred rules of behavior in the experimental data overlap with the predicted rules of behavior.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:gamebe:v:87:y:2014:i:c:p:178-203
    DOI: 10.1016/j.geb.2014.05.007
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    3. Zhao, Shuchen, 2021. "Taking turns in continuous time," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 257-279.
    4. Leonidas Spiliopoulos, 2018. "Randomization and serial dependence in professional tennis matches: Do strategic considerations, player rankings and match characteristics matter?," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 13(5), pages 413-427, September.
    5. 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.
    6. J{o}rgen Vitting Andersen & Philippe de Peretti, 2020. "Heuristics in experiments with infinitely large strategy spaces," Papers 2005.02337, arXiv.org.
    7. Isabelle Brocas & Juan D. Carrillo, 2022. "The development of randomization and deceptive behavior in mixed strategy games," Quantitative Economics, Econometric Society, vol. 13(2), pages 825-862, May.
    8. Chernov, G. & Susin, I., 2019. "Models of learning in games: An overview," Journal of the New Economic Association, New Economic Association, vol. 44(4), pages 77-125.
    9. Jasmina Arifovic & John Ledyard, 2018. "Learning to alternate," Experimental Economics, Springer;Economic Science Association, vol. 21(3), pages 692-721, September.
    10. Jørgen Vitting Andersen & Philippe de Peretti, 2018. "New method to detect convergence in simple multi-period market games with infinite large strategy spaces," Documents de travail du Centre d'Economie de la Sorbonne 18038, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    11. Marco Lambrecht & Eugenio Proto & Aldo Rustichini & Andis Sofianos, 2024. "Intelligence Disclosure and Cooperation in Repeated Interactions," American Economic Journal: Microeconomics, American Economic Association, vol. 16(3), pages 199-231, August.
    12. Andersen, Jørgen Vitting & de Peretti, Philippe, 2021. "Heuristics in experiments with infinitely large strategy spaces," Journal of Business Research, Elsevier, vol. 129(C), pages 612-620.
    13. Jørgen Vitting Andersen & Philippe de Peretti, 2020. "Heuristics in experiments with infinitely large strategy spaces," Post-Print hal-02435934, HAL.
    14. 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.
    15. repec:cup:judgdm:v:13:y:2018:i:5:p:413-427 is not listed on IDEAS
    16. Jørgen Vitting Andersen & Philippe de Peretti, 2018. "New method to detect convergence in simple multi-period market games with infinite large strategy spaces," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01960900, HAL.
    17. Alejandro Lee-Penagos, 2016. "Learning to Coordinate: Co-Evolution and Correlated Equilibrium," Discussion Papers 2016-11, The Centre for Decision Research and Experimental Economics, School of Economics, University of Nottingham.
    18. Wang, Yang & Chen, Peng & Wu, Bing & Wan, Chengpeng & Yang, Zaili, 2022. "A trustable architecture over blockchain to facilitate maritime administration for MASS systems," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    19. Zhang, Huanren, 2018. "Errors can increase cooperation in finite populations," Games and Economic Behavior, Elsevier, vol. 107(C), pages 203-219.
    20. Jørgen Vitting Andersen & Philippe de Peretti, 2020. "Heuristics in experiments with infinitely large strategy spaces," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-02435934, HAL.

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    More about this item

    Keywords

    Adaptive models; Belief learning; Repeated-game strategies; Finite automata; Prisoner's Dilemma; Battle of the Sexes; Stag-Hunt; Chicken;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles

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