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Pattern recognition and subjective belief learning in a repeated constant-sum game

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  • Spiliopoulos, Leonidas

Abstract

This paper aspires to fill a conspicuous gap in the literature regarding learning in games—the absence of empirical verification of learning rules involving pattern recognition. Weighted fictitious play is extended to detect two-period patterns in opponentsʼ behavior and to comply with the cognitive laws of subjective perception. An analysis of the data from Nyarko and Schotter (2002) uncovers significant evidence of pattern recognition in elicited beliefs and action choices. The probability that subjects employ pattern recognition depends positively on a measure of the exploitable two-period patterns in an opponentʼs action choices, in stark contrast to the minimax hypothesis. A significant proportion of the subjectsʼ competence in pattern recognition is the result of a subconscious/automatic cognitive mechanism, implying that elicited beliefs may not adequately represent the complete learning process of game players. Additionally, standard weighted fictitious play models are found to bias memory parameter estimates upwards due to mis-specification.

Suggested Citation

  • Spiliopoulos, Leonidas, 2012. "Pattern recognition and subjective belief learning in a repeated constant-sum game," Games and Economic Behavior, Elsevier, vol. 75(2), pages 921-935.
  • Handle: RePEc:eee:gamebe:v:75:y:2012:i:2:p:921-935
    DOI: 10.1016/j.geb.2012.01.005
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    Cited by:

    1. repec:elg:eechap:15532_7 is not listed on IDEAS
    2. Eric Guerci & Nobuyuki Hanaki & Naoki Watanabe, 2015. "Meaningful Learning in Weighted Voting Games: An Experiment," Working Papers halshs-01216244, HAL.
    3. Nobuyuki Hanaki & Alan Kirman & Paul Pezanis-Christou, 2018. "Observational and reinforcement pattern-learning: An exploratory study ," Post-Print halshs-01723513, HAL.
    4. Emara, Noha & Owens, David & Smith, John & Wilmer, Lisa, 2017. "Serial correlation in National Football League play calling and its effects on outcomes," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 69(C), pages 125-132.
    5. 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.
    6. 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.
    7. Nobuyuki Hanaki & Alan Kirman & Paul Pezanis-Christou, 2016. "Counter Intuitive Learning: An Exploratory Study," School of Economics Working Papers 2016-12, University of Adelaide, School of Economics.
    8. Spiliopoulos, Leonidas, 2013. "Beyond fictitious play beliefs: Incorporating pattern recognition and similarity matching," Games and Economic Behavior, Elsevier, vol. 81(C), pages 69-85.
    9. Duffy, Sean & Naddeo, JJ & Owens, David & Smith, John, 2016. "Cognitive load and mixed strategies: On brains and minimax," MPRA Paper 71878, University Library of Munich, Germany.
    10. Emara, Noha & Owens, David & Smith, John & Wilmer, Lisa, 2014. "Minimax on the gridiron: Serial correlation and its effects on outcomes in the National Football League," MPRA Paper 58907, University Library of Munich, Germany.
    11. Arifovic, J. & Hommes, C.H. & Salle, I., 2016. "Learning to believe in Simple Equilibria in a Complex OLG Economy - evidence from the lab," CeNDEF Working Papers 16-06, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    12. Nobuyuki Hanaki & Alan Kirman & Paul Pezanis-Christou, 2016. "Observational and Reinforcement Pattern-learning: An Exploratory Study," GREDEG Working Papers 2016-24, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), University of Nice Sophia Antipolis, revised Jun 2017.
    13. Andreas Ortmann & Leonidas Spiliopoulos, 2017. "The beauty of simplicity? (Simple) heuristics and the opportunities yet to be realized," Chapters,in: Handbook of Behavioural Economics and Smart Decision-Making, chapter 7, pages 119-136 Edward Elgar Publishing.
    14. repec:eee:eecrev:v:104:y:2018:i:c:p:1-21 is not listed on IDEAS

    More about this item

    Keywords

    Behavioral game theory; Learning; Fictitious play beliefs; Pattern detection; Repeated constant-sum games;

    JEL classification:

    • C70 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - General
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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