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Beyond fictitious play beliefs: Incorporating pattern recognition and similarity matching

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  • Spiliopoulos, Leonidas
Abstract
Belief models capable of detecting 2- to 5-period patterns in repeated games by matching the current historical context to similar realizations of past play are presented. The models are implemented in a cognitive framework, ACT-R, and vary in how they implement similarity-based categorization—using either an exemplar or a prototype approach. Empirical estimation is performed on the elicited-belief data from two experiments (Nyarko and Schotter, 2002; Rutström and Wilcox, 2009) using repeated games with a unique, albeit significantly different, stage mixed-strategy Nash equilibrium. Model comparisons are performed by cross-validation both within and between these two datasets, and using data from completely unrelated non-strategic tasks. Subjectsʼ beliefs are best described by 2-period pattern detection. Parameter estimates exhibited considerable instability across the two belief-elicitation datasets, and surprisingly, using median values from a wide variety of unrelated studies led to better predictions.

Suggested Citation

  • Spiliopoulos, Leonidas, 2013. "Beyond fictitious play beliefs: Incorporating pattern recognition and similarity matching," Games and Economic Behavior, Elsevier, vol. 81(C), pages 69-85.
  • Handle: RePEc:eee:gamebe:v:81:y:2013:i:c:p:69-85
    DOI: 10.1016/j.geb.2013.04.005
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Andreas Ortmann & Leonidas Spiliopoulos, 2017. "The beauty of simplicity? (Simple) heuristics and the opportunities yet to be realized," Chapters, in: Morris Altman (ed.), Handbook of Behavioural Economics and Smart Decision-Making, chapter 7, pages 119-136, Edward Elgar Publishing.
    2. 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.
    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. Hanaki, Nobuyuki & Kirman, Alan & Pezanis-Christou, Paul, 2018. "Observational and reinforcement pattern-learning: An exploratory study," European Economic Review, Elsevier, vol. 104(C), pages 1-21.
    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.
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    More about this item

    Keywords

    Learning; Pattern recognition; Beliefs; Repeated games; Memory; Cognitive models; Behavioral game theory; ACT-R;
    All these keywords.

    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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