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Learning by similarity-weighted imitation in winner-takes-all games

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  • Mohlin, Erik
  • Östling, Robert
  • Wang, Joseph Tao-yi

Abstract

We study a simple model of similarity-based global cumulative imitation in symmetric games with large and ordered strategy sets and a salient winning player. We show that the learning model explains behavior well in both field and laboratory data from one such “winner-takes-all” game: the lowest unique positive integer game in which the player that chose the lowest number not chosen by anyone else wins a fixed prize. We corroborate this finding in three other winner-takes-all games and discuss under what conditions the model may be applicable beyond this class of games. Theoretically, we show that global cumulative imitation without similarity weighting results in a version of the replicator dynamic in winner-takes-all games.

Suggested Citation

  • Mohlin, Erik & Östling, Robert & Wang, Joseph Tao-yi, 2020. "Learning by similarity-weighted imitation in winner-takes-all games," Games and Economic Behavior, Elsevier, vol. 120(C), pages 225-245.
    • Handle: RePEc:eee:gamebe:v:120:y:2020:i:c:p:225-245
    DOI: 10.1016/j.geb.2019.12.008
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    More about this item

    Keywords

    Learning; Imitation; Behavioral game theory; Evolutionary game theory; Stochastic approximation; Replicator dynamic; Similarity-based reasoning; Beauty contest; Lowest unique positive integer game; Mixed equilibrium;
    All these keywords.

    JEL classification:

    • 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
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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