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

Erik Mohlin, Robert Östling, Joseph Tao yi Wang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)225-245
Number of pages21
JournalGames and Economic Behavior
Publication statusPublished - 2020 Mar

Subject classification (UKÄ)

  • Media and Communication Technology

Free keywords

  • Beauty contest
  • Behavioral game theory
  • Evolutionary game theory
  • Imitation
  • Learning
  • Lowest unique positive integer game
  • Mixed equilibrium
  • Replicator dynamic
  • Similarity-based reasoning
  • Stochastic approximation


Dive into the research topics of 'Learning by similarity-weighted imitation in winner-takes-all games'. Together they form a unique fingerprint.

Cite this