The logit-response dynamics

Carlos Alós-Ferrer, Nick Netzer

Research output: Contribution to journalArticlepeer-review


We develop a characterization of stochastically stable states for the logit-response learning dynamics in games, with arbitrary specification of revision opportunities. The result allows us to show convergence to the set of Nash equilibria in the class of best-response potential games and the failure of the dynamics to select potential maximizers beyond the class of exact potential games. We also study to which extent equilibrium selection is robust to the specification of revision opportunities. Our techniques can be extended and applied to a wide class of learning dynamics in games.

Original languageEnglish (US)
Pages (from-to)413-427
Number of pages15
JournalGames and Economic Behavior
Issue number2
StatePublished - Mar 2010


  • Best-response potential games
  • Learning in games
  • Logit-response dynamics

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics


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