 Stochastic adaptive learning with committed players in games with strict Nash equilibriaNaoki Funai Games and Economic Behavior, 2025, vol. 154, issue C, 351-376 Abstract: We investigate the convergence properties of an adaptive learning model that overlaps those of stochastic fictitious play learning and experience-weighted attraction learning in normal form games with strict Nash equilibria. In particular, we consider the case in which adaptive players play a game against not only other adaptive players but also committed players, who do not revise their behaviour but follow a fixed (strict Nash equilibrium or corresponding logit quantal response equilibrium) action. We then provide conditions under which the adaptive learning process, the choice probability profile of adaptive players, almost surely converges to the logit quantal response equilibrium that committed players follow. We also provide conditions under which the adaptive learning process of a more general adaptive learning model which overlaps those of payoff assessment learning and delta learning converges to a logit quantal response equilibrium different from the equilibrium that committed players follow with positive probability. Keywords: Adaptive learning; Stochastic fictitious play learning; Experience-weighted attraction learning; Quantal response equilibrium; Stochastic approximation; Equilibrium selection (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:gamebe:v:154:y:2025:i:c:p:351-376 DOI: 10.1016/j.geb.2025.09.011 Access Statistics for this article Games and Economic Behavior is currently edited by E. Kalai More articles in Games and Economic Behavior from Elsevier |
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