title: Recency, consistent learning, and Nash equilibrium.
abstract: We examine the long-term implication of two models of learning with recency bias: recursive weights and limited memory. We show that both models generate similar beliefs and that both have a weighted universal consistency property. Using the limited-memory model we produce learning procedures that both are weighted universally consistent and converge with probability one to strict Nash equilibrium.
authors.affiliation: Department of Economics, Harvard University, Cambridge, MA 02138;
authors.forename: Drew
authors.initials: D
authors.lastname: Fudenberg
authors.affiliation: Department of Economics, European University Institute, 50014 Florence, Italy; andDepartment of Economics, Washington University in St. Louis, St. Louis, MO 63130 david@dklevine.com.
authors.forename: David K
authors.initials: DK
authors.lastname: Levine
mesh_terms: D000465:Algorithms; D005716:Game Theory; D006801:Humans; D007858:Learning / Q000502:physiology*; D008568:Memory / Q000502:physiology*; D008570:Memory, Short-Term / Q000502:physiology; D008960:Models, Psychological; D012153:Retention, Psychology / Q000502:physiology