Two Competing Models of How People Learn in Games

Research output: Working paperDiscussion paper

Abstract

Reinforcement learning and stochastic fictitious play are apparent rivals as models of human learning. They embody quite different assumptions about the processing of information and optimization. This paper compares their properties and finds that they are far more similar than were thought. In particular, the expected motion of stochastic fictitious play and reinforcement learning with experimentation can both be written as a perturbed form of the evolutionary replicator dynamics. Therefore they will in many cases have the same asymptotic behavior. In particular, local stability of mixed equilibria under stochastic fictitious play implies local stability under perturbed reinforcement learning. The main identifiable difference between the two models is speed: stochastic fictitious play gives rise to faster learning. Copyright The Econometric Society 2002.

(This abstract was borrowed from another version of this item.)

Original languageEnglish
Publisherwww.najecon.org
Publication statusPublished - 21 Sep 2001

Publication series

NameNajEcon Working Paper Reviews

Fingerprint

Dive into the research topics of 'Two Competing Models of How People Learn in Games'. Together they form a unique fingerprint.

Cite this