This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in Web of Science Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Valluri, A. Search for Related Content Social Bookmarking                         What's this?

Learning and Cooperation in Sequential Games

Department of Operations and Information Management, The Wharton School of the University of Pennsylvania

The predictions of classical game theory for one-shot and finitely repeated play of many 2x2 simulta neous games do not correspond to human behavior observed in laboratory experiments. The promis ing results of learning models in tracking human behavior coupled with the growing electronic market and the number of e-commerce applications has resulted in an increased interest in studying the behavior of adaptive artificial agents in different economic games. We model agents with a reinforce ment learning algorithm and analyze cooperative behavior in a sequential prisoner's dilemma game. Our results demonstrate the ability of artificial agents to learn cooperative behavior even in sequential games where defection is the subgame perfect Nash equilibrium. We attribute the reciprocal-like behavior to the structural flow of information, which reduces the risks of exploitation faced by the second-mover. Additionally, we analyze the impact of the second-mover's temptation payoff and pay off risks on the rate of cooperative behavior.

Key Words: reinforcement learning • prisoner's dilemma • sequential games

Adaptive Behavior, Vol. 14, No. 3, 195-209 (2006)
DOI: 10.1177/105971230601400304


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?