A BEJTE Advances article.

Abstract

This paper considers a simple adaptive learning rule in Bayesian games with binary actions where players employ threshold strategies. Global convergence results are given for supermodular games and potential games. If there is a unique equilibrium, players' strategies converge almost surely to it. Even if there is not, in potential games and in the two-player case in supermodular games, any limit point of the learning process must be an equilibrium. In particular, if equilibria are isolated, the learning process converges to one of them almost surely.

Submitted: January 28, 2008 · Accepted: February 12, 2009 · Published: September 30, 2009

Recommended Citation

Beggs, Alan (2009) "Learning in Bayesian Games with Binary Actions," The B.E. Journal of Theoretical Economics: Vol. 9 : Iss. 1 (Advances), Article 33.
DOI: 10.2202/1935-1704.1452
Available at: http://www.bepress.com/bejte/vol9/iss1/art33