Learning in Monotone Bayesian Games

Jan 2015 | 737

Authors: Alan Beggs


This paper studies learning in monotone Bayesian games with one-dimensional types and finitely many actions. Players switch between actions at a set of thresholds.  A learning algorithm under which players adjust their strategies in the direction of better ones using payoffs received at similar signals to their current thresholds is examined.  Convergence to equilibrium is shown in the case of supermodular games and potential games.

JEL Codes: C72, D83

Keywords: bayesian games, monotone strategies, learning, stochastic approximation, supermodular games


View All Working Papers