Learning in monotone Bayesian games

Working paper

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.

Authors: Beggs, A

• Publication status: Published
• Publisher: University of Oxford
• Series: Department of Economics Discussion Paper Series
• Publication date: 2015-01-08
• Paper number: 737
• Keywords: supermodular games; bayesian games; learning; stochastic approximation; monotone strategies