# American Institute of Mathematical Sciences

April  2015, 2(2): 117-140. doi: 10.3934/jdg.2015.2.117

## Learning in monotone bayesian games

 1 Wadham College, University of Oxford, Oxford, OX1 3PN, United Kingdom

Received  December 2014 Revised  October 2015 Published  December 2015

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.
Citation: Alan Beggs. Learning in monotone bayesian games. Journal of Dynamics & Games, 2015, 2 (2) : 117-140. doi: 10.3934/jdg.2015.2.117
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