Learning in Monotone Bayesian Games

Alan Beggs

No 737, Economics Series Working Papers from University of Oxford, Department of Economics

Abstract: This paper studies learning in monotoneBayesian 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.

Keywords: bayesian games; monotone strategies; learning; stochastic approximation; supermodular games (search for similar items in EconPapers)
JEL-codes: C72 D83 (search for similar items in EconPapers)
Date: 2015-01-08
New Economics Papers: this item is included in nep-gth, nep-hpe and nep-mic
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