Learning in Monotone Bayesian Games

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.

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Year of publication:	2015-01-08
Authors:	Beggs, Alan
Institutions:	Department of Economics, Oxford University
Subject:	bayesian games \| monotone strategies \| learning \| stochastic approximation \| supermodular games

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Extent:	application/pdf
Series:	Economics Series Working Papers.
Type of publication:	Book / Working Paper
Notes:	Number 737
Classification:	C72 - Noncooperative Games ; D83 - Search, Learning, Information and Knowledge
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10011115589