Maximum a posteriori learning in demand competition games
Computer Science and Game Theory
2016-12-01 v1 Optimization and Control
Abstract
We consider an inventory competition game between two firms. The question we address is this: If players do not know the opponent's action and opponent's utility function can they learn to play the Nash policy in a repeated game by observing their own sales? In this work it is proven that by means of Maximum A Posteriori (MAP) estimation, players can learn the Nash policy. It is proven that players' actions and beliefs do converge to the Nash equilibrium.
Keywords
Cite
@article{arxiv.1611.10270,
title = {Maximum a posteriori learning in demand competition games},
author = {Mohsen Rakhshan},
journal= {arXiv preprint arXiv:1611.10270},
year = {2016}
}
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6 pages