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arXiv · 1611.10270

Maximum a posteriori learning in demand competition games

Mohsen Rakhshan
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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.

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Mohsen Rakhshan. 2016-11-28. Maximum a posteriori learning in demand competition games. https://arxiv.org/abs/1611.10270

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