Bayesian Learning in Mean Field Games

Eran Shmaya; Bruno Ziliotto

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arXiv · 2401.17696

Bayesian Learning in Mean Field Games

Eran Shmaya·Bruno Ziliotto

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Abstract

We consider a mean-field game model where the cost functions depend on a fixed parameter, called \textit{state}, which is unknown to players. Players learn about the state from a a stream of private signals they receive throughout the game. We derive a mean field system satisfied by the equilibrium payoff of the game and prove existence of a solution under standard regularity assumptions. Additionally, we establish the uniqueness of the solution when the cost function satisfies the monotonicity assumption of Lasry and Lions at each state.

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Eran Shmaya, Bruno Ziliotto. 2024-01-31. Bayesian Learning in Mean Field Games. https://arxiv.org/abs/2401.17696

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