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Pré-Publication, Document De Travail Année : 2024

Bayesian Learning in Mean Field Games

Résumé

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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Dates et versions

hal-04568449 , version 1 (04-05-2024)

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Eran Shmaya, Bruno Ziliotto. Bayesian Learning in Mean Field Games. 2024. ⟨hal-04568449⟩
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