Bayesian Learning in Mean Field Games
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917579433443328 |
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| author | Shmaya, Eran Ziliotto, Bruno |
| author_facet | Shmaya, Eran Ziliotto, Bruno |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17696 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bayesian Learning in Mean Field Games Shmaya, Eran Ziliotto, Bruno Optimization and Control Analysis of PDEs 91A16, 91A27, 91A26 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. |
| title | Bayesian Learning in Mean Field Games |
| topic | Optimization and Control Analysis of PDEs 91A16, 91A27, 91A26 |
| url | https://arxiv.org/abs/2401.17696 |