Risk-averse mean field games: exploitability and non-asymptotic analysis
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866916409996476416 |
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| author | Cheng, Ziteng Jaimungal, Sebastian |
| author_facet | Cheng, Ziteng Jaimungal, Sebastian |
| contents | In this paper, we use mean field games (MFGs) to investigate approximations of $N$-player games with uniformly symmetrically continuous heterogeneous closed-loop actions. To incorporate agents' risk aversion (beyond the classical expected utility of total costs), we use an abstract evaluation functional for their performance criteria. Centered around the notion of exploitability, we conduct non-asymptotic analysis on the approximation capability of MFGs from the perspective of state-action distributions without requiring the uniqueness of equilibria. Under suitable assumptions, we first show that scenarios in the $N$-player games with large $N$ and small average exploitabilities can be well approximated by approximate solutions of MFGs with relatively small exploitabilities. We then show that $δ$-mean field equilibria can be used to construct $\varepsilon$-equilibria in $N$-player games. Furthermore, in this general setting, we prove the existence of mean field equilibria. This proof reveals a possible avenue for incorporating penalization for randomized action into MFGs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_06930 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Risk-averse mean field games: exploitability and non-asymptotic analysis Cheng, Ziteng Jaimungal, Sebastian Optimization and Control Probability In this paper, we use mean field games (MFGs) to investigate approximations of $N$-player games with uniformly symmetrically continuous heterogeneous closed-loop actions. To incorporate agents' risk aversion (beyond the classical expected utility of total costs), we use an abstract evaluation functional for their performance criteria. Centered around the notion of exploitability, we conduct non-asymptotic analysis on the approximation capability of MFGs from the perspective of state-action distributions without requiring the uniqueness of equilibria. Under suitable assumptions, we first show that scenarios in the $N$-player games with large $N$ and small average exploitabilities can be well approximated by approximate solutions of MFGs with relatively small exploitabilities. We then show that $δ$-mean field equilibria can be used to construct $\varepsilon$-equilibria in $N$-player games. Furthermore, in this general setting, we prove the existence of mean field equilibria. This proof reveals a possible avenue for incorporating penalization for randomized action into MFGs. |
| title | Risk-averse mean field games: exploitability and non-asymptotic analysis |
| topic | Optimization and Control Probability |
| url | https://arxiv.org/abs/2301.06930 |