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BIMSA Digital Economy Lab Seminar
Risk-averse mean field games: exploitability and non-asymptotic analysis
Risk-averse mean field games: exploitability and non-asymptotic analysis
Speaker
Zhiteng Cheng
Time
Tuesday, January 20, 2026 3:00 PM - 4:00 PM
Venue
A3-2-303
Online
Zoom 435 529 7909
(BIMSA)
Abstract
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 nonasymptotic 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 ε-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.
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 nonasymptotic 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 ε-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.
Speaker Intro
成子腾，香港科技大学（广州）金融科技学院助理教授。在加入香港科技大学（广州）之前，他曾在多伦多大学统计系做博士后(合作导师Sebastian Jaimungal教授)。成博士在伊利诺伊理工学院应用数学系获得博士学位，师从Tomasz R. Bielecki教授和龚若汀教授，研究领域涵盖风险规避决策、强化学习、逆强化学习、平均场博弈等方向。