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<p>This paper considers risk-sensitive linear-quadratic mean-field games. By the so-called direct approach via dynamic programming, the authors determine the feedback Nash equilibrium in an <i>N</i>-player game. Subsequently, the authors design a set of decentralized strategies by passing to the mean-field limit. The authors prove that the set of decentralized strategies constitutes an <i>O</i>(1/<i>N</i>)-Nash equilibrium when applied by the <i>N</i> players, and hence obtain so far the tightest equilibrium error bounds for this class of models.</p>

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Risk-Sensitive Linear-Quadratic Mean-Field Games: Asymptotic Solvability and Decentralized O(1/N)-Nash Equilibria

  • 期刊论文

  • 发表日期：

    2025年2月1日

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  • Yu Wang,
  • Minyi Huang

摘要

This paper considers risk-sensitive linear-quadratic mean-field games. By the so-called direct approach via dynamic programming, the authors determine the feedback Nash equilibrium in an N-player game. Subsequently, the authors design a set of decentralized strategies by passing to the mean-field limit. The authors prove that the set of decentralized strategies constitutes an O(1/N)-Nash equilibrium when applied by the N players, and hence obtain so far the tightest equilibrium error bounds for this class of models.