平均场对策的非渐近收敛率:弱公式和McKean-Vlasov BSDEs

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED
Dylan Possamaï, Ludovic Tangpi
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引用次数: 0

摘要

这项工作主要涉及所谓的平均场博弈的极限理论。采用Carmona和Lacker提出的弱公式范式(Ann Appl Probab 25(3): 1189-1231, 2015),我们考虑了一个完全非马尔可夫设置,允许漂移控制和通过玩家状态和控制的联合分布进行交互。我们首先给出了由一类新的McKean-Vlasov倒向随机微分方程的解引起的平均场平衡的特征,并为此提供了一个完备性理论。我们偶然地得到了平均场平衡的不寻常的存在性和唯一性结果,它不需要短时间范围,系数的可分性假设,也不需要Lasry和Lions的单调性条件,而是终端奖励的小或正则性条件和漂移的耗散性条件。然后,我们利用这一特征,为一般开环均衡的值函数和n人版本的纳什均衡提供非渐近收敛率。对我们的方法进行适当的重新表述也使我们能够处理闭环平衡,并获得与问题相关的主方程的收敛结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-asymptotic Convergence Rates for Mean-Field Games: Weak Formulation and McKean–Vlasov BSDEs

This work is mainly concerned with the so-called limit theory for mean-field games. Adopting the weak formulation paradigm put forward by Carmona and Lacker (Ann Appl Probab 25(3):1189–1231, 2015), we consider a fully non-Markovian setting allowing for drift control and interactions through the joint distribution of players’ states and controls. We provide first a characterisation of mean-field equilibria as arising from solutions to a novel kind of McKean–Vlasov backward stochastic differential equations, for which we provide a well-posedness theory. We incidentally obtain there unusual existence and uniqueness results for mean-field equilibria, which do not require short-time horizon, separability assumptions on the coefficients, nor Lasry and Lions’s monotonicity conditions, but rather smallness—or alternatively regularity—conditions on the terminal reward and a dissipativity condition on the drift. We then take advantage of this characterisation to provide non-asymptotic rates of convergence for the value functions and the Nash-equilibria of the N-player version to their mean-field counterparts, for general open-loop equilibria. An appropriate reformulation of our approach also allows us to treat closed-loop equilibria, and to obtain convergence results for the master equation associated to the problem.

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来源期刊
CiteScore
3.30
自引率
5.60%
发文量
103
审稿时长
>12 weeks
期刊介绍: The Applied Mathematics and Optimization Journal covers a broad range of mathematical methods in particular those that bridge with optimization and have some connection with applications. Core topics include calculus of variations, partial differential equations, stochastic control, optimization of deterministic or stochastic systems in discrete or continuous time, homogenization, control theory, mean field games, dynamic games and optimal transport. Algorithmic, data analytic, machine learning and numerical methods which support the modeling and analysis of optimization problems are encouraged. Of great interest are papers which show some novel idea in either the theory or model which include some connection with potential applications in science and engineering.
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