大图上有限智能体随机微分对策:1 .线性二次情形

IF 1.7 2区 数学 Q2 MATHEMATICS, APPLIED
Ruimeng Hu, Jihao Long, Haosheng Zhou
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引用次数: 0

摘要

本文研究了图上有限智能体线性二次对策问题。具体来说,我们提出了一个综合框架,通过整合异质和可解释的玩家互动来扩展现有文献。与以前的工作相比,我们的模型对战略决策过程提供了更现实的描述。对于一般图,我们建立了虚拟游戏的收敛性,这是一种广泛使用的迭代求解方法,用于确定我们提出的博弈模型的纳什均衡。值得注意的是,在适当的条件下,无论参与者的数量如何,这种趋同都是正确的。对于顶点传递图,我们开发了纳什均衡的半显式表征。通过严格的分析,我们证明了这种表征在一定条件下的完备性。我们提出了数值实验来验证我们的理论结果,并提供了对各种游戏动态和潜在图结构之间复杂关系的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Finite-Agent Stochastic Differential Games on Large Graphs: I. The Linear-Quadratic Case

In this paper, we study finite-agent linear-quadratic games on graphs. Specifically, we propose a comprehensive framework that extends the existing literature by incorporating heterogeneous and interpretable player interactions. Compared to previous works, our model offers a more realistic depiction of strategic decision-making processes. For general graphs, we establish the convergence of fictitious play, a widely-used iterative solution method for determining the Nash equilibrium of our proposed game model. Notably, under appropriate conditions, this convergence holds true irrespective of the number of players involved. For vertex-transitive graphs, we develop a semi-explicit characterization of the Nash equilibrium. Through rigorous analysis, we demonstrate the well-posedness of this characterization under certain conditions. We present numerical experiments that validate our theoretical results and provide insights into the intricate relationship between various game dynamics and the underlying graph structure.

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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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