标题： 具有对角占优的网络控制与均场问题：分散化与社会最优性 显示英文标题

标题： Networked Control and Mean Field Problems Under Diagonal Dominance: Decentralized and Social Optimality

摘要： 在本文中，我们采用输入输出方法来扩展研究由平均场相互作用表征的协同多智能体控制和优化问题，这些问题可以得到去中心化和自私的解决方案。 该设置涉及 $n$个独立智能体，它们仅通过一个共享成本函数进行交互，该函数惩罚每个智能体相对于群体平均集体行为的偏离。 在我们之前针对同质智能体建立的结果基础上，我们将框架扩展到非相同的智能体，并表明，在集体动态的对角占优相互作用下，具有有界局部开环动态的情况下，当智能体数量 $n$趋于无穷大时， $H_\infty$ 和 $H_2$ 范数最小化的最优控制器仍保持去中心化和自私。 显示英文摘要

摘要： In this article, we employ an input-output approach to expand the study of cooperative multi-agent control and optimization problems characterized by mean-field interactions that admit decentralized and selfish solutions. The setting involves $n$ independent agents that interact solely through a shared cost function, which penalizes deviations of each agent from the group's average collective behavior. Building on our earlier results established for homogeneous agents, we extend the framework to nonidentical agents and show that, under a diagonal dominant interaction of the collective dynamics, with bounded local open-loop dynamics, the optimal controller for $H_\infty$ and $H_2$ norm minimization remains decentralized and selfish in the limit as the number of agents $n$ grows to infinity.