多智能体最大覆盖问题：无序与稳定之间的权衡
Multiagent Maximum Coverage Problems: The Trade-off Between Anarchy and Stability
AI总结：
本文研究了多智能体最大覆盖问题，从设计角度探讨了无序与稳定之间的权衡，提出了一个显式的帕累托前沿，揭示了在覆盖游戏中无序价格与稳定价格之间的根本权衡关系，并展示了如何利用系统级信息优化系统性能。
摘要：
无序价格和稳定价格是三种广泛研究的性能度量标准，旨在表征分布式系统中均衡的低效率。这两种度量标准的区别在于它们所关注的均衡：无序价格表征最差表现的均衡质量，而稳定价格表征最佳表现的均衡质量。尽管大部分文献从分析角度研究这些度量标准，但本文从设计角度考虑这些度量标准。具体而言，我们关注系统运营商设计局部效用函数以优化这些度量标准的场景，所涉及的游戏称为覆盖游戏。我们的主要结果表征了无序价格与稳定价格之间的根本权衡，形式为一个完全显式的帕累托前沿。在此设定中，优化无序价格直接以稳定价格为代价（反之亦然）。我们的第二个结果展示了系统运营商如何将额外的系统级信息纳入智能体效用函数的设计中，以突破这些限制并提高系统性能。这种有价值的系统级信息涉及系统中最差表现的智能体的性能。
英文摘要：
The price of anarchy and price of stability are three well-studied performance metrics that seek to characterize the inefficiency of equilibria in distributed systems. The distinction between these two performance metrics centers on the equilibria that they focus on: the price of anarchy characterizes the quality of the worst-performing equilibria, while the price of stability characterizes the quality of the best-performing equilibria. While much of the literature focuses on these metrics from an analysis perspective, in this work we consider these performance metrics from a design perspective. Specifically, we focus on the setting where a system operator is tasked with designing local utility functions to optimize these performance metrics in a class of games termed covering games. Our main result characterizes a fundamental trade-off between the price of anarchy and price of stability in the form of a fully explicit Pareto frontier. Within this setup, optimizing the price of anarchy comes directly at the expense of the price of stability (and vice versa). Our second results demonstrates how a system-operator could incorporate an additional piece of system-level information into the design of the agents' utility functions to breach these limitations and improve the system's performance. This valuable piece of system-level information pertains to the performance of worst performing agent in the system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
