The control of large-scale, multi-agent systems often entails distributing decision-making across the system components. However, with advances in communication and computation technologies, we can consider new collaborative decision-making paradigms that exist somewhere between centralized and distributed control. In this work, we seek to understand the benefits and costs of increased collaborative communication in multi-agent systems. We specifically study this in the context of common interest games in which groups of up to k agents can coordinate their actions in maximizing the common objective function. The equilibria that emerge in these systems are the k-strong Nash equilibria of the common interest game; studying the properties of these states can provide relevant insights into the efficacy of inter-agent collaboration. Our contributions come threefold: 1) provide bounds on how well k-strong Nash equilibria approximate the optimal system welfare, formalized by the k-strong price of anarchy, 2) study the run-time and transient performance of collaborative agent-based dynamics, and 3) consider the task of redesigning objectives for groups of agents which improve system performance. We study these three facets generally as well as in the context of resource allocation problems, in which we provide tractable linear programs that give tight bounds on the k-strong price of anarchy.

翻译：大规模多智能体系统的控制通常需要将决策权分散到系统各组件中。然而，随着通信与计算技术的发展，我们可以考虑介于集中控制与分布式控制之间的新型协作决策范式。本文旨在探究多智能体系统中增强协作通信的收益与代价。我们聚焦于共同利益博弈这一场景，其中至多k个智能体可协调行动以最大化共同目标函数。此类系统产生的均衡状态即为共同利益博弈的k-强纳什均衡；研究这些状态的性质可为智能体间协作效能提供相关洞见。我们的贡献体现在三方面：（1）给出k-强纳什均衡逼近最优系统福利的边界，形式化为k-强无政府价格；（2）研究基于协作智能体动力学机制的运行时与暂态性能；（3）考虑为智能体群体重新设计目标以提升系统性能的任务。我们既从一般性角度研究这三个维度，也在资源分配问题中展开探讨，为此类问题提供可解线性规划以严格界定k-强无政府价格的上界。