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布于系统各组件。然而，随着通信与计算技术的进步，我们可以考虑介于集中式与分布式控制之间的新型协同决策范式。本研究旨在理解多智能体系统中增强协同通信所带来的收益与成本。我们特别在共同利益博弈的框架下探讨此问题，其中最多由k个智能体组成的群体能够协调其行动以最大化共同目标函数。此类系统中涌现的均衡态即共同利益博弈的k-强纳什均衡；研究这些状态的特征可为智能体间协作效能提供重要见解。我们的贡献包含三方面：1）通过形式化的k-强无政府状态代价，界定k-强纳什均衡逼近系统最优福利的边界；2）研究基于协同智能体动态过程的运行时间与暂态性能；3）探索通过重构智能体群体目标函数以提升系统性能的任务。我们不仅从一般理论层面研究这三个维度，更将其置于资源分配问题的具体语境中，在此我们提出了可处理的线性规划方法，为k-强无政府状态代价提供紧致边界。