合则俱损：多重网络与多层网络博弈的纳什均衡研究
United We Fall: On the Nash Equilibria of Multiplex and Multilayer Network Games
AI总结：
针对现有网络博弈模型无法覆盖多网络交互、多行动维度场景的问题，本文提出多重与多层网络博弈新模型，分析组成网络特征值对纳什均衡性质的影响，揭示均衡脆弱性成因并给出干预方向。
摘要：
网络博弈为研究受智能体间结构化依赖关系支配的策略决策过程提供了框架。然而，现有模型未考虑智能体同时在多个网络上交互，或智能体在多个行动维度上运作的环境。本文提出多重网络博弈新模型以刻画策略智能体间不同的交互模式，提出多层网络博弈模型以刻画其在多个行动维度上的交互。我们探究了多重/多层网络的组成网络的属性如何削弱或支撑博弈纳什均衡的存在性、唯一性与稳定性。值得注意的是，我们强调组成网络的最大与最小特征值（分别反映其连通性与双向性）是决定多重/多层网络博弈均衡唯一性的关键因素。综上，我们的发现揭示了智能体在“网络之网络”上交互时均衡脆弱性的成因，并指出了缓解该问题的潜在干预手段。
英文摘要：
Network games provide a framework to study strategic decision making processes that are governed by structured interdependencies among agents. However, existing models do not account for environments in which agents simultaneously interact over multiple networks, or when agents operate over multiple action dimensions. In this paper, we propose new models of multiplex network games to capture the different modalities of interactions among strategic agents, and multilayer network games to capture their interactions over multiple action dimensions. We explore how the properties of the constituent networks of a multiplex/multilayer network can undermine or support the existence, uniqueness, and stability of the game's Nash equilibria. Notably, we highlight that both the largest and smallest eigenvalues of the constituent networks (reflecting their connectivity and two-sidedness, respectively) are instrumental in determining the uniqueness of the multiplex/multilayer network game's equilibrium. Together, our findings shed light on the reasons for the fragility of equilibria when agents interact over networks of networks, and point out potential interventions to alleviate them.
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。
