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arXiv 2603.17153cs.GT
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联盟形成中的分裂-合并动态

Split-Merge Dynamics for Shapley-Fair Coalition Formation

New York University(纽约大学)

Quanyan Zhu, Zhengye Han

AI总结:

本文提出一种动态分裂-合并框架,平衡个体公平与集体效率,通过拓扑操作信号实现公平和效率的动态平衡,证明算法在有限时间内收敛到Shapley-Fair且Merge-Stable的稳定状态。

摘要:

联盟形成常被建模为静态均衡问题,忽视了支配代理自我组织的动态过程。本文提出一种动态分裂-合并框架,平衡两个冲突的经济力量:个体公平和集体效率。我们引入一种控制论机制,其中拓扑操作由不同的信号驱动:分裂由公平性违规(具体而言,负Shapley值代表

英文摘要:

Coalition formation is often modeled as a static equilibrium problem, neglecting the dynamic processes governing how agents self-organize. This paper proposes a dynamic split-and-merge framework that balances two conflicting economic forces: individual fairness and collective efficiency. We introduce a control-theoretic mechanism where topological operations are driven by distinct signals: splits are triggered by fairness violations (specifically, negative Shapley values representing "agent-responsible inefficiency"), while merges are driven by strict surplus improvements (superadditivity). We prove that these dynamics converge in finite time to a specific class of steady states termed Shapley-Fair and Merge-Stable (SFMS) partitions. Convergence is established via a vector Lyapunov function tracking aggregate fairness deficits and system surplus, leveraging a discrete-time LaSalle invariance principle. Numerical case studies on a 10-player game demonstrate the algorithm's ability to resolve fairness tensions and reach stable configurations, providing a rigorous foundation for endogenous coalition formation in dynamic environments.

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。

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