Split-Merge Dynamics for Shapley-Fair Coalition Formation

TL;DR

This paper introduces a dynamic split-and-merge process for coalition formation that balances fairness and efficiency, converging to stable, fair partitions using control theory and Lyapunov functions.

Contribution

It proposes a novel dynamic framework driven by fairness and surplus signals, with proven finite-time convergence to stable, fair coalition structures.

Findings

The dynamics converge to Shapley-Fair and Merge-Stable partitions.

Numerical simulations demonstrate effective resolution of fairness and efficiency tensions.

The approach provides a rigorous foundation for endogenous coalition formation.

Abstract

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.