Exit-and-Join Dynamics for Decentralized Coalition Formation

Computer Science > Artificial Intelligence [Submitted on 18 Jun 2026] Exit-and-Join Dynamics for Decentralized Coalition Formation Quanyan Zhu This paper studies coalition formation as a decentralized dynamical process driven by unilateral exit-and-join decisions. Agents evaluate local moves using the Aumann-Dreze value, so payoffs are computed within the agent's current coalition rather than through a globally negotiated coalition structure. The resulting model links cooperative payoff allocation with noncooperative best-response behavior: a terminal partition is precisely a coalition structure with no admissible, individually profitable exit-and-join deviation. We establish equilibrium characterizations, identify conditions under which the dynamics admit scalar Lyapunov or exact-potential representations, and analyze how switching and acceptance costs shape local stability. Numerical experiments test finite-time stabilization, cost sensitivity, and a special convex-game benchmark. Subjects: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Systems and Control (eess.SY) MSC classes: 91A12, 91A43, 68T05, 91D30 ACM classes: I.2.11; I.2.6; J.4 Cite as: arXiv:2606.19683 [cs.AI]   (or arXiv:2606.19683v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.19683 Focus to learn more Submission history From: Quanyan Zhu [view email] [v1] Thu, 18 Jun 2026 01:19:31 UTC (76 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.MA cs.SY eess eess.SY References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)