AI-Generated Compromises for Coalition Formation
arXiv:2506.06837v4 Announce Type: replace Abstract: The challenge of finding compromises between agent proposals is fundamental to AI subfields such as argumentation, mediation, and negotiation. Building on this tradition, Elkind et al. (2021) introduced a process for coalition formation that seeks majority-supported proposals preferable to the status quo, using a metric space where each agent has an ideal point. A crucial step in this process involves identifying compromise proposals around which agent coalitions can unite. How to effectively find such compromise proposals remains an open question. We address this gap by formalizing a model that incorporates agent bounded rationality and uncertainty, and by developing AI methods to generate compromise proposals. We focus on the domain of
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Abstract:The challenge of finding compromises between agent proposals is fundamental to AI subfields such as argumentation, mediation, and negotiation. Building on this tradition, Elkind et al. (2021) introduced a process for coalition formation that seeks majority-supported proposals preferable to the status quo, using a metric space where each agent has an ideal point. A crucial step in this process involves identifying compromise proposals around which agent coalitions can unite. How to effectively find such compromise proposals remains an open question. We address this gap by formalizing a model that incorporates agent bounded rationality and uncertainty, and by developing AI methods to generate compromise proposals. We focus on the domain of collaborative document writing, such as the democratic drafting of a community constitution. Our approach uses natural language processing techniques and large language models to induce a semantic metric space over text. Based on this space, we design algorithms to suggest compromise points likely to receive broad support. To evaluate our methods, we simulate coalition formation processes and show that AI can facilitate large-scale democratic text editing, a domain where traditional tools are limited.
Subjects:
Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
Cite as: arXiv:2506.06837 [cs.MA]
(or arXiv:2506.06837v4 [cs.MA] for this version)
https://doi.org/10.48550/arXiv.2506.06837
arXiv-issued DOI via DataCite
Submission history
From: Eyal Briman [view email] [v1] Sat, 7 Jun 2025 15:28:27 UTC (84 KB) [v2] Sun, 3 Aug 2025 13:13:17 UTC (84 KB) [v3] Sat, 9 Aug 2025 18:00:01 UTC (85 KB) [v4] Tue, 31 Mar 2026 12:27:54 UTC (165 KB)
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