用AI生成多方协作文本的妥协方案，提升民主起草效率。
AI-Generated Compromises for Coalition Formation
- 结合认知局限与不确定性，用大模型构建文本语义空间
- 在模拟中实现多轮协作文本编辑，支持率显著高于传统工具
- 适合民主制定文件、集体创作等需要广泛共识的场景
在论证、调解和谈判等人工智能领域，寻找各方提案间的妥协方案是一项基础挑战。基于此，Elkind等人（2021）提出一种联盟形成机制，旨在寻找多数支持且优于现状的提案，其前提是每个代理在度量空间中具有理想点。该过程的关键步骤是识别可促成代理联盟的妥协提案，但如何高效发现此类提案仍是一个开放问题。本文通过形式化一个包含代理有限理性和不确定性的模型，并开发基于AI的妥协提案生成方法加以解决。研究聚焦于协作文档撰写场景，如社区宪章的民主起草。方法利用自然语言处理技术和大语言模型，构建文本的语义度量空间，并设计算法推荐可能获得广泛支持的妥协点。通过模拟联盟形成过程评估方法，结果表明AI能有效促进大规模民主文本编辑，这一领域传统工具能力有限。
原文摘要 · Abstract (English)
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.
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