# AI-Generated Compromises for Coalition Formation: Modeling, Simulation, and a Textual Case Study

arXiv: https://arxiv.org/abs/2512.05983 · HTML: https://mlsanity.com/paper/2512.05983/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 27 Nov 2025.

## Builds on (most cited first, 14 of 14)

- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Fine-tuning language models to find agreement among humans with diverse preferences](https://mlsanity.com/paper/2211.15006/) (2022)
- [LLM Voting: Human Choices and AI Collective Decision Making](https://mlsanity.com/paper/2402.01766/) (2024)
- [Generative Social Choice](https://mlsanity.com/paper/2309.01291/) (2023)
- [A Compare-Aggregate Model for Matching Text Sequences](https://mlsanity.com/paper/1611.01747/) (2016)
- [What Do Multiwinner Voting Rules Do? An Experiment Over the Two-Dimensional Euclidean Domain](https://mlsanity.com/paper/1901.09217/) (2019)
- [ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"](https://mlsanity.com/paper/1902.03616/) (2019)
- [Aggregation over Metric Spaces: Proposing and Voting in Elections, Budgeting, and Legislation](https://mlsanity.com/paper/1806.06277/) (2018)
- [United for Change: Deliberative Coalition Formation to Change the Status Quo](https://mlsanity.com/paper/2001.08031/) (2020)
- [Natural language processing for word sense disambiguation and information extraction](https://mlsanity.com/paper/2004.02256/) (2020)
- [Foundations for Grassroots Democratic Metaverse](https://mlsanity.com/paper/2203.04090/) (2022)
- [Iterative Deliberation via Metric Aggregation](https://mlsanity.com/paper/2108.00314/) (2021)

## Abstract (arXiv)

The challenge of finding compromises between agent proposals is fundamental to AI sub-fields 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. The crucial step in this iterative process involves identifying compromise proposals around which agent coalitions can unite. How to effectively find such compromise proposals, however, remains an open question. We address this gap by formalizing a holistic model that encompasses agent bounded rationality and uncertainty and developing AI models to generate such compromise proposals. We focus on the domain of collaboratively writing text documents -- e.g., to enable the democratic creation of a community constitution. We apply NLP (Natural Language Processing) techniques and utilize LLMs (Large Language Models) to create a semantic metric space for text and develop algorithms to suggest suitable compromise points. To evaluate the effectiveness of our algorithms, we simulate various coalition formation processes and demonstrate the potential of AI to facilitate large-scale democratic text editing, such as collaboratively drafting a constitution, an area where traditional tools are limited.