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

TL;DR

This paper introduces AI models that generate compromise proposals for coalition formation in text editing, using NLP and large language models to facilitate democratic collaboration like constitution drafting.

Contribution

It formalizes a holistic model incorporating agent rationality and uncertainty, and develops algorithms leveraging NLP and LLMs for effective compromise proposal generation.

Findings

Algorithms successfully identify compromise proposals in simulated coalition processes.

AI models facilitate large-scale democratic text editing tasks.

Demonstrated potential for AI to support collaborative constitution drafting.

Abstract

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.