# Argumentation for Common Ground: Finding Zones of Possible Agreement between Individuals in Conflict

arXiv: https://arxiv.org/abs/2608.15634 · HTML: https://mlsanity.com/paper/2608.15634/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 16 Aug 2026.

## Builds on (most cited first, 10 of 10)

- [Interpreting Neural Networks as Gradual Argumentation Frameworks (Including Proof Appendix)](https://mlsanity.com/paper/2012.05738/) (2020)
- [Modular Semantics and Characteristics for Bipolar Weighted Argumentation Graphs](https://mlsanity.com/paper/1807.06685/) (2018)
- [Can Large Language Models perform Relation-based Argument Mining?](https://mlsanity.com/paper/2402.11243/) (2024)
- [Contribution Functions for Quantitative Bipolar Argumentation Graphs: A Principle-based Analysis](https://mlsanity.com/paper/2401.08879/) (2024)
- [Empirical Evaluation of Abstract Argumentation: Supporting the Need for Bipolar and Probabilistic Approaches](https://mlsanity.com/paper/1707.09324/) (2017)
- [Retrieval- and Argumentation-Enhanced Multi-Agent LLMs for Judgmental Forecasting (Extended Version with Supplementary Material)](https://mlsanity.com/paper/2510.24303/) (2025)
- [Machine Learning for Utility Prediction in Argument-Based Computational Persuasion](https://mlsanity.com/paper/2112.04953/) (2021)
- [Using Collective Dialogues and AI to Find Common Ground Between Israeli and Palestinian Peacebuilders](https://mlsanity.com/paper/2503.01769/) (2025)
- [Impact Measures for Gradual Argumentation Semantics](https://mlsanity.com/paper/2407.08302/) (2024)
- [From User Preferences to Base Score Extraction Functions in Gradual Argumentation (with Appendix)](https://mlsanity.com/paper/2602.14674/) (2026)

## Abstract (arXiv)

How can common ground between societies in conflict be identified when citizens' acceptability of peace agreements is shaped by contested narratives? Such acceptability is mediated not only by the clauses that agreements include or exclude, but crucially by citizens' subjective reasoning concerning agreements' clauses. In this paper, we leverage computational argumentation to introduce a novel approach to identifying mutually acceptable agreements among individuals in conflict, i.e. a Zone of Possible Agreement (ZOPA). First, we introduce a quantitative bipolar argumentation framework tailored to represent each side's reasoning about peace agreements. We then show how merging these frameworks can enable negotiators to identify peace agreements that are mutually acceptable. To evaluate our approach under conditions of real-world relevance, we focus on the Palestinian-Israeli conflict, where long-standing policy, practitioner and public interest underscores the demand for methods capable of analysing polarised public reasoning. We show how our framework identifies a ZOPA through theoretical analysis and preliminary experiments using survey data from both existing work and retrieved by a large language model. The results illustrate how argumentation can empower negotiators and conflict-resolution teams in mapping feasible ZOPAs grounded in citizens' reasoning.