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

arXiv 2608.15634 ↗First posted 16 Aug 2026cs.AIcited by 0

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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.

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References · 10

  1. 2020Interpreting Neural Networks as Gradual Argumentation Frameworks (Including Proof Appendix)cited by 39
  2. 2018Modular Semantics and Characteristics for Bipolar Weighted Argumentation Graphscited by 23
  3. 2024Can Large Language Models perform Relation-based Argument Mining?cited by 23
  4. 2024Contribution Functions for Quantitative Bipolar Argumentation Graphs: A Principle-based Analysiscited by 11
  5. 2017Empirical Evaluation of Abstract Argumentation: Supporting the Need for Bipolar and Probabilistic Approachescited by 9
  6. 2025Retrieval- and Argumentation-Enhanced Multi-Agent LLMs for Judgmental Forecasting (Extended Version with Supplementary Material)cited by 8
  7. 2021Machine Learning for Utility Prediction in Argument-Based Computational Persuasioncited by 6
  8. 2025Using Collective Dialogues and AI to Find Common Ground Between Israeli and Palestinian Peacebuilderscited by 6
  9. 2024Impact Measures for Gradual Argumentation Semanticscited by 5
  10. 2026From User Preferences to Base Score Extraction Functions in Gradual Argumentation (with Appendix)cited by 1

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