A Benchmark for Multi-Party Negotiation Games from Real Negotiation Data
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Abstract (arXiv)
Many real-world multi-party negotiations unfold as sequences of binding, action-level commitments rather than a single final outcome, yet this regime remains under-studied in existing benchmarks. We introduce a benchmark and evaluation framework for this setting, combining a configurable negotiation game generator with document-grounded instances derived from a climate negotiation exercise. We also provide several baseline solvers. Exact evaluation on small games and comparative evaluation on larger instances show that no solver dominates across regimes; performance depends on the structural properties of the game. These results motivate the creation of novel negotiation methods that value partial commitments robustly across diverse strategic regimes. Code and data for the benchmark are available at: https://anonymous.4open.science/r/negotiation_MARL-46B8
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 24 references that are in the map. First posted on arXiv 14 Mar 2026.
Builds on
9 of 24 references.
References · 9
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2017Deal or No Deal? End-to-End Learning for Negotiation Dialoguescited by 202
- 2018Decoupling Strategy and Generation in Negotiation Dialoguescited by 156
- 2023Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiationcited by 94
- 2022AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-Ncited by 22
- 2024"ChatGPT, Don't Tell Me What to Do": Designing AI for Context Analysis in Humanitarian Frontline Negotiationscited by 6
- 2024Uncertainty-Aware Reward-Free Exploration with General Function Approximationcited by 5
- 2025Learning to Negotiate via Voluntary Commitmentcited by 2
- 2023Coalitional Bargaining via Reinforcement Learning: An Application to Collaborative Vehicle Routingcited by 1
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Citing papers per year: 2026: 2.
2 papers.