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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.14066 |
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| _version_ | 1866910213528879104 |
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| author | Benac, Leo Raedler, Jonas Ma, Zilin Doshi-Velez, Finale |
| author_facet | Benac, Leo Raedler, Jonas Ma, Zilin Doshi-Velez, Finale |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14066 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Benchmark for Multi-Party Negotiation Games from Real Negotiation Data Benac, Leo Raedler, Jonas Ma, Zilin Doshi-Velez, Finale Multiagent Systems Artificial Intelligence Machine Learning 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 |
| title | A Benchmark for Multi-Party Negotiation Games from Real Negotiation Data |
| topic | Multiagent Systems Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2603.14066 |