Abstract: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: this https URL
| Subjects: | Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2603.14066 [cs.MA] |
| (or arXiv:2603.14066v2 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2603.14066 arXiv-issued DOI via DataCite |
Submission history
From: Leo Benac [view email]
[v1]
Sat, 14 Mar 2026 18:12:06 UTC (524 KB)
[v2]
Tue, 12 May 2026 23:30:59 UTC (228 KB)