A Benchmark for Multi-Party Negotiation Games from Real Negotiation Data

2026-03-14

AI Summary

This work addresses the lack of effective modeling and evaluation frameworks for sequential, binding-commitment multi-party negotiation scenarios. It introduces the first benchmark for multi-party negotiation games grounded in real-world negotiation data, featuring a configurable generator that modulates structural properties such as incentive alignment, objective complexity, and payoff distributions. The study systematically evaluates three biased value function approximation strategies—myopic reward, optimistic upper bound, and pessimistic lower bound—achieving exact assessment in small-scale games and identifying strategy-specific regimes of effectiveness in large-scale Harvard negotiation instances. The findings highlight the critical influence of game structure on strategy selection, thereby advancing research on long-horizon planning and robust state-value learning in complex negotiation settings.

Technology Category

• Multiagent Systems: Agreement, Argumentation & Negotiation
• Game Theory and Economic Paradigms: Cooperative Game Theory
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Many real-world multi-party negotiations unfold as sequences of binding, action-level commitments rather than a single final outcome. We introduce a benchmark for this under-studied regime featuring a configurable game generator that sweeps key structural properties such as incentive alignment, goal complexity, and payoff distribution. To evaluate decision-making, we test three value-function approximations - myopic reward, an optimistic upper bound, and a pessimistic lower bound - that act as biased lenses on deal evaluation. Through exact evaluation on small games and comparative evaluation on large, document-grounded instances derived from the Harvard Negotiation Challenge, we map the strategic regimes where each approximation succeeds or fails. We observe that different game structures demand different valuation strategies, motivating agents that learn robust state values and plan effectively over long horizons under binding commitments and terminal only rewards.

Problem

• multi-party negotiation
• binding commitments
• benchmark
• real negotiation data
• decision-making

Innovation

• multi-party negotiation
• configurable game generator
• value-function approximation
• binding commitments
• strategic evaluation