LLM Rationalis? Measuring Bargaining Capabilities of AI Negotiators

2025-12-15

AI Summary

This work systematically evaluates the bargaining capabilities of large language models (LLMs) in bilateral negotiations, exposing fundamental deficiencies in anchoring, contextual adaptation, and opponent modeling: pervasive extreme anchoring, neglect of power asymmetries and situational context, and monotonous, non-improving strategies across model generations. We propose a novel concession dynamics framework grounded in the hyperbolic tangent function and introduce two interpretable, quantitative metrics—burstiness parameter τ and Concession Rigidity Index (CRI). Through rigorous mathematical modeling, large-scale natural-language and numerical offer experiments, controlled studies across six power-asymmetry conditions, and qualitative strategy analysis, we demonstrate that human negotiators exhibit adaptive anchor adjustment, implicit leverage recognition, and rhythmic concession control—capabilities absent in current LLMs. Our findings reveal a critical gap between human strategic adaptability and LLM behavioral rigidity in negotiation settings.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Bilateral negotiation is a complex, context-sensitive task in which human negotiators dynamically adjust anchors, pacing, and flexibility to exploit power asymmetries and informal cues. We introduce a unified mathematical framework for modeling concession dynamics based on a hyperbolic tangent curve, and propose two metrics burstiness tau and the Concession-Rigidity Index (CRI) to quantify the timing and rigidity of offer trajectories. We conduct a large-scale empirical comparison between human negotiators and four state-of-the-art large language models (LLMs) across natural-language and numeric-offers settings, with and without rich market context, as well as six controlled power-asymmetry scenarios. Our results reveal that, unlike humans who smoothly adapt to situations and infer the opponents position and strategies, LLMs systematically anchor at extremes of the possible agreement zone for negotiations and optimize for fixed points irrespective of leverage or context. Qualitative analysis further shows limited strategy diversity and occasional deceptive tactics used by LLMs. Moreover the ability of LLMs to negotiate does not improve with better models. These findings highlight fundamental limitations in current LLM negotiation capabilities and point to the need for models that better internalize opponent reasoning and context-dependent strategy.

Problem

• Measuring LLM bargaining capabilities in bilateral negotiations
• Quantifying concession dynamics with burstiness and rigidity metrics
• Comparing human adaptability to LLM anchoring in power-asymmetry scenarios

Innovation

• Hyperbolic tangent curve models concession dynamics
• Burstiness tau and CRI metrics quantify offer trajectories
• Empirical comparison of humans and LLMs in varied negotiation scenarios