Toward a Better Understanding of the Emotional Dynamics of Negotiation with Large Language Models

Current approaches to building negotiation agents rely either on model-based techniques that explicitly implement key principles of negotiation or model-free techniques leveraging algorithms developed via training on large amounts of human-generated text. We bridge these two approaches by combining a model-based approach with large language models for natural language understanding and generation. We find large language models perform well at recognizing dialogue acts and an opponent's emotions; perform reasonably well at recognizing opponents' preferences in the negotiation; and perform worse at understanding opponent offers. We also perform a qualitative comparison of the capabilities of our hybrid approach with a model-free method and find our hybrid agent provides safeguards against hallucinations and guarantees more control over aspects of negotiation such as emotional expressions, information sharing, and concession strategies.