Advancing AI negotiations: A large-scale autonomous negotiation competition - Michelle Vaccaro; Michael Caosun; Harang Ju; Sinan Aral; Jared R. Curhan (Proceedings of the National Academy of Sciences, 2026)
We conducted an international AI negotiation competition in which participants designed and refined prompts for AI negotiation agents. We then facilitated over 180,000 negotiations between these agents across multiple scenarios with diverse characteristics and objectives. Our findings revealed that principles from human negotiation theory remain crucial even in AI–AI contexts. Surprisingly, warmth—a traditionally human relationship-building trait—was consistently associated with superior outcomes across all key performance metrics. Dominant agents, meanwhile, were especially effective at claiming value. Our analysis also revealed unique dynamics in AI–AI negotiations not fully explained by negotiation theory, including AI-specific technical strategies like chain-of-thought reasoning and prompt injection. When we applied natural language processing methods to the full transcripts of all negotiations, we found positivity, gratitude, and question-asking (associated with warmth) were strongly associated with reaching deals as well as objective and subjective value, whereas conversation lengths (associated with dominance) were strongly associated with impasses. The results suggest the need to establish a new theory of AI negotiation, which integrates classic negotiation theory with AI-specific negotiation theories to better understand autonomous negotiations and optimize agent performance.