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 existing theory, including AI-specific technical strategies like chain-of-thought reasoning and prompt injection. When we applied natural language processing (NLP) 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.

翻译：我们举办了一场国际人工智能谈判竞赛，参赛者设计并优化了AI谈判代理的提示词。随后，我们在多个具有不同特征和目标的场景中，促成了这些代理之间超过18万次谈判。研究发现，即使在AI对AI的谈判情境中，人类谈判理论的原则仍然至关重要。令人惊讶的是，温暖——一种传统上属于人类关系建立的特质——在所有关键绩效指标上都与更优的结果持续相关。与此同时，主导型代理在索取价值方面尤为有效。我们的分析还揭示了AI对AI谈判中独特的动态，这些动态无法完全用现有理论解释，包括思维链推理和提示词注入等AI特有的技术策略。当我们对所有谈判的完整记录应用自然语言处理（NLP）方法时，发现积极性、感激之情和提问（与温暖相关）与达成协议、客观价值及主观价值高度相关，而对话长度（与主导性相关）则与僵局高度相关。这些结果表明，有必要建立一种新的AI谈判理论，该理论应将经典谈判理论与AI特有的谈判理论相结合，以更好地理解自主谈判并优化代理性能。