多议题谈判博弈中具有人格特质的大型语言模型
LLMs with Personalities in Multi-issue Negotiation Games
AI总结：
本文在博弈论框架下通过1,500次单议题与多议题谈判模拟，分析Big Five人格对LLM公平、理性、毒性及谈判结果的影响，并提出评估与设计谈判机器人的框架。
摘要：
在大型语言模型（LLMs）的驱动下，AI智能体已经能够完成许多人类任务。我们采用Big Five人格最规范的定义，在博弈论框架内衡量LLMs的谈判能力，以及衡量公平与风险概念所面临的方法论挑战。针对单议题和多议题谈判开展的模拟（n=1,500）表明，随着议题估值不对称导致领域复杂性增加，协议达成率有所提高，但激进谈判带来的剩余却下降。通过梯度提升回归和Shapley解释器，我们发现高开放性、尽责性和神经质与公平倾向相关；低宜人性和低开放性与理性倾向相关；低尽责性与高毒性相关。这些结果表明，LLMs可能具有内置护栏，会默认采取公平行为，但也可能被“jail broken”以利用宜人性较高的对手。我们还为如何设计谈判机器人提供了务实洞见，并给出一个基于博弈论和计算社会科学评估谈判行为的框架。
英文摘要：
Powered by large language models (LLMs), AI agents have become capable of many human tasks. Using the most canonical definitions of the Big Five personality, we measure the ability of LLMs to negotiate within a game-theoretical framework, as well as methodological challenges to measuring notions of fairness and risk. Simulations (n=1,500) for both single-issue and multi-issue negotiation reveal increase in domain complexity with asymmetric issue valuations improve agreement rates but decrease surplus from aggressive negotiation. Through gradient-boosted regression and Shapley explainers, we find high openness, conscientiousness, and neuroticism are associated with fair tendencies; low agreeableness and low openness are associated with rational tendencies. Low conscientiousness is associated with high toxicity. These results indicate that LLMs may have built-in guardrails that default to fair behavior, but can be "jail broken" to exploit agreeable opponents. We also offer pragmatic insight in how negotiation bots can be designed, and a framework of assessing negotiation behavior based on game theory and computational social science.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
