探索大五人格与AI能力在LLM模拟谈判对话中的影响
Exploring Big Five Personality and AI Capability Effects in LLM-Simulated Negotiation Dialogues
AI总结：
本文基于Sotopia平台通过两个实验系统评估大五人格和AI特征（透明度、能力、适应性）对LLM模拟谈判结果的影响，发现宜人性和外向性显著影响可信度与目标达成，并建立了面向人机团队动态的可重复AI可靠性评估方法。
摘要：
本文提出了一个用于任务关键型谈判场景中智能体AI系统的评估框架，旨在满足AI智能体需要适应多样化人类操作者和利益相关者的需求。我们以Sotopia作为模拟测试平台，开展了两个实验，系统评估了人格特质和AI智能体特征如何影响LLM模拟的社交谈判结果——这一能力对于涉及跨团队协调和军民互动的多种应用至关重要。实验1采用因果发现方法来测量人格特质如何影响讨价还价谈判，我们发现宜人性和外向性显著影响可信度、目标达成和知识获取结果。从团队沟通中提取的社会认知词汇测量检测到了智能体在共情沟通、道德基础和观点模式方面的细粒度差异，为必须在高风险操作场景中可靠运行的智能体AI系统提供了可操作的见解。实验2通过操纵模拟人类人格和AI系统特征（具体包括透明度、能力和适应性）来评估人机工作谈判，展示了AI智能体的可信度如何影响任务效能。这些发现建立了一种可重复的评估方法，用于在多样化操作者人格和人机团队动态中实验AI智能体的可靠性，直接支持可靠AI系统的操作需求。我们的工作推进了智能体AI工作流的评估，超越了标准性能指标，纳入了对复杂操作中任务成功至关重要的社会动态。
英文摘要：
This paper presents an evaluation framework for agentic AI systems in mission-critical negotiation contexts, addressing the need for AI agents that can adapt to diverse human operators and stakeholders. Using Sotopia as a simulation testbed, we present two experiments that systematically evaluated how personality traits and AI agent characteristics influence LLM-simulated social negotiation outcomes--a capability essential for a variety of applications involving cross-team coordination and civil-military interactions. Experiment 1 employs causal discovery methods to measure how personality traits impact price bargaining negotiations, through which we found that Agreeableness and Extraversion significantly affect believability, goal achievement, and knowledge acquisition outcomes. Sociocognitive lexical measures extracted from team communications detected fine-grained differences in agents' empathic communication, moral foundations, and opinion patterns, providing actionable insights for agentic AI systems that must operate reliably in high-stakes operational scenarios. Experiment 2 evaluates human-AI job negotiations by manipulating both simulated human personality and AI system characteristics, specifically transparency, competence, adaptability, demonstrating how AI agent trustworthiness impact mission effectiveness. These findings establish a repeatable evaluation methodology for experimenting with AI agent reliability across diverse operator personalities and human-agent team dynamics, directly supporting operational requirements for reliable AI systems. Our work advances the evaluation of agentic AI workflows by moving beyond standard performance metrics to incorporate social dynamics essential for mission success in complex operations.
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。
