AI design characteristics and human personality traits each impact the quality and outcomes of human-AI interactions. However, their relative and joint impacts are underexplored in imperfectly cooperative scenarios, where people and AI only have partially aligned goals and objectives. This study compares a purely simulated dataset comprising 2,000 simulations and a parallel human subjects experiment involving 290 human participants to investigate these effects across two scenario categories: (1) hiring negotiations between human job candidates and AI hiring agents; and (2) human-AI transactions wherein AI agents may conceal information to maximize internal goals. We examine user Extraversion and Agreeableness alongside AI design characteristics, including Adaptability, Expertise, and chain-of-thought Transparency. Our causal discovery analysis extends performance-focused evaluations by integrating scenario-based outcomes, communication analysis, and questionnaire measures. Results reveal divergences between purely simulated and human study datasets, and between scenario types. In simulation experiments, personality traits and AI attributes were comparatively influential. Yet, with actual human subjects, AI attributes -- particularly transparency -- were much more impactful. We discuss how these divergences vary across different interaction contexts, offering crucial insights for the future of human-centered AI agents.

翻译：AI设计特征与人类个性特质均会影响人机交互的质量与结果。然而，在人类与AI仅具有部分一致目标的不完美合作场景中，二者的相对影响与联合效应尚未得到充分探索。本研究对比了包含2000次模拟的纯仿真数据集与涉及290名受试者的平行人类实验，在两类场景中考察这些效应：（1）人类求职者与AI招聘代理之间的雇佣谈判；（2）AI代理可能隐藏信息以最大化内部目标的人机交易场景。我们考察了用户的外向性与宜人性特质，以及AI设计特征（包括适应性、专业性与思维链透明度）。我们的因果发现分析通过整合基于场景的结果变量、对话分析与问卷调查指标，拓展了以绩效为导向的评估。结果显示，纯仿真数据与人类实验数据集之间以及不同场景类型之间均存在差异。在模拟实验中，个性特质与AI属性具有相当的影响力；然而在实际人类受试者中，AI属性（尤其是透明度）的影响更为显著。我们讨论了这些差异如何随交互情境而变化，为以人为中心的AI代理未来发展提供了关键启示。