不完全合作的人机交互：在模拟和用户研究中比较人类和AI属性的影响
Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies
AI总结：
研究通过模拟和用户实验比较人类和AI属性在不完全合作场景中的影响，发现AI属性，尤其是透明度，在真实用户研究中更具影响力。
摘要：
AI设计特征和人类个性特质各自影响人机交互的质量和结果。然而，在不完全合作的情景中，它们的相对和联合影响尚未被充分探讨，其中人和AI只有部分目标一致。本研究比较了包含2000次模拟的纯模拟数据集和涉及290名人类参与者的平行人类实验，以调查两种情景类别中的这些影响：（1）人类求职者与AI招聘代理的招聘谈判；（2）人机交易中AI代理可能隐瞒信息以最大化内部目标。我们检查了用户的外向性和宜人性，以及AI设计特征，包括适应性、专业知识和推理过程透明度。我们的因果发现分析通过整合基于场景的结果、沟通分析和问卷测量扩展了以性能为导向的评估。结果揭示了纯模拟数据集和人类研究数据集之间的差异，以及不同情景类型之间的差异。在模拟实验中，个性特质和AI属性相对重要。然而，在实际人类参与者中，AI属性——特别是透明度——更具影响力。我们讨论这些差异在不同交互情境中的变化，为未来的人本AI代理提供关键见解。
英文摘要：
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.
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。
