Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies

2026-04-16

AI Summary

This study investigates the relative and joint effects of human personality traits (extraversion, agreeableness) and AI design attributes (adaptability, expertise, thought transparency) on interaction quality and outcomes in partially aligned, non-fully cooperative human-AI interactions. Drawing on 2,000 simulation trials and a user study with 290 participants, it systematically compares simulated and real human data across two distinct scenarios: recruitment negotiation and information-concealment trading. Integrating causal discovery, communication content analysis, and survey measures, the research reveals that while personality and AI attributes exert comparable influence in simulations, AI attributes—particularly thought transparency—dominate in real interactions. Moreover, significant heterogeneity emerges between the two scenarios, underscoring the critical role of AI transparency in authentic human-AI collaboration.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Imperfectly Cooperative
• Human-AI Interaction
• Personality Traits
• AI Attributes
• Goal Misalignment

Innovation

• Imperfectly Cooperative Interaction
• Human-AI Collaboration
• Chain-of-Thought Transparency
• Causal Discovery
• Personality Traits