用囚徒困境测试人类如何与独立智能体合作，发现身份设定影响合作行为。
When Trust Collides: Decoding Human-LLM Cooperation Dynamics through the Prisoner's Dilemma
- 让30人与不同身份的AI玩重复囚徒困境游戏
- 人类对LLM代理的合作率显著高于规则型AI
- 性别差异影响决策速度，身份塑造信任预期
随着大语言模型（LLMs）在自主决策方面能力增强，它们在混合动机情境中为人类-人工智能协作带来新挑战与机遇。以往研究多关注AI的辅助或合作角色，但对人类如何与被视为独立、策略性主体的AI互动知之甚少。本研究通过让30名参与者（15男15女）与三类不同声明身份的代理——伪装成人类、基于规则的AI、LLM代理——进行重复囚徒困境博弈，分析合作率、决策延迟、自发合作行为及信任修复容忍度等行为指标。结果显示，代理身份对多数合作行为有显著影响，且性别在决策延迟上存在明显差异。质性反馈表明，这些行为差异源于参与者对代理的解读与预期。研究深化了对人类在竞争性协作中适应机制的理解，并强调了代理设定在构建有效、伦理的人机交互中的关键作用。
原文摘要 · Abstract (English)
As large language models (LLMs) become increasingly capable of autonomous decision-making, they introduce new challenges and opportunities for human-AI cooperation in mixed-motive contexts. While prior research has primarily examined AI in assistive or cooperative roles, little is known about how humans interact with AI agents perceived as independent and strategic actors. This study investigates human cooperative attitudes and behaviors toward LLM agents by engaging 30 participants (15 males, 15 females) in repeated Prisoner's Dilemma games with agents differing in declared identity: purported human, rule-based AI, and LLM agent. Behavioral metrics, including cooperation rate, decision latency, unsolicited cooperative acts and trust restoration tolerance, were analyzed to assess the influence of agent identity and participant gender. Results revealed significant effects of declared agent identity on most cooperation-related behaviors, along with notable gender differences in decision latency. Furthermore, qualitative responses suggest that these behavioral differences were shaped by participants interpretations and expectations of the agents. These findings contribute to our understanding of human adaptation in competitive cooperation with autonomous agents and underscore the importance of agent framing in shaping effective and ethical human-AI interaction.
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