[论文解读] Normative Equivalence in Human-AI Cooperation: Behaviour, Not Identity, Drives Cooperation in Mixed-Agent Groups

Nico Mutzner, Taha Yasseri+1 more|arXiv (Cornell University)|Jan 28, 2026

Psychology of Moral and Emotional Judgment被引用 1

一句话总结

研究表明，在四人公共物品博弈中，合作动态由群体行为和规范性期望驱动，而非伙伴是人类还是 AI 的标签；在混合人类–AI 群体中存在规范等价。

ABSTRACT

The introduction of artificial intelligence (AI) agents into human group settings raises essential questions about how these novel participants influence cooperative social norms. While previous studies on human-AI cooperation have primarily focused on dyadic interactions, little is known about how integrating AI agents affects the emergence and maintenance of cooperative norms in small groups. This study addresses this gap through an online experiment using a repeated four-player Public Goods Game (PGG). Each group consisted of three human participants and one bot, which was framed either as human or AI and followed one of three predefined decision strategies: unconditional cooperation, conditional cooperation, or free-riding. In our sample of 236 participants, we found that reciprocal group dynamics and behavioural inertia primarily drove cooperation. These normative mechanisms operated identically across conditions, resulting in cooperation levels that did not differ significantly between human and AI labels. Furthermore, we found no evidence of differences in norm persistence in a follow-up Prisoner's Dilemma, or in participants' normative perceptions. Participants' behaviour followed the same normative logic across human and AI conditions, indicating that cooperation depended on group behaviour rather than partner identity. This supports a pattern of normative equivalence, in which the mechanisms that sustain cooperation function similarly in mixed human-AI and all human groups. These findings suggest that cooperative norms are flexible enough to extend to artificial agents, blurring the boundary between humans and AI in collective decision-making.

研究动机与目标

• 探索在一个成员被标注为 AI 时，较小群体的合作规范是否会改变（相较于全人类群体）
• 检验机器人策略（无条件合作、条件性合作、搭便车）对群体合作与规范形成的影响
• 在群体情境之外使用单次博弈与规范诱发衡量来评估规范持续性

提出的方法

• 各处理之间的 2x3 设计，四人组（3 人类 + 1 机器人），按标签（人类 vs AI）与机器人策略（无条件合作、条件性合作、搭便车）分组
• 线性公共物品博弈十轮，随后进行单次囚徒困境
• 机器人遵循预设策略，未向参与者披露；贡献量乘以 1.5 后均等再分配
• 通过规范诱发任务测量社会适宜性、经验性规范与指令性规范
• 包含预注册与稳健性检验；样本量最终定格为完成 PGG 的 236 人

Figure 1 : Schematic representation of the experimental design and PGG mechanics

实验结果

研究问题

• RQ1 在混合组设置中，AI 标注的机器人是否会降低合作或规范影响力，相对于人工标注的机器人？
• RQ2 机器人策略（无条件、条件性、搭便车）如何塑造群体合作与规范形成？
• RQ3 单次 Prisoner’s Dilemma 中的规范持续性是否受代理标签或策略的影响？
• RQ4 参与者的规范性认知（社会适宜性、经验性/指引性规范）是否因代理标签而异？

主要发现

Predictor	Estimate	CI	p-value
AI label (human vs. AI)	1.09	-5.31 – 7.49	0.738

• 人类与 AI 标签下的合作水平相似；AI 标签并未显著降低贡献
• 机器人策略对平均合作的影响较小且在统计上不显著
• 规范性机制（条件性合作、对他人反应、惰性）在两种处理下主导合作行为
• 在单次 PD 中，规范持续性不因标签或机器人策略而异；先前的群体贡献在两种条件下均预测 PD 合作
• 任务后对规范的认知在两种处理之间一致，社会适宜性或规范期望在人工组与 AI 组之间无显著差异
• 信任预测总体贡献较高；在 AI 条件下，信任与规范压力提升了合作，但 AI 接受度预测贡献较低，提示算法厌恶效应

Figure 2 : Mean Contribution per Round by Treatment and Bot Strategy.