# Bounded Normative Equivalence in Human-AI Cooperation: Group Behaviour, Not Partner Labels, Predicts Cooperation under Anonymous Aggregate Feedback

arXiv: https://arxiv.org/abs/2601.20487 · HTML: https://mlsanity.com/paper/2601.20487/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 52 references that are in the map.
- First posted on arXiv 28 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [Emergence of Social Norms in Generative Agent Societies: Principles and Architecture](https://mlsanity.com/paper/2403.08251/) (2024)
- [A new sociology of humans and machines](https://mlsanity.com/paper/2402.14410/) (2024)
- [Collective Intelligence in Human-AI Teams: A Bayesian Theory of Mind Approach](https://mlsanity.com/paper/2208.11660/) (2022)
- [Who/What is My Teammate? Team Composition Considerations in Human-AI Teaming](https://mlsanity.com/paper/2105.11000/) (2021)
- [Warmth and competence in human-agent cooperation](https://mlsanity.com/paper/2201.13448/) (2022)
- [Shaping New Norms for AI](https://mlsanity.com/paper/2307.08564/) (2023)
- [TIP: A Trust Inference and Propagation Model in Multi-Human Multi-Robot Teams](https://mlsanity.com/paper/2301.10928/) (2023)
- [Learning Nudges for Conditional Cooperation: A Multi-Agent Reinforcement Learning Model](https://mlsanity.com/paper/2409.09509/) (2024)
- [AI's assigned gender affects human-AI cooperation](https://mlsanity.com/paper/2412.05214/) (2024)
- [Promoting Cooperation in the Public Goods Game using Artificial Intelligent Agents](https://mlsanity.com/paper/2412.05450/) (2024)

## Abstract (arXiv)

The introduction of artificial intelligence (AI) agents into human groups raises questions about how they influence cooperative social norms. Prior work has examined human-AI and human-robot teaming in small groups, but less is known about whether an AI label alters cooperation and norm-related outcomes in repeated group interactions. We report an online experiment using a repeated four-player Public Goods Game. Each group comprised three human participants and one bot, framed either as human or AI, following one of three predefined strategies: unconditional cooperation, conditional cooperation, or free-riding. Among 236 participants, cooperation was primarily associated with the group's contribution in the previous round and with participants' own previous contributions. These patterns were similar across human- and AI-labelled conditions, and cooperation levels did not differ significantly by agent label; a formal equivalence test (TOST) indicated that any label effect was smaller than +/-5 tokens (5% of the endowment). We also found no evidence of label-based differences in norm persistence in a follow-up Prisoner's Dilemma or in participants' normative perceptions. We describe this pattern as bounded normative equivalence: under anonymous aggregate group feedback, an AI label produced no detectable differences in observed cooperation or norm-related outcomes. We argue that this equivalence is bounded by the informational structure of the setting: aggregate feedback makes individual actions difficult to attribute, diluting the identity cues that might otherwise trigger differentiation. These findings suggest that, in collective settings where individual contributions are not identifiable, cooperative norms can extend to groups that include artificial agents.