# Strategic Algorithmic Monoculture: Experimental Evidence from Coordination Games

arXiv: https://arxiv.org/abs/2604.09502 · HTML: https://mlsanity.com/paper/2604.09502/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 71 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Multi-Agent Risks from Advanced AI](https://mlsanity.com/paper/2502.14143/) (2025)
- [Algorithmic Collusion by Large Language Models](https://mlsanity.com/paper/2404.00806/) (2024)
- [LLM economicus? Mapping the Behavioral Biases of LLMs via Utility Theory](https://mlsanity.com/paper/2408.02784/) (2024)
- [We're Different, We're the Same: Creative Homogeneity Across LLMs](https://mlsanity.com/paper/2501.19361/) (2025)
- [Cultivating Pluralism In Algorithmic Monoculture: The Community Alignment Dataset](https://mlsanity.com/paper/2507.09650/) (2025)
- [Heads, Tails, and AI Fails: LLMs, Randomness, and Human Judgments](https://mlsanity.com/paper/2406.00092/) (2024)
- [Competition and Diversity in Generative AI](https://mlsanity.com/paper/2412.08610/) (2024)
- [EconEvals: Benchmarks and Litmus Tests for Economic Decision-Making by LLM Agents](https://mlsanity.com/paper/2503.18825/) (2025)
- [Human Misperception of Generative-AI Alignment: A Laboratory Experiment](https://mlsanity.com/paper/2502.14708/) (2025)
- [Recognising, Anticipating, and Mitigating LLM Pollution of Online Behavioural Research](https://mlsanity.com/paper/2508.01390/) (2025)
- [Reducing Selection Bias in Large Language Models](https://mlsanity.com/paper/2402.01740/) (2024)
- [Strategic Hiring under Algorithmic Monoculture](https://mlsanity.com/paper/2502.20063/) (2025)

## Built on by (most cited first, 7 of 7)

- [The Judgment-Consequence Gap: LLM Moral Reasoning in Healthcare Decisions](https://mlsanity.com/paper/2608.05583/) (2026)
- [Expressing Social Emotions: Misalignment Between LLMs and Human Cultural Emotion Norms](https://mlsanity.com/paper/2604.16757/) (2026)
- [When Shippers Become Algorithms: Candidate Exposure, Information Design, and the Concentration of LLM-Mediated Freight Markets](https://mlsanity.com/paper/2607.19967/) (2026)
- [Symbolic Reasoning Frameworks Trigger Memory-Mediated Ecosystem Dynamics in Multi-Agent LLM Systems](https://mlsanity.com/paper/2606.07552/) (2026)
- [Diversifying Personalized Research Ideation against AI-Induced Homogenization](https://mlsanity.com/paper/2607.28087/) (2026)
- [Fair Like Us? Auditing LLM Alignment in Resource Allocation](https://mlsanity.com/paper/2609.29692/) (2026)
- [Shared and structured inputs undermine collective random choice by reasoning AI agents](https://mlsanity.com/paper/2610.09667/) (2026)

## Abstract (arXiv)

AI agents increasingly operate in multi-agent environments where outcomes depend on coordination. We distinguish primary algorithmic monoculture -- baseline action similarity -- from strategic algorithmic monoculture, whereby agents adjust similarity in response to incentives. We implement a simple experimental design that cleanly separates these forces, and deploy it on human and large language model (LLM) subjects. LLMs exhibit high levels of baseline similarity (primary monoculture) and, like humans, they regulate it in response to coordination incentives (strategic monoculture). While LLMs coordinate extremely well on similar actions, they lag behind humans in sustaining heterogeneity when divergence is rewarded.