# Artificial Intelligence and Spontaneous Collusion

arXiv: https://arxiv.org/abs/2202.05946 · HTML: https://mlsanity.com/paper/2202.05946/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 12 Feb 2022.

## Builds on (most cited first, 2 of 2)

- [Maintaining cooperation in complex social dilemmas using deep reinforcement learning](https://mlsanity.com/paper/1707.01068/) (2017)
- [Exploration-Exploitation in Multi-Agent Learning: Catastrophe Theory Meets Game Theory](https://mlsanity.com/paper/2012.03083/) (2020)

## Built on by (most cited first, 10 of 10)

- [On the Fragility of AI Agent Collusion](https://mlsanity.com/paper/2603.20281/) (2026)
- [Is Thompson Sampling Susceptible to Algorithmic Collusion?](https://mlsanity.com/paper/2405.17463/) (2024)
- [The Illusion of Collusion](https://mlsanity.com/paper/2411.16574/) (2024)
- [Impact of Price Inflation on Algorithmic Collusion Through Reinforcement Learning Agents](https://mlsanity.com/paper/2504.05335/) (2025)
- [Misspecified Estimate-then-Optimize Leads to Supra-Competitive Prices](https://mlsanity.com/paper/2605.16064/) (2026)
- [Oblivious Learning and Collusive Pricing](https://mlsanity.com/paper/2606.05363/) (2026)
- [The Bounds of Algorithmic Collusion; Q-learning, Gradient Learning, and the Folk Theorem](https://mlsanity.com/paper/2411.12725/) (2024)
- [Cycles and collusion in congestion games under Q-learning](https://mlsanity.com/paper/2502.18984/) (2025)
- [Homogenization of Multi-agent Learning Dynamics in Finite-state Markov Games](https://mlsanity.com/paper/2506.21079/) (2025)
- [Persistent Partners Raise Prices Among Learning Agents](https://mlsanity.com/paper/2609.35402/) (2026)

## Abstract (arXiv)

We develop a tractable model for studying strategic interactions between learning algorithms. We uncover a mechanism responsible for the emergence of algorithmic collusion. We observe that algorithms periodically coordinate on actions that are more profitable than static Nash equilibria. This novel collusive channel relies on an endogenous statistical linkage in the algorithms' estimates which we call spontaneous coupling. The model's parameters predict whether the statistical linkage will appear, and what market structures facilitate algorithmic collusion. We show that spontaneous coupling can sustain collusion in prices and market shares, complementing experimental findings in the literature. Finally, we apply our results to design algorithmic markets.