# Regulation of Algorithmic Collusion

arXiv: https://arxiv.org/abs/2401.15794 · HTML: https://mlsanity.com/paper/2401.15794/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2024.

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

- [Algorithmic Collusion by Large Language Models](https://mlsanity.com/paper/2404.00806/) (2024)
- [Algorithmic Collusion Without Threats](https://mlsanity.com/paper/2409.03956/) (2024)
- [Games with Payments between Learning Agents](https://mlsanity.com/paper/2405.20880/) (2024)
- [Proximal Regret and Proximal Correlated Equilibria: A New Tractable Solution Concept for Online Learning and Games](https://mlsanity.com/paper/2511.01852/) (2025)
- [Markets with Heterogeneous Agents: Dynamics and Survival of Bayesian vs. No-Regret Learners](https://mlsanity.com/paper/2502.08597/) (2025)
- [Learning from Delayed Feedback in Games via Extra Prediction](https://mlsanity.com/paper/2509.22426/) (2025)
- [Mitigating Retaliatory Algorithmic Collusion in Repeated Games](https://mlsanity.com/paper/2609.20548/) (2026)
- [Algorithmic Collusion and the Complexity of Information-Value-Free Equilibria](https://mlsanity.com/paper/2609.22757/) (2026)
- [How Market Volatility Shapes Algorithmic Collusion: A Comparative Analysis of Learning-Based Pricing Algorithms](https://mlsanity.com/paper/2512.02134/) (2025)
- [Persistent Partners Raise Prices Among Learning Agents](https://mlsanity.com/paper/2609.35402/) (2026)

## Abstract (arXiv)

Consider sellers in a competitive market that use algorithms to adapt their prices from data that they collect. In such a context it is plausible that algorithms could arrive at prices that are higher than the competitive prices and this may benefit sellers at the expense of consumers (i.e., the buyers in the market). This paper gives a definition of plausible algorithmic non-collusion for pricing algorithms. The definition allows a regulator to empirically audit algorithms by applying a statistical test to the data that they collect. Algorithms that are good, i.e., approximately optimize prices to market conditions, can be augmented to contain the data sufficient to pass the audit. Algorithms that have colluded on, e.g., supra-competitive prices cannot pass the audit. The definition allows sellers to possess useful side information that may be correlated with supply and demand and could affect the prices used by good algorithms. The paper provides an analysis of the statistical complexity of such an audit, i.e., how much data is sufficient for the test of non-collusion to be accurate.