Algorithmic collusion: A critical review

TL;DR

This paper critically reviews the potential for algorithmic collusion, highlighting the overestimation of machine learning capabilities in complex market settings and suggesting that current legal frameworks may be insufficient for self-learning algorithms.

Contribution

It provides a multidisciplinary critique of existing literature on algorithmic collusion, integrating insights from computer science and economics to assess real-world risks and legal implications.

Findings

Current models oversimplify market dynamics for collusion

Self-learning algorithms may not yet pose significant collusion risks

Legislative action might be needed for hub-and-spoke arrangements

Abstract

The prospect of collusive agreements being stabilized via the use of pricing algorithms is widely discussed by antitrust experts and economists. However, the literature is often lacking the perspective of computer scientists, and seems to regularly overestimate the applicability of recent progress in machine learning to the complex coordination problem firms face in forming cartels. Similarly, modelling results supporting the possibility of collusion by learning algorithms often use simple market simulations which allows them to use simple algorithms that do not produce many of the problems machine learning practitioners have to deal with in real-world problems, which could prove to be particularly detrimental to learning collusive agreements. After critically reviewing the literature on algorithmic collusion, and connecting it to results from computer science, we find that while it is likely too early to adapt antitrust law to be able to deal with self-learning algorithms colluding in real markets, other forms of algorithmic collusion, such as hub-and-spoke arrangements facilitated by centralized pricing algorithms might already warrant legislative action.