Limiting Algorithmic Cartels

Recent studies have proven that pricing algorithms can autonomously learn to coordinate prices, and set them at supra-competitive levels. The growing use of such algorithms mandates the creation of solutions that limit the negative welfare effects of algorithmic coordination. Unfortunately, to date, no good means exist to limit such conduct. While this challenge has recently prompted scholars from around the world propose different solutions, many suggestions are inefficient or impractical, and some might even strengthen coordination.This challenge requires thinking outside the box. Accordingly, this article suggests four (partial) solutions. The first is market-based, and entails using consumer algorithms to counteract at least some of the negative effects of algorithmic coordination. By creating buyer power, such algorithms can also enable offline transactions, eliminating the online transparency that strengthens coordination. The second suggestion is to change merger review so as to limit mergers that are likely to increase algorithmic coordination. The next two are more radical, yet can capture more cases of such conduct. The third involves the introduction of a disruptive algorithm, which would disrupt algorithmic coordination by creating noise on the supply side. The final suggestion entails freezing the price of one competitor, in line with prior suggestions to address predatory pricing suggested by Edlin and others. The advantages and risks of each solution are discussed. As antitrust agencies around the world are just starting to experiment with different ways to limit algorithmic coordination, there is no better time to explore how best to achieve this important task