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Computational antitrust is a new branch of legal informatics focused on the mechanization of antitrust analyses and procedures.

Its development can serve both sides of the aisle. On the one hand, it can provide companies with the tools to assess and ensure compliance with antitrust laws–before implementing new practices. It can also help them automate their interactions with antitrust agencies, starting with merger control. On the other hand, agencies can use computational tools for improving their assessment of (anti-competitive) practices and mergers. They can also benefit from more accurate data and new methods to mechanize part of their activities.

Computational antitrust has great potential. Antitrust laws regulate all economic transactions throughout the world; finding ways to make them more accurate while ensuring easier compliance or enforcement will benefit all stakeholders as well as the public at large. Against this background, our project’s mission is to introduce, discuss, and develop these tools. Thanks to recent technical advances, the momentum is on our side.

The Computational Antitrust project will initially focus on bringing together academics from different backgrounds (law, computer science, economics…) with developers, policymakers, and regulators.

Starting in 2021, we will invite authors to publish articles on a regular basis dealing with the intersection between antitrust and computational techniques. At the end of each calendar year, we will organize a workshop bringing together our authors with other experts to engage in a constructive dialogue. We will then publish an annual report gathering all the articles published throughout the year, the comments made during the workshop, and our team’s analysis.

In parallel, the Computational Antitrust team will establish a network of antitrust agencies for reporting their implementation of computational methods. These advancements will also appear in our annual report.

Project leader: Dr. Thibault Schrepel