Antitrust by Algorithm

Stanford Computational Antitrust, Vol. 2, p. 1, 2022

U of Penn, Inst for Law & Econ Research Paper No. 21-33

22 Pages Posted: 15 Dec 2021 Last revised: 17 Mar 2022

See all articles by Cary Coglianese

Cary Coglianese

University of Pennsylvania Carey Law School

Alicia Lai

University of Pennsylvania Law School ; U.S. Courts of Appeals

Date Written: 2022

Abstract

Technological innovation is changing private markets around the world. New advances in digital technology have created new opportunities for subtle and evasive forms of anticompetitive behavior by private firms. But some of these same technological advances could also help antitrust regulators improve their performance in detecting and responding to unlawful private conduct. We foresee that the growing digital complexity of the marketplace will necessitate that antitrust authorities increasingly rely on machine-learning algorithms to oversee market behavior. In making this transition, authorities will need to meet several key institutional challenges—building organizational capacity, avoiding legal pitfalls, and establishing public trust—to ensure successful implementation of antitrust by algorithm.

Keywords: Antitrust law & policy, competition, computational antitrust, artificial intelligence, machine learning, algorithmic regulation, information technology, data analytics, market dynamics, dynamic pricing, digital platforms, rent-seeking

JEL Classification: K21, L40, L50

Suggested Citation

Coglianese, Cary and Lai, Alicia, Antitrust by Algorithm (2022). Stanford Computational Antitrust, Vol. 2, p. 1, 2022, U of Penn, Inst for Law & Econ Research Paper No. 21-33, Available at SSRN: https://ssrn.com/abstract=3985553

Cary Coglianese (Contact Author)

University of Pennsylvania Carey Law School ( email )

3501 Sansom Street
Philadelphia, PA 19104
United States
215-898-6867 (Phone)

HOME PAGE: http://www.law.upenn.edu/coglianese

Alicia Lai

University of Pennsylvania Law School ( email )

Philadelphia, PA 19104
United States
8142066530 (Phone)

U.S. Courts of Appeals ( email )

Charlottesville, VA 22902
United States

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