Antitrust by Algorithm
Stanford Computational Antitrust, Vol. 2, p. 1, 2022
22 Pages Posted: 15 Dec 2021 Last revised: 17 Mar 2022
Cary Coglianese
University of Pennsylvania Carey Law School
Alicia Lai
Susman Godfrey, LLP; University of Pennsylvania Law School
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: Suggested Citation
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
Susman Godfrey, LLP ( email )
New York, NY
United States
University of Pennsylvania Law School ( email )
Philadelphia, PA 19104
United States
8142066530 (Phone)
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