Document Type
Article
Publication Date
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
Publication Title
Stanford Computational Antitrust
Repository Citation
Coglianese, Cary and Lai, Alicia, "Antitrust by Algorithm" (2022). All Faculty Scholarship. 2755.
https://scholarship.law.upenn.edu/faculty_scholarship/2755
Included in
Antitrust and Trade Regulation Commons, Artificial Intelligence and Robotics Commons, Economic Policy Commons, Law and Economics Commons, Public Administration Commons
Publication Citation
Stanford Computational Antitrust, vol. 2, p. 1 (2022)