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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.


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

Publication Citation

Stanford Computational Antitrust, vol. 2, p. 1 (2022)