Collusive Algorithms as Mere Tools, Super-tools or Legal Persons

Guan Zheng and Hong Wu

Journal of Competition Law and Economics, 2019, vol. 15, issue 2-3, 123-158

Abstract: The widespread use of algorithmic technologies makes rules on tacit collusion, which are already controversial in antitrust law, more complicated. These rules have obvious limitations in effectively regulating algorithmic collusion. Although some scholars and practitioners within antitrust circles in the United States, Europe and beyond have taken notice of this problem, they have failed to a large extent to make clear its specific manifestations, root causes, and effective legal solutions. In this article, the authors make a strong argument that it is no longer appropriate to regard algorithms as mere tools of firms, and that the distinct features of machine learning algorithms as super-tools and as legal persons may inevitably bring about two new cracks in antitrust law. This article clarifies the root causes why these rules are inapplicable to a large extent to algorithmic collusion particularly in the case of machine learning algorithms, classifies the new legal cracks, and provides sound legal criteria for the courts and competition authorities to assess the legality of algorithmic collusion much more accurately. More importantly, this article proposes an efficacious solution to revive the market pricing mechanism for the purposes of resolving the two new cracks identified in antitrust law.

Date: 2019
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Journal of Competition Law and Economics is currently edited by Nicholas Economides, Amelia Fletcher, Michal Gal, Damien Geradin, Ioannis Lianos and Tommaso Valletti

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