Algorithms, AI and Mergers

Antitrust Law Journal (2023)

NYU Law and Economics Research Paper No. 23-36

50 Pages Posted: 5 Jun 2023 Last revised: 19 Sep 2023

See all articles by Michal Gal

Michal Gal

University of Haifa - Faculty of Law

Daniel L. Rubinfeld

University of California at Berkeley - School of Law; National Bureau of Economic Research (NBER); New York University (NYU) - Center for Law and Business

Date Written: June 5, 2023

Abstract

Algorithms, especially those based on artificial intelligence, play an increasingly important role in our economy. They are used by market participants to make pricing, output, quality, and inventory decisions; to predict market entry, expansion, and exit; and to predict regulatory moves. In a growing number of jurisdictions, algorithms are also used by regulators to detect and analyze anti-competitive conduct. This game-changing switch to (semi-)automated decision-making has the potential to reshape market dynamics. While the effect of algorithms on coordination between competitors has been a focus of attention, and scholarly work on their effects on unilateral conduct is beginning to accumulate, merger control issues have been undertreated. Accordingly, this article focuses on such issues.

The article identifies six main functions of algorithms that may affect market dynamics: collection and ordering of data; improving the ability to use existing data; reducing the need for data, for in-stance by generating synthetic data; monitoring; predicting, to deter-mine how different types of conduct, including mergers, are likely to affect market conditions; and decision-making.

The article demonstrates how such algorithms can exacerbate anti-competitive conduct with respect to both unilateral and coordinated effects. Towards this end, seven scenarios are explored: collusion, oligopolistic coordination, high unilateral prices, price discrimination, predation, selective pricing (in which a buyer offers a higher price to some suppliers in an aggressive bid for an input), and reducing the interoperability of datasets. For each scenario, we analyze how the market conditions necessary for such conduct are affected by algorithms.

These findings are then translated into merger policy. Algorithms are shown to affect substantive as well as institutional features of merger control. Algorithms also challenge some of the assumptions that are ingrained in merger control, suggesting that a more informed approach to some algorithmic-related mergers is appropriate.

Keywords: Algoriths, AI, Artificial intelligence, mergers, merger policy, abuse of dominance, collusion, algorithmic coordination, algorithmic competition, algorithmic collusion

JEL Classification: K21, K23, L12, L13, L22,L4, L41, L42, L43, L53

Suggested Citation

Gal, Michal and Rubinfeld, Daniel L., Algorithms, AI and Mergers (June 5, 2023). Antitrust Law Journal (2023), NYU Law and Economics Research Paper No. 23-36, Available at SSRN: https://ssrn.com/abstract=4469586

Michal Gal (Contact Author)

University of Haifa - Faculty of Law ( email )

Mount Carmel
Haifa, 31905
Israel

HOME PAGE: http://weblaw.haifa.ac.il/en/faculty/gal/pages/home.aspx

Daniel L. Rubinfeld

University of California at Berkeley - School of Law ( email )

215 Law Building
Berkeley, CA 94720-7200
United States
(510) 642-1959 (Phone)
(510) 642-3767 (Fax)

HOME PAGE: http://www.law.berkeley.edu/faculty/rubinfeldd

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

New York University (NYU) - Center for Law and Business ( email )

44 West Fourth Street, Suite 9-53
New York, NY 10012-1126
United States
(212) 992 8834 (Phone)

HOME PAGE: http://rb.gy/1voeui

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