When Machines Learn to Collude: Lessons from a Recent Research Study on Artificial Intelligence

8 Pages Posted: 5 Sep 2017 Last revised: 20 Sep 2017

Ai Deng

Bates White, LLC; Johns Hopkins University

Date Written: August 30, 2017

Abstract

From Professors Maurice Stucke and Ariel Ezrachi’s Virtual Competition published a year ago, to speeches by the Federal Trade Commission Commissioner Terrell McSweeny and Acting Chair Maureen K. Ohlhausen, to an entire issue of a recent CPI Antitrust Chronicles, and a conference hosted by Organisation for Economic Co-operation and Development (OECD) in June this year, there has been an active and ongoing discussion in the antitrust community about computer algorithms. In this note, I briefly summarize the current views and concerns in the antitrust and artificial intelligence (AAI) literature pertaining to algorithmic collusion and then discuss the insights and lessons we could learn from a recent AI research study. As I argue in the article, not all assumptions in the antitrust scholarship have empirical support at this point.

Keywords: Antitrust, Artificial Intelligence, Algorithmic Collusion

Suggested Citation

Deng, Ai, When Machines Learn to Collude: Lessons from a Recent Research Study on Artificial Intelligence (August 30, 2017). Available at SSRN: https://ssrn.com/abstract=3029662 or http://dx.doi.org/10.2139/ssrn.3029662

Ai Deng (Contact Author)

Bates White, LLC ( email )

2001 K Street NW, North Building
Suite 500
Washington, DC DC 20006
United States

Johns Hopkins University ( email )

1717 Massachusetts Ave NW
Washington, DC DC 20036
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
603
rank
40,158
Abstract Views
1,716
PlumX