When Machines Learn to Collude: Lessons from a Recent Research Study on Artificial Intelligence
8 Pages Posted: 5 Sep 2017 Last revised: 18 May 2020
Date Written: August 30, 2017
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
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