How Much Is No Longer A Simple Question – Pricing Algorithms and Antitrust Laws

Abstract

With the backdrop of increased use of algorithms in doing business, the objective of this paper is examining in-depth the use of one such category of algorithms – pricing algorithms. The scope of this paper is confined to two potential issues associated with the use of pricing algorithms – algorithmic collusion and personalized pricing. Finally, this paper offers solutions that may be used by antitrust agencies in the United States, i.e., the Department of Justice and the Federal Trade Commission, to deal with these issues within the confines of the existing antitrust jurisprudence. Section I of this paper describes what pricing algorithms are, and how they assist with dynamic pricing. The section demonstrates that it is irrefutable that the use of pricing algorithms has improved the competitive landscape by increasing transparency, reducing information asymmetry and improving the allocative efficiency of the market, bringing it closer to the ideal of perfect competition. At the same time, agencies are wary that the use of big data analytics and pricing algorithms could lead to the anti competitive outcomes which both contribute to and are aggravated by concentration of market power. Section II examines the role pricing algorithms play in facilitating in explicit and tacit collusion. The section also concludes that the existing framework of antitrust law would be sufficient while dealing with algorithmic forms of explicit collusion, where an algorithm is used to execute an anti competitive agreement but may not suffice for tackling algorithmic tacit collusion. Further, trying to uncover evidence of anti-competitive agreement and intent may prove challenging as machine learning is an ongoing process and the design of an algorithm can be complicated to decipher. Section III explores the use of algorithms in personalizing pricing. This section notes that while from an economic perspective, personalized pricing fosters static efficiency, it also has the tendency to be used to exploit unsuspecting consumers. Policymakers and antitrust agencies are also concerned about issues of fairness and consumer welfare regarding the use of pricing algorithms. However, given that the unilateral use of a pricing algorithm to set high prices is not an offense under US antitrust laws, agencies are skeptical of prosecuting the use of exploitative personalized pricing. Section IV puts forward some possible solutions. The paper reasons that at this time, rather than making legislative changes, it is essential for the FTC to invest in research into such methods in order to both uncover evidence of anti-competitive agreements and audit algorithms that appear to collude even in the absence of an agreement. Further, this paper argues that FTC may have the locus to investigate exploitative personalized pricing under Section 5 of the FTC Act . Though, the most practical short-term solution seems to be to increase transparency and to put power back into the hands of the consumer.