Price Discrimination-Driven Algorithmic Collusion: Platforms for Durable Cartels

58 Pages Posted: 8 Oct 2020 Last revised: 10 Nov 2020

See all articles by Salil K. Mehra

Salil K. Mehra

Temple University - James E. Beasley School of Law

Date Written: September 24, 2020


Algorithmic competition has arrived. With it has come the specter of algorithmic collusion – rapid detection of co-conspirators’ defection via technologically enhanced price monitoring and setting capability can encourage anticompetitive collusion. Strikingly, the ability to track consumers’ willingness-to-pay and price discriminate among them may synergize with algorithmic collusion into something antitrust scholars had previously thought impossible: stable cartels.

In particular, consumer-facing digital platforms increasingly can determine consumers’ individual willingness to pay. Doing this allows them to deploy sophisticated forms of price discrimination, and thereby effect large welfare transfers from consumers to producers. This Article is the first to describe and analyze the potential interaction between price discrimination and algorithmic collusion. Algorithm-driven platforms now knit together large numbers of previously-independent firms and agents; some platforms set the price these participating firms and agents will charge. Crucially, if the gains to producers from collusive price discrimination are big enough, a qualitative change may take place: participants may find that they are no longer are in a Prisoner’s Dilemma tempting them to undercut each other on price, but rather in a coordination game with a single, rational choice: keep their collusion going. This Article sets forth how this dynamic can produce agreements by competitors, facilitated by price-discriminating, price-setting platforms that transfer wealth from consumers to producers – arguably a violation of Section 1 of the Sherman Act. Indeed, in contrast to the traditional view that firms need to first obtain Section 2 monopoly power, and only then can implement price discrimination, the model presented here shows the causation can run the other way: The ability to price discriminate effectively can drive the joint maintenance of monopoly power by colluding competitors. This dynamic takes on new urgency as more and more commerce shifts to the Internet and smartphone apps, a trend that has been accelerated by the COVID-19 pandemic and its associated acceleration of the shift to e-commerce.

Potential solutions to this problem will be complicated by antitrust law’s current relegation of price discrimination to the dead letter office – no Federal Trade Commission complaint under the Robinson-Patman Act, the main relevant statute, has been brought this century. Indeed, during the past decade, the most recent edition of the leading antitrust casebook in the U.S. deleted its section on price discrimination and the Act. This Article proposes three actions: (i) revive some enforcement against price discrimination, (ii) prioritize action against price discriminating platforms that inhibit switching by participants, including scrutinizing mergers between firms whose Big Data-based ability to gauge willingness-to-pay may, if combined, have negative ramifications for consumers, and (iii) factor price discrimination-driven algorithmic collusion into the current reevaluation of vertical restraints.

Keywords: antitrust, competition, collusion, Sherman Act, price discrimination, Robinson-Patman Act, gig economy

JEL Classification: K21, K20

Suggested Citation

Mehra, Salil K., Price Discrimination-Driven Algorithmic Collusion: Platforms for Durable Cartels (September 24, 2020). Stanford Journal of Law, Business, and Finance, Forthcoming, Temple University Legal Studies Research Paper No. 2020-35, Available at SSRN:

Salil K. Mehra (Contact Author)

Temple University - James E. Beasley School of Law ( email )

1719 North Broad Street
Philadelphia, PA 19122
United States
215-204-7113 (Phone)
215-204-1185 (Fax)

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