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Academic Articles Awards > Concerted Practices

Antitrust and the Robo-Seller: Competition in the Time of Algorithms

Salil K. Mehra, Minnesota Law Review, Vol. 100, forthcoming 2016

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Readers’ vote will close on February 15, 2016. Readers’ vote will allow you to nominate 1 article for each of the Awards, i.e., 10 Academic articles, 10 Business articles, and the best Soft Laws. The readers’ short-list of Academic and Business Articles will be communicated to the Board together with the 20 articles nominated by the Steering Committees. The Board will decide on the award-winning articles. Results will be announced at the Awards ceremony to take place in Washington DC on the eve of the ABA Antitrust Spring Meeting on April 5, 2016.

Increasingly, firms are knitting together newly available mass data collection, Internet-driven interconnective power, and automated algorithmic selling with their traditional supply-chain and sales functions. Traditional sales functions such as competitive intelligence gathering and pricing are being delegated to software “robo-sellers.” This Article offers the first descriptive and normative study of the implications of this shift away from humans to machines (the “robo-sellers”) for antitrust law. This change is a critical challenge for antitrust law – both in how it is currently applied and in highlighting and exacerbating its existing weaknesses.

First – and critically – robo-sellers will increase the risk that oligopolists will coordinate prices above the competitive level, thereby harming consumers. The Sherman Act contains a well-known gap in its coverage under which oligopolists that achieve price coordination interdependently, without communication or facilitating practices, generally escape antitrust enforcement, even when their actions yield supracompetitive pricing that harms consumers. Because robo-sellers possess traits that will make them better than humans at achieving supracompetitive pricing without communication, all things being equal, they will increase consumer harm due to this gap.

A second problem concerns blackletter antitrust law in dealing with price coordination through communication or facilitating practices; current doctrine requires that there be an anticompetitive “agreement” for there to be a violation of the Sherman Act for price fixing. Under standard models, even where oligopolists have independent incentives to price supracompetitively, they can often do better via an agreement; moreover, in other cases, competing firms can only achieve supracompetitive pricing by explicit collusion. In these cases, usually analyzed as a prisoner’s dilemma in which the Nash equilibrium is to “cheat” on the cartel, an agreement is required to avoid the inferior (from the price-fixers’ perspective) outcome. In order to find such an “agreement,” courts, government enforcers, and practitioners tend to focus on finding “intent,” efforts to sowing fear and distrust, and discovering a “meeting of the minds.” These standard inquiries derive from a more than a century-old embedded assumption that antitrust regulates sales by human actors; they will be a poor fit in addressing robo-sellers, which will function differently and which will likely not create the same kinds of evidence that these inquiries rely on.

What can be done about the anticompetitive effects of robo-selling? This Article assesses several possible solutions, but find that they will be quite difficult to reconcile with current antitrust law. It conclude that, at least as a feasible second-best result, incorporating an evolving approach to robo-sellers may be a worthwhile expansion of the FTC’s ongoing regulatory program that has already begun target the competition and consumer protection aspects of consumer data collection by sellers. For example, the FTC has already begun to consider the effects of mass data collection and algorithmic processing on consumers from the perspective of disclosure and discrimination (both price and social); efficiencies should exist in broadening the inquiry to include effects on price coordination and cartel behavior.

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