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32 Antitrust 75 (2017-2018)
The Implications of Algorithmic Pricing for Coordinated Effects Analysis and Price Discrimination Markets in Antitrust Enforcement

handle is hein.journals/antitruma32 and id is 77 raw text is: A R T I C L E S The Implications of Algorithmic Pricing for Coordinated Effects Analysis and Price Discrimination Markets in Antitrust Enforcement BY TERRELL MCSWEENY AND BRIAN O'DEA HEN CONGRESS ENACTED THE Sherman and Clayton Acts over a century ago, the term robot did not exist.1 The framers of our antitrust laws would likely be amazed by the increasingly powerful and autonomous technologies, such as algorithms, machine learn- ing, and artificial intelligence (AI) that have come to play a significant role in many firms' competitive behavior. These technologies have the potential to deliver meaningful con- sumer benefits. For example, algorithms may enable firms to become more efficient and to provide consumers with per- sonalized product recommendations. Big data and algorithms may also provide companies with insights that help them design better products and services. But these technologies are also likely to present novel chal- lenges for competition enforcers. We must understand the potential effects of intelligent, high-velocity pricing tech- nologies on competition and adapt our enforcement approach to keep pace. For example, algorithmic pricing might con- tribute to overt collusion or facilitate tacit collusion. It is also possible, as we show in this article, that increasingly sophisti- cated price discrimination may lead to narrower relevant prod- uct markets, potentially increasing the chances that a merger will harm consumers in some relevant market. Algorithmic Collusion Some applications of antitrust law in the age of machines will be familiar. For example, the Department of Justice recently prosecuted two e-commerce sellers for agreeing to align their pricing algorithms to increase online prices for posters.2 In that case, United States v. Topkins, the humans reached an explicit agreement to use technology to fix prices. The appli- cation of antitrust law to that agreement was straightfor- ward. As algorithms and the software running them become more sophisticated, however, coordinated behavior may become more common without explicit instruction by humans. Challenging conduct where the role of humans in decision making is less clear may be more difficult under cur- rent law.3 For example, while express collusion is illegal, mere conscious parallelism is not.4 Separating one from the other can prove difficult even when dealing with solely human decision making. Professor Salil Mehra suggests that the rise of robo-sellers may make the task more difficult still: a number of current inquiries used to distinguish conscious parallelism from express collusion will be of limited use in the machine context. Concepts such as intent and meeting of the minds, he writes, presuppose quintessentially human mental states and thus may prove less useful in dealing with computer software and hardware.5 Algorithms Might Contribute to Overt Collusion. The defendants in Topkins used pricing algorithms as an instru- ment to facilitate a pre-arranged price fixing conspiracy.6 In their recent book, Virtual Competition, Professors Ariel Ezrachi and Maurice Stucke refer to this as a messenger sce- nario: the pricing algorithms were following explicit human instructions to violate the antitrust laws and thus merely act- ing as messengers among the various co-conspirators.7 It is worth pausing to consider why the Topkins defendants chose to employ algorithms rather than setting prices and monitoring their agreement directly. Algorithms may facili- tate the stability of certain price-fixing schemes by enabling firms to more quickly detect, and respond to, attempts to cheat on the collusive pricing agreement. The U.S. antitrust agencies' 2010 Horizontal Merger Guidelines specifically note that speed in identifying and responding to competitors' strategic initiatives is a factor that makes markets more vul- nerable to coordinated conduct. Swift competitive reaction times diminish each firm's prospective competitive reward from attracting customers away from its rivals.8 Margrethe Vestager, the European Commissioner for Competition, recently remarked on the potential for algo- rithms to sustain cartel behavior: Every cartel faces the risk that its members will start cheat- ing each other as well as the public. If everyone else's price is high, you can gain a lot of customers by quietly under- cutting them. So whether cartels survive depends on how quickly others spot those lower prices, and cut their own price in retaliation. By doing that quickly, cartelists can make sure that others will be less likely to try cutting prices in the F A L L 2 0 1 7 - 7 5

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