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The Implications of Algorithmic Pricing for Coordinated Effects Analysis and Price Discrimination Markets in Antitrust Enforceme ARTICLES Antitrust, Vol. 32, No. 1, Fall 2017. © 2017 by the American Bar Association. Reproduced with permission. All rights reserved. This information or any portion thereof may not be copied or disseminated in any form or by any means or stored in an electronic database or retrieval system without the express written consent of the American Bar Association. Algorithmic Collusion Some applications of antitrust law in the age of machines will The Implications of be familiar. For example, the Department of Justice recently prosecuted two e-commerce sellers for agreeing to align their Algorithmic Pricing for 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- Coordinated Effects cation of antitrust law to that agreement was straightfor- ward. As algorithms and the software running them become Analysis and more sophisticated, however, coordinated behavior may become more common without explicit “instruction” by humans. Challenging conduct where the role of humans in Price Discrimination decision making is less clear may be more difficult under cur- rent law.3 For example, while express collusion is illegal, mere Markets in Antitrust 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 Enforcement of “robo-sellers” may make the task more difficult still: a number of current inquiries used to distinguish conscious BY TERRELL MCSWEENY AND BRIAN O’DEA 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 HEN CONGRESS ENACTED THE with computer software and hardware.”5 Sherman and Clayton Acts over a century Algorithms Might Contribute to Overt Collusion. The ago, the term “robot” did not exist.1 The defendants in Topkins used pricing algorithms as an instru- framers of our antitrust laws would likely ment to facilitate a pre-arranged price fixing conspiracy.6 In Wbe amazed by the increasingly powerful and their recent book, Virtual Competition, Professors Ariel autonomous technologies, such as algorithms, machine learn- Ezrachi and Maurice Stucke refer to this as a “messenger” sce- ing, and artificial intelligence (AI) that have come to play a nario: the pricing algorithms were following explicit human significant role in many firms’ competitive behavior. These instructions to violate the antitrust laws and thus merely act- technologies have the potential to deliver meaningful con- ing as “messengers” among the various co-conspirators.7 sumer benefits. For example, algorithms may enable firms to It is worth pausing to consider why the Topkins defendants become more efficient and to provide consumers with per- chose to employ algorithms rather than setting prices and sonalized product recommendations. Big data and algorithms monitoring their agreement directly. Algorithms may facili- may also provide companies with insights that help them tate the stability of certain price-fixing schemes by enabling design better products and services. firms to more quickly detect, and respond to, attempts to But these technologies are also likely to present novel chal- cheat on the collusive pricing agreement. The U.S. antitrust lenges for competition enforcers. We must understand the agencies’ 2010 Horizontal Merger Guidelines specifically potential effects of intelligent, high-velocity pricing tech- note that speed in identifying and responding to competitors’ nologies on competition and adapt our enforcement approach strategic initiatives is a factor that makes markets more vul- to keep pace. For example, algorithmic pricing might con- nerable to coordinated conduct. Swift competitive reaction tribute to overt collusion or facilitate tacit collusion. It is also times diminish each firm’s “prospective competitive reward possible, as we show in this article, that increasingly sophisti- from attracting customers away from its rivals.”8 cated price discrimination may lead to narrower relevant prod- Margrethe Vestager, the European Commissioner for uct markets, potentially increasing the chances that a merger Competition, recently remarked on the potential for algo- will harm consumers in some relevant market. rithms to sustain cartel behavior: Every cartel faces the risk that its members will start cheat- Terrell McSweeny is a Commissioner, Federal Trade Commission. Brian ing each other as well as the public. If everyone else’s price O’Dea is Attorney Advisor to Commissioner McSweeny. The views expressed is high, you can gain a lot of customers by quietly under- in this article are those of the authors and do not necessarily reflect the cutting them. So whether cartels survive depends on how views of the Federal Trade Commission or any other Commis sioner. The quickly others spot those lower prices, and cut their own authors thank Nathan Wilson for his thoughtful comments. price in retaliation. By doing that quickly, cartelists can make sure that others will be less likely to try cutting prices in the FALL 2017 · 75 ARTICLES ious features of pricing algorithms may increase market price 21 Competition enforcers must recognize the possibility transparency and reduce reaction times among competitors. But other features of pricing algorithms may enable firms to that algorithms might facilitate cartel formation and reduce transparency and mask their competitive initiatives. Algorithms can enable companies to engage in sophisticated maintenance. A second possibility is that algorithms price discrimination involving a combination of differential may facilitate tacit collusion between competitors. “list” prices and targeted discounts. Salcedo’s paper models a scenario in which firms have “the option to obfuscate their algorithms so they can never be decoded” but his model future. And the trouble is, automated systems help to do finds that, even with this option, firms “would never choose exactly that.9 to obfuscate their algorithms.”22 Competition enforcers must recognize the possibility that The increasing power of algorithms and AI may indeed algorithms might facilitate cartel formation and maintenance. lead to more coordinated interaction but it is too early to say Detection of such arrangements may require novel inves- this with certainty. Future research may prove especially valu- tigative approaches or additional resources.10 From a legal able in this area. If, in fact, new technologies are found to perspective, however, the analysis of the messenger scenario make coordinated interaction between competitors more is “relatively straightforward.”11 Once detected, competition likely, that would provide a strong argument for an enhanced enforcers have the tools to challenge overt collusion. As focus on coordinated effects in merger analysis and for lower Vestager put it, “no one should imagine they can get away thresholds of concern related to coordinated effects. with price-fixing by allowing software to make those agree- Notably, the use of a pricing algorithm, by itself, does not ments for them.”12 raise antitrust concerns. And as the DOJ’s successful prose- Algorithms Might Facilitate Tacit Collusion. A sec- cution of algorithmic price fixing shows, enforcers will be able ond possibility is that algorithms may facilitate tacit collusion to identify and challenge the improper use of these new tech- between competitors. Ezrachi and Stucke describe this as the nologies in many cases. Nonetheless, the potential that pric- “predictable agent” scenario.13 Professor Salil Mehra notes ing algorithms will facilitate tacit collusion beyond the reach that “automated pricing powered by algorithmic processing of Section 1 of the Sherman Act is far from fanciful. Indeed, and mass data collection should reduce the costs to firms [of] the Federal Trade Commission’s authority under Section 5 of interdependent pricing.”14 the FTC Act to prosecute “unfair methods of competition” The analysis closely follows that above, with the focus may be the only current tool available to police individual again on the speed with which algorithms can identify and instances of algorithmic collusion.23 react to changing market dynamics.15 Mehra posits that pric- ing algorithms will surpass humans in their ability to achieve Price Discrimination Markets and sustain elevated prices through coordinated interaction: To price discriminate successfully (i.e., charge different prices “the increased accuracy in detecting changes in price, greater to different groups of consumers), firms must possess some speed in pricing response, and reduced irrationality in dis- degree of market power and there must be factors limiting the count rates all should make the robo-seller a more skillful oli- potential for buyers’ arbitrage. Economists recognize three gopolist than its human counterpart in competitive intelli- main types of price discrimination: (1) first-degree, or so- gence and sales.”16 Ezrachi and Stucke contend that “as called perfect price discrimination, in which each customer competitors’ prices shift online, their algorithms can assess is charged a different price that perfectly matches his or her and adjust prices—even for particular individuals at partic- willingness to pay; (2) second-degree price discrimination, in ular times and for thousands
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