Virtual Competition

What are the implications of Big Data and Big Analytics on competition policy? EU and US competition authorities are currently grappling with this question. The EU Commission has recently issued preliminary results of its e-commerce sector enquiry. The European Data Protection Supervisor and the UK House of Lords, among others, have issued reports and convened roundtables on the issue. The OECD will host in late 2016 an enforcer roundtable on this subject.

The significance of these inquires cannot be overstated, as their conclusions will determine the dynamics of future technology markets and level of antitrust intervention.

As Virtual Competition (HUP 2016) and Big Data and Competition Policy (OUP 2016) explore in more detail, Big Data and the development of sophisticated computer algorithms and artificial intelligence are neither good, bad, nor neutral. Their nature depends on how firms employ them, whether their incentives are aligned with our interests, and certain market characteristics. At times, Big Data and Big Analytics can enhance information flows, access to markets, and promote a competitive online environment where we benefit.

However, we cannot uncritically assume that we will always benefit. When we critically examine the complex algorithm-driven environment, we will likely witness imperfections of the new market dynamics, including the following three potential risks:

The first risk involves collusion. Industries are shifting from a pricing environment where store clerks once stamped prices on products, to dynamic, differential pricing where sophisticated computer algorithms rapidly calculate and update prices. At times dynamic pricing is good—one example we explore in Virtual Competition is ’smart’ parking meters in San Francisco. But as pricing shifts from humans to computers, so too will the types of collusion in which companies may engage. For example, the use of a single algorithm by numerous competitors can establish a hub-and-spoke cartel. Algorithms may also foster tacit collusion, given their ability to detect and quickly react to price changes in a highly transparent market.

A second risk involves behavioural discrimination, which differs from price discrimination in several important respects. The strategy involves firms harvesting our personal data to identify which emotion (or bias) will prompt us to buy a product, and what is the most we are willing to pay. Sellers, in tracking us and collecting data about us, can tailor their advertising and marketing to target us at critical moments with the right price and emotional pitch. So behavioural discrimination increases profits by increasing overall consumption (by shifting the demand curve to the right and price discriminating) and reducing consumer surplus.

A third risk arises from the dynamics of ‘Frenemy’, where a relationship of both competition and cooperation exists between the super-platforms and independent apps. One example involves mobile and tablet operating systems, where two super-platforms—Apple's iOS and Google's Android mobile software platforms—dominate. Each super-platform, like a coral reef, attracts to its ecosystem software developers, apps, and accessory makers. A growing, and seemingly appealing, part of this marketplace are free goods and services. The proliferation of free mobile apps seemingly benefits consumers (as well as advertisers, smartphone manufacturers, mobile carriers, and independent application developers) by reducing search costs and increasing demand. The anticompetitive risks, however, arise when firms cooperate to extract data from individuals and promote asymmetrical information flows to foster behavioural exploitation, while simultaneously competing among themselves over the consumer surplus. Extraction and capture may be viewed from an evolutionary perspective: a den of lions cooperates to circle the gazelle and they then compete over which of them gets the choice cuts. They all benefit from the combined effort, yet the dominant lion gets the best cut, which further enhances its power.

Virtual competition is not necessarily bleak. The innovations from machine learning and Big Data can be transformative—lowering our search costs in finding a raincoat or parking spot, lowering entry barriers, creating new channels for expansion and entry, and ultimately stimulating competition.

But these technological developments will not necessarily improve our welfare. Much depends on how the companies employ the technologies and whether their incentives are aligned with our interests. What appears to be a competitive environment may not be the welfare-enhancing competition that we know.

Our concerns are not with technological advances or successful online businesses. Our concerns go deeper, to the core of the new market dynamics—where entry is possible, but expansion will likely be controlled by super-platforms; where choice is ample, but competition is limited; and where disruptive innovative threats emerge, but are eliminated through acquisitions or exclusionary practices. The competitive façade masks the wealth transfer, and the targets of anti-competitive practices—the buyers—are often unaware of the extent of the manipulation.

Data-driven online markets will not necessarily correct themselves. As power shifts to the hands of the few, the risks this will likely have for competition, our democratic ideals, and our economic and overall well-being will increase accordingly. The concerns are real, but so are the challenges for intervention. The anticompetitive effects are not always easy to see. Companies can be a step ahead in developing sophisticated strategies and technologies that distort the perceived competitive environment. Antitrust, while not obsolete, may prove difficult, at times, to apply even when a theory of harm is present and the competition agency wishes to intervene. Controversy may surround the timing of an intervention, its nature, and its extent. Moreover, the current laws may not deter some of the anticompetitive behaviour.

Accordingly, competition authorities must devote resources to understand how the rise of sophisticated computer algorithms and the new market reality can significantly change our paradigm of competition—either for the better or the worse. Greater coordination is necessary with privacy and consumer protection officials to assess the preconditions for an effective, welfare-enhancing competitive process. They may consider a range of dimensions, including ways to empower customers (including data mobility), foster incentives for mavericks to enter and expand in problematic markets, and deter abuses by data-opolies.

Otherwise, with a somnolent competition agency, we may likely experience more durable forms of collusion (beyond the reach of enforcers), more sophisticated forms of price discrimination, and an array of abuses by data-driven monopolies that, by controlling key platforms (like the operating system of your smartphone), dictate the flow of your personal data, and who gets to exploit you.

Author notes

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Ariel Ezrachi is the Slaughter and May Professor of Competition Law at the University of Oxford and Director of the University of Oxford Centre for Competition Law and Policy. Maurice E. Stucke is of counsel at the Konkurrenz Group and Professor at the University of Tennessee College of Law.