The importance of tech firms, big data, and the accelerating use of digital technologies in many industries has led to a range of emerging competition issues, some of which are already a focus of public and private antitrust enforcement both in the United States and abroad. Assessing these issues may require the use of novel theoretical frameworks and nontraditional econometric methods.

Two-Sided Markets

Analyzing theories of harm relating to two-sided platforms often entails a variety of innovative approaches in market definition and competitive effects analyses. These require a deep understanding of zero-price markets, the implications of multi- or single-homing patterns, and entry barriers. Particularly important are analyses of indirect network effects and the conditions under which they might increase the attractiveness and feasibility of foreclosure strategies.

Mergers and Killer Acquisitions

Competition policy practitioners actively debate how to review horizontal and vertical acquisitions of nascent firms by incumbents in tech markets or when digital platforms are involved. Such reviews may need to account for complementarities among firms’ data portfolios, service offerings, and customer bases.

Experts who understand tech firms’ competitive strategies, when and how data may generate market power, and applications of big data methods may play an increasing role in these cases.

Collusion and Algorithms

Online sellers and platforms are likely to employ pricing algorithms to reduce transaction costs and improve the customer experience. However, observers have suggested that, by making competitors’ prices easier to monitor, pricing algorithms may also reduce firms’ costs of enforcing potential collusive agreements or facilitate tacit collusion. Relatedly, observers have expressed concerns that an algorithm concurrently employed by multiple competing firms might reduce competition by maximizing group profits, in an inadvertent hub-and-spoke-style setup. Analyses for such cases may require expertise in self-learning algorithms, artificial intelligence, and screening methods.

Exclusionary Practices Due to Data Ownership or Use of Algorithms


Data and the use of algorithms have raised a number of questions in claims regarding exclusionary practices.

  • When would the market data owned by a firm provide an advantage that prevents entrants from competing?
  • Can a vertically integrated platform use its resources and algorithms to benefit its own products and exclude competitors?
  • What does the design of a platform’s search algorithms imply for the competition among platform participants?

A combination of both industry and antitrust expertise may be necessary to answer these questions.

Price Discrimination via Algorithms

A firm’s ability to simultaneously charge different prices to different customers for the same product is not new. Yet the advent of extensive data collection and machine learning allows firms to target pricing to specific customer groups or even individuals in more sophisticated ways than ever before. Whether this leads to anticompetitive price discrimination or discrimination against protected classes is an empirical question that may require both an understanding of pricing algorithms as well as expertise in detecting discrimination through rigorous empirical analysis.