Algorithmic Predation and Exclusion

By Thomas K. Cheng (The University of Hong Kong) & Julian Nowag (Lund University)

The debate about the implications of algorithms on competition law enforcement has so far focused on multi-firm conduct in general and collusion in particular. The implications of algorithms on abuse of dominance have been largely neglected. This article seeks to fill the gap in the existing literature by exploring how the increasingly precise practice of individualized targeting by algorithms can facilitate the practice of a range of abuses of dominance, including predatory pricing, rebates, and tying and bundling. The ability to target disparate groups of consumers with different prices helps a predator to minimize the losses it sustains during predation and maximize its ability to recoup its losses. This changes how recoupment should be understood and ascertained and may even undermine the rationale for requiring a proof of likelihood of recoupment under US antitrust law. This increased ability to price discriminate also enhances a dominant firm’s ability to offer exclusionary rebates. Finally, algorithms allow dominant firms to target their tying and bundling practices to loyal customers, hence avoiding the risk of alienating marginal customers with an unwelcome tie. This renders tying and bundling more feasible and effective for dominant firms.

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