Algorithmic Predation and Exclusion

The debate about the implications of algorithms on antitrust 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 this 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 U.S. 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.