Increasingly, online merchants, together with brick-and-mortar sellers and data intermediaries, are knitting together mass data collection, the interconnective power of the “Internet of Things” and automated algorithmic pricing and selling with their existing retailing and supply-chain businesses. The result of this coordination is that traditional sales functions such as competitive intelligence gathering and pricing are being delegated to software “robo-sellers.” In a recent paper I study the implications of this shift away from humans to robo-sellers for antitrust law.
Blackletter antitrust law conditions illegality on an anticompetitive “agreement.” To find an “agreement,” courts, government enforcers, and practitioners tend to focus on finding “intent,” efforts to sow fear and distrust, and a “meeting of the minds.” These totemic inquiries derive from a more than a century-old embedded assumption that antitrust regulates sales by human actors.
I point out that, as sales are increasingly generated and implemented by machines, such standard antitrust inquiries will become less effective.
Robo-sellers will function differently and will likely not create the same kinds of evidence that these inquiries rely on. For example, robo-sellers will not need to create an internal paper- or email-trail of communication between sales and marketing employees evidencing an anticompetitive intent. Furthermore, robo-sellers will not be deterred by the possibility of individual criminal punishment – a tool that the DOJ uses to inhibit price-fixing. The outcome may be anticompetitive, but the human element showing intent will have vanished.
Second, robo-sellers will exacerbate an existing gap in the Sherman Act. Oligopolists that achieve price coordination interdependently, without explicit communication, generally escape antitrust enforcement, even when their actions yield supracompetitive (“above market”) pricing that harms consumers. This antitrust dilemma in dealing with parallel behavior by oligopolists will widen: robo-sellers possess certain traits that will probably make them better than humans at achieving supracompetitive pricing without the need for express communication and collusion. For example, the ability to gather and process massive amounts of data will reduce the probability that coordinated pricing would break down due to error or mistake in assessing market conditions.
What can be done about the anticompetitive effects of roboselling? I assess several possible solutions, but find that they will be quite difficult to reconcile with current antitrust law. I conclude that, at least as a feasible second-best result, the FTC should incorporate an evolving approach to robo-sellers. Indeed, the FTC’s ongoing regulatory program has already begun to target the competitive and consumer protection aspects of consumer data collection by sellers. For example, the FTC has already begun to consider the effects of mass data collection and algorithmic processing on consumers from the perspective of disclosure and discrimination (both price and social); efficiencies should exist in broadening the inquiry to include effects on price coordination and cartel behavior.