5th Online NETWORKS Seminar Talk

Abstract

Can price algorithms learn to collude instead of compete against each other, potentially leading to higher consumer prices and lower social welfare? The question is controversial among economists and competition policy regulators. One the one hand, concerns have been expressed that self-learning price algorithms do not only make it easier to form price cartels, but also that this can be achieved within the boundaries of current antitrust legislation – raising the question whether the existing competition law needs to be adjusted to mitigate undesired algorithmic collusion.

On the other hand, a number of economists believe that algorithms learning to collude is science fiction, except by using forms of signaling or communication that are already illegal, and argue that there is no need to change antitrust laws. Motivated by this discussion, I will present recent work-in-progress on learning supra-competitive prices in price and assortment games.

 

Joint work with Janusz Meylahn, Thomas Loots, and Ali Aouad.