Michal Gal, University of Haifa
Recent studies have proven that pricing algorithms can autonomously learn to coordinate prices, and set them at supra-competitive levels. The growing use of such algorithms mandates the creation of solutions that limit the negative welfare effects of algorithmic coordination. Unfortunately, to date, no good means exist to limit such conduct. While this challenge has recently prompted scholars from around the world propose different solutions, many suggestions are inefficient or impractical, and some might even strengthen coordination. Michal Gal (University of Haifa) suggests four (partial) solutions. The advantages and risks of each solution are discussed. As antitrust agencies around the world are just starting to experiment with different ways to limit algorithmic coordination, there is no better time to explore how best to achieve this important task.
Watch the seminar here.