Using both economic theory and Artficial Intelligence (AI) pricing algorithms, we investigate the ability of a platform to design its marketplace to promote competition, improve consumer surplus, and even raise its own profits. We allow sellers to use Q-learning algorithms (commonly used in computer science) to devise pricing strategies in a setting with repeated interactions, and consider the effect of platform rules that reward firms that cut prices with additional exposure to consumers. Overall, the evidence from our experiments suggests that platform design decisions can meaningfully benefit consumers, but that achieving these gains may require demand-steering policies that condition on past behavior and treat sellers in a non-neutral fashion. These more sophisticated policies disrupt the ability of algorithms to rotate demand and split industry profits, leading to very low prices. This is consistent with our theoretical results, which show that a platform can undermine collusion even when firms become infinitely patient.
Justin Pappas Johnson, Andrew Rhodes, and Matthijs Wildenbeest, “Platform Design when Sellers Use Pricing Algorithms”, TSE Working Paper, n. 20-1146, September 2020, revised July 2021.
TSE Working Paper, n. 20-1146, September 2020, revised July 2021