Using both economic theory and Artificial 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 (a common reinforcement-learning technique from the computer-science literature) to devise pricing strategies in a setting with repeated interactions, and consider the effect of steering policies 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 even when algorithmic collusion might otherwise emerge but that achieving these gains may require more than the simplest steering policies when algorithms value the future highly. We also find that policies that raise consumer surplus can raise the profits of the platform, depending on the platform’s revenue model. Finally, we document several learning challenges faced by the algorithms.
Justin Pappas Johnson, Andrew Rhodes, and Matthij Wildenbeest, “Platform Design when Sellers Use Pricing Algorithms”, TSE Working Paper, n. 20-1146, September 2020.
TSE Working Paper, n. 20-1146, September 2020