Econometrica

Journal Of The Econometric Society

An International Society for the Advancement of Economic
Theory in its Relation to Statistics and Mathematics

Edited by: Guido W. Imbens • Print ISSN: 0012-9682 • Online ISSN: 1468-0262

Econometrica: Sep, 2023, Volume 91, Issue 5

Platform Design When Sellers Use Pricing Algorithms

https://doi.org/10.3982/ECTA19978
p. 1841-1879

Justin P. Johnson, Andrew Rhodes, Matthijs Wildenbeest

We investigate the ability of a platform to design its marketplace to promote competition, improve consumer surplus, and increase its own payoff. We consider demand‐steering rules that reward firms that cut prices with additional exposure to consumers. We examine the impact of these rules both in theory and by using simulations with artificial intelligence pricing algorithms (specifically Q‐learning algorithms, which are commonly used in computer science). Our theoretical results indicate that these policies (which require little information to implement) can have strongly beneficial effects, even when sellers are infinitely patient and seek to collude. Similarly, our simulations suggest that platform design can benefit consumers and the platform, but that achieving these gains may require policies that condition on past behavior and treat sellers in a nonneutral fashion. These more sophisticated policies disrupt the ability of algorithms to rotate demand and split industry profits, leading to low prices.


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Supplemental Material

Supplement to "Platform Design When Sellers Use Pricing Algorithms"

Justin P. Johnson, Andrew Rhodes, and Matthijs Wildenbeest

This online appendix contains material not found within the manuscript.

Supplement to "Platform Design When Sellers Use Pricing Algorithms"

Justin P. Johnson, Andrew Rhodes, and Matthijs Wildenbeest

This zip file contains the replication files for the manuscript.