Platform design when sellers use pricing algorithms

Justin Pappas Johnson (), Andrew Rhodes and Matthijs Wildenbeest
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Justin Pappas Johnson: CORNELL UNIVERSITY PORTLAND USA - Partenaires IRSTEA - IRSTEA - Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture
Andrew Rhodes: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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Abstract: 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.

Date: 2023-09
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Citations: View citations in EconPapers (11)

Published in Econometrica, 2023, 91 (5), pp.1841-1879. ⟨10.3982/ECTA19978⟩

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Working Paper: Platform Design When Sellers Use Pricing Algorithms (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04226232

DOI: 10.3982/ECTA19978

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