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Discussion Paper Details

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Title: Platform Design When Sellers Use Pricing Algorithms

Author(s): Justin Johnson, Andrew Rhodes and Matthijs Wildenbeest

Publication Date: November 2020

Keyword(s): Algorithms, artificial intelligence, Collusion and platform design

Programme Area(s): Industrial Organization

Abstract: 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 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 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.

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Bibliographic Reference

Johnson, J, Rhodes, A and Wildenbeest, M. 2020. 'Platform Design When Sellers Use Pricing Algorithms'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=15504