Competition, Alignment, and Equilibria in Digital Marketplaces

TL;DR

This paper models a duopoly digital marketplace with competing bandit algorithms, revealing that market competition often leads to suboptimal user utility due to complex data and algorithm interactions.

Contribution

It introduces a theoretical duopoly model with bandit algorithms, analyzing how competition and data sharing influence market equilibria and user utility.

Findings

Market competition does not always align outcomes with user utility.

Data sharing impacts the nature and degree of market misalignment.

Market outcomes can be suboptimal even with shared data repositories.

Abstract

Competition between traditional platforms is known to improve user utility by aligning the platform's actions with user preferences. But to what extent is alignment exhibited in data-driven marketplaces? To study this question from a theoretical perspective, we introduce a duopoly market where platform actions are bandit algorithms and the two platforms compete for user participation. A salient feature of this market is that the quality of recommendations depends on both the bandit algorithm and the amount of data provided by interactions from users. This interdependency between the algorithm performance and the actions of users complicates the structure of market equilibria and their quality in terms of user utility. Our main finding is that competition in this market does not perfectly align market outcomes with user utility. Interestingly, market outcomes exhibit misalignment not only when the platforms have separate data repositories, but also when the platforms have a shared data repository. Nonetheless, the data sharing assumptions impact what mechanism drives misalignment and also affect the specific form of misalignment (e.g. the quality of the best-case and worst-case market outcomes). More broadly, our work illustrates that competition in digital marketplaces has subtle consequences for user utility that merit further investigation.