Competition, Alignment, and Equilibria in Digital Marketplaces


  • Meena Jagadeesan UC Berkeley
  • Michael I. Jordan UC Berkeley
  • Nika Haghtalab UC Berkeley



GTEP: Other Foundations of Game Theory & Economic Paradigms, GTEP: Auctions and Market-Based Systems, GTEP: Game Theory, ML: Online Learning & Bandits


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.




How to Cite

Jagadeesan, M., Jordan, M. I., & Haghtalab, N. (2023). Competition, Alignment, and Equilibria in Digital Marketplaces. Proceedings of the AAAI Conference on Artificial Intelligence, 37(5), 5689-5696.



AAAI Technical Track on Game Theory and Economic Paradigms