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.
翻译:众所周知,传统平台之间的竞争通过使平台行动与用户偏好相匹配,可以提高用户的效用。但是,在数据驱动市场中显示的一致程度如何?为了从理论角度研究这一问题,我们引入了双极市场,平台行动是强盗算法,两个平台竞争用户参与。这个市场的显著特点是,建议的质量既取决于强盗算法,也取决于用户互动提供的数据数量。算法绩效与用户行动之间的相互依赖性使市场平衡结构及其在用户效用方面的质量复杂化。我们的主要发现是,这一市场的竞争结果并不完全与用户效用相一致。有趣的是,市场结果显示出不协调,不仅当平台拥有单独的数据储存库,而且当平台拥有共享的数据储存库时也是如此。然而,数据共享假设影响着什么机制导致不匹配,也影响着特定形式的错配关系(例如,最佳和最坏的市场结果的质量)。更广泛地说,我们的工作表明,数字市场的竞争对用户效用的调查具有微妙的后果,因为用户的效用更有利于进一步的调查。