Artificial Intelligence, Big Data, and Competition with Algorithms
Saturday, Jan. 4, 2020 2:30 PM - 4:30 PM (PDT)
- Chair: Jay Pil Choi, Michigan State University
Statistical Discrimination in Ratings-Guided Markets
AbstractWe study statistical discrimination of individuals based on payoff-irrelevant social identities in markets where ratings/recommendations facilitate social learning among users. Despite the potential promise and guarantee for the ratings/recommendation algorithms to be fair and free of human bias and prejudice, we identify possible vulnerability of the ratings-based social learning to discriminatory inferences on social groups. In our model, users’ equilibrium attention decision may lead data to be sampled differentially across different groups so that differential inferences on individuals may emerge based on their group identities. We explore policy implications in terms of regulating trading relationships as well as algorithm design [to be added].
Competition Law and Pricing Algorithms
Data and Competition
AbstractThe question of data has been at the center of recent debates around competition policy in the digital era. Concerns in this area are wide-ranging, and encompass privacy, collusion, barriers to entry, exploitative practices, and data-driven mergers.
Data can serve several purposes: for instance it can be used to improve algorithms, to target advertising, or to offer personalized discounts to consumers. While this heterogeneity of uses for data has sparked a large literature in economics, the multiplicity of models makes it difficult to draw general conclusions about the competitive effects of data.
In this paper we introduce data into a competition-in-utility framework. The three key features of data are that (i) it allows to generate more revenue for a given level of utility, (ii) it is a byproduct of firms' economic activity, and (iii) it is a club good (non-rival and excludable).
We provide a sufficient condition for data to be pro-competitive, and apply it to several environments illustrating the variety of uses for data. We then use the framework to study market dynamics, data-driven mergers and privacy policies.
- C8 - Data Collection and Data Estimation Methodology; Computer Programs
- D2 - Production and Organizations