Job market paper
Feed for good? On the effects of personalization algorithms in social platforms, with M. Lleonart
In this paper, a social media platform governs the exchange of information among users with preferences for sincerity and conformity by providing personalized feeds. We show that the pursuit of engagement maximization results in the proliferation of echo chambers. A monopolistic platform implements an algorithm that disregards social learning and provides feeds that primarily consist of content from like-minded individuals. We study the consequences on learning and welfare resulting from transitioning to this algorithm from the previously employed chronological feed. While users' experience improves under the platform's optimal algorithm, social learning is worsened. Indeed, learning vanishes in large populations. However, the platform could create value by using its privileged information to design an algorithm that balances learning and engagement, maximizing users' welfare. We discuss interoperability as a possible regulatory solution that would eliminate entry barriers in platform competition caused by network effects, thereby inducing competing platforms to adopt the socially optimal algorithm.
Network effects on information acquisition by DeGroot updaters (R&R at Economic Theory)
In today’s world, social networks have a significant impact on information processes, shaping individuals’ beliefs and influencing their decisions. This paper proposes a model to understand how boundedly rational (DeGroot) individuals behave when seeking information to make decisions in situations where both social communication and private learning take place. The model assumes that information is a local public good, and individuals must decide how much effort to invest in costly information sources to improve their knowledge of the state of the world. Depending on the network structure and agents’ positions, some individuals will invest in private learning, while others will free-ride on the social supply of information. The model shows that multiple equilibria can arise, and uniqueness is controlled by the lowest eigenvalue of a matrix determined by the network. The lowest eigenvalue roughly captures how two-sided a network is. Two-sided networks feature multiple equilibria. Under a utilitarian perspective, agents would be more informed than they are in equilibrium. Social welfare would be improved if influential agents increased their information acquisition levels.
Work in progress
The economics of likes: welfare effects of binary ratings on two-sided platforms
Draft available upon request
This paper explores the impact of a binary rating system, known as the liking technology, on a two-sided digital platform. An intermediary leverages feedback about the retailer’s products to make recommendations to the user. We analyze the welfare consequences when the user sets a constant liking threshold, the retailer determines product quality, and the platform sets prices. The introduction of the feedback technology results in positive network effects, which increase the user’s expected utility. However, the platform captures all the surplus by raising the registration fee. If network effects persist in future periods but prices cannot be adjusted, the retailer will internalize the surplus by decreasing product quality and lowering production costs.