Publications

Network effects on information acquisition by DeGroot Updaters, Economic Theory, 2024

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.

Working papers

Feed for good? On the effects of personalization algorithms in social media platforms (with M. Lleonart-Anguix)

This paper builds a theoretical model of communication and learning on a social media platform, and describes the algorithm an engagement-maximizing platform implements in equilibrium. Such algorithm excessively exploits similarity, locking users in echo chambers. Moreover, learning vanishes as platform size grows large. As this is far from ideal, we explore alternatives. The reverse-chronological algorithm the DSA mandated to reincorporate turns out to be not good enough, so we build the "breaking echo chambers" algorithm, a modification of the platform-optimal algorithm that improves learning by promoting opposite thoughts. Additionally, we seek a natural implementation path for the utilitarian optimal algorithm. This is why we advocate for horizontal interoperability, which interoperability compels platforms to compete based on algorithms. In the absence of platform-specific network effects that entrench users within dominant platforms, the retention of user bases hinges on implementing algorithms that outperform those of competitors.

Work in progress

Based on the papers you liked: designing a rating system for strategic users (with J. Gambato)

Draft available upon request

Rating systems allow streaming platforms to leverage users' experience to signal quality of the third-party products they host. We study the interaction between strategic rating by users, granularity of the rating technology, and streaming platform size. Users rate products to be grouped together and to receive high quality recommendations, an effort that is more effective the larger the platform and the more granular the rating system are. Users become more demanding as the selection of potentially available content grows. The platform's need to generate value for users and remunerate sellers upfront leads to a trade-off: a platform with limited reach prefers more granular systems to employ users' ratings efficiently; a large one prefers a less informative and less taxing system to increase engagement. If the platform is large enough to affect competition intensity on the outside market, she has an incentive to limit access to sellers to minimize operational costs.

Hosting the influencer: How influencers and their platform coexist as (digital) advertisers (with P. Dall'Ara)