Publications

Network effects on information acquisition by DeGroot Updaters, Economic Theory 79, 201-234 (2025) 

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)

We study how engagement-maximizing personalization algorithms shape exposure and learning on a social media platform. Users observe private signals, post, and consume ranked feeds; utility reflects sincerity, conformity, and accuracy. Users are heterogeneous: rational users choose engagement, while naive users continue scrolling with a payoff-dependent probability. Truth-telling is an equilibrium—unique for rationals—so platforms influence behavior only through feed ranking. In general, more similar users are prioritized—especially in naive users’ feeds—producing echo-chamber exposure. At scale, learning stalls for naives. Algorithms such as reverse-chronological and balanced-insertion restore diversity, though they may underperform overall. Finally, personalization can cause network effects to fail for naive users.

Contestability and the Optimal Regulation of Social Media Platforms (with M. Banchio, F. Decarolis, C. Groh and R. Jiménez-Durán)

We study the optimal regulatory approach for social media markets within a model of asymmetric platform competition between an entrant and an incumbent platform.  Any platform has incentives to display harmful content to its users because this maximizes user engagement, thereby generating more revenue for the platform. Some users are naive: When choosing which platform to join, naive users neglect the adverse effects of being exposed to  harmful content. We show that user welfare must be strictly higher in any equilibrium in which all users join the incumbent than in any equilibrium in which some users join the entrant. If the share of naive users is high, reducing the incumbent's competitive advantage cannot benefit users. Reducing the share of naive users benefits users by reducing the harmful content platforms display, but may grant the incumbent a market share of one. The user-optimal outcome emerges if the incumbent has no competitive advantage and the share of naive users is small. 

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.

Harmful Content, Engagement, and Platform Contestability: An Experiment on Social Media Users (with M. Banchio, F. Decarolis, C. Groh and R. Jiménez-Durán )

The Cursed Equilibrium of Algorithmic Traumatization (with C. Firullo)

The Economics of Algorithmic Discovery

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