This episode features the research team, from the University of Milan-Bicocca, who wrote the paper “Balancing Benefits and Risks: RL Approaches for Addiction-Aware Social Media Recommenders.” Their work represents a rigorous and nuanced attempt to mathematically model the tension at the heart of the social media business model: how to balance user engagement with long-term well-being.

ORIGINALLY PUBLISHED OCTOBER 2025

The first half of the conversation focuses on the paper itself—its core methodology, the design of the simulated user environment, and the way reinforcement learning algorithms can optimize for more than just click-through rates. We explore how the team modeled addiction, agency, and healthy decision-making, along with the findings from their simulations.

From there, the discussion zooms out. What are the current frontiers in recommender system research? How can we shift the incentives of tech companies to care about long-term user flourishing? And what kinds of policy or institutional frameworks might actually support healthier design choices in real-world platforms?

Our hope with this podcast was to have a discussion with people at the cutting edge of a research field. We hope you enjoy this discussion and let us know if you’d like more of this kind of perspective.

You can learn more about the team's current research efforts by checking out their Social Media Data Collection Project: https://biconnect.psicologia.unimib.i...