SERC Symposium: Speaker Bio and Abstract

Manish Raghavan, Assistant Professor of Information Technology, MIT Sloan & EECS

Abstract

The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization

Online platforms have a wealth of data, run countless experiments and use industrial-scale algorithms to optimize user experience. Despite this, many users seem to regret the time they spend on these platforms. One possible explanation is misaligned incentives: platforms are not optimizing for user happiness. We suggest the problem runs deeper, transcending the specific incentives of any particular platform, and instead stems from a mistaken revealed-preference assumption: To understand what users want, platforms look at what users do. Yet research has demonstrated, and personal experience affirms, that we often make choices in the moment that are inconsistent with what we actually want. In this work, we develop a model of media consumption where users have inconsistent preferences. We show how our model of users’ preference inconsistencies produces phenomena that are familiar from everyday experience, but difficult to capture in traditional user interaction models. By linking these effects to abstractions of platform design choices, our model thus creates a theoretical framework and vocabulary in which to explore interactions between design, behavioral science, and social media. Joint work with Jon Kleinberg and Sendhil Mullainathan.

Bio

Manish Raghavan is the Drew Houston (2005) Career Development Professor at the MIT Sloan School of Management and department of Electrical Engineering and Computer Science. Before that, he was a postdoctoral fellow at the Harvard Center for Research on Computation and Society (CRCS), working with Cynthia Dwork. He completed his PhD at the Computer Science department at Cornell University, advised by Jon Kleinberg. He serves on the International Advisory Board of Sai University in Chennai. His research focuses on the societal impacts of algorithmic decision making.