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 that incentives are misaligned: 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 foundational assumption. To understand what users want, platforms look at what users do. This is a kind of revealed-preference assumption that is ubiquitous in user models. Yet research has demonstrated, and personal experience affirms, that we often make choices in the moment that are inconsistent with what we actually want: we can choose mindlessly or myopically, behaviors that feel entirely familiar on online platforms.

In this work, we develop a model of media consumption where users have inconsistent preferences. We consider what happens when a platform that simply wants to maximize user utility is only able to observe behavioral data in the form of user engagement. Our framework is based on a stochastic model of user behavior, in which users are guided by two conflicting sets of preferences — one that operates impulsively in the moment, and the other of which makes plans over longer time-scales. By linking the behavior of this model to abstractions of platform design choices, we can develop a theoretical framework and vocabulary in which to explore interactions between design, behavioral science, and social media.

The talk is based on joint work with Sendhil Mullainathan and Manish Raghavan.

Bio:

Jon Kleinberg is the Tisch University Professor in the Departments of Computer Science and Information Science at Cornell University. His research focuses on the interaction of algorithms and networks, the roles they play in large-scale social and information systems, and their broader societal implications. He is a member of the National Academy of Sciences and National Academy of Engineering, and serves on the National AI Advisory Committee. He has received MacArthur, Packard, Simons, Sloan, and Vannevar Bush research fellowships, as well awards including the Harvey Prize, the Nevanlinna Prize, and the ACM Prize in Computing.