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 model produces phenomena related to overconsumption that are familiar from everyday experience, but difficult to capture in traditional user interaction models. A key ingredient is a formulation for how platforms determine what to show users: they optimize over a large set of potential content (the content manifold) parametrized by underlying features of the content. We show how the relationship between engagement and utility depends on the structure of the content manifold, characterizing when engagement optimization leads to good utility outcomes. 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.

This is joint work with Sendhil Mullainathan and Manish Raghavan.