Data: The Fundamental Particle of Interaction

Most economic models of interaction assume that agents hold beliefs in the form of priors, or probability distributions over a state of the world, which guide their behavior. In this talk, Nicole Immorlica with Microsoft Research; considers a model in which beliefs are built off data or anecdotes that are drawn from a distribution parameterized by the state of the world and study how this impacts outcomes. Nicole will discuss a model where agents communicate by sharing anecdotes. This mode of communication results in higher noise and bias when agents have differing preferences, giving rise to informational homophily and polarization. The results have implications for content regulation in social networks. She will then discuss a model where a principal selectively discloses anecdotes to facilitate social learning and show that an appropriate information structure, chosen ex-ante, can incentivize exploration and thus avoid the herding problems common in such social learning settings. The results have implications for the selection of reviews in online recommendation systems. Based on joint work with Nika Haghtalab, Brendan Lucier, Jieming Mao, Markus Mobius, Divya Mohan, Alex Slivkins, and Steven Wu.