  We develop a dynamic model of discrete choice that incorporates peer effects
into random consideration sets. We characterize the equilibrium behavior and
study the empirical content of the model. In our setup, changes in the choices
of friends affect the distribution of the consideration sets. We exploit this
variation to recover the ranking of preferences, attention mechanisms, and
network connections. These nonparametric identification results allow
unrestricted heterogeneity across people and do not rely on the variation of
either covariates or the set of available options. Our methodology leads to a
maximum-likelihood estimator that performs well in simulations. We apply our
results to an experimental dataset that has been designed to study the visual
focus of attention.
