# Peer Effects in Random Consideration Sets

## TL;DR

This paper introduces a dynamic model of discrete choice incorporating peer effects into consideration sets, enabling the recovery of preferences, attention, and network structure without restrictive assumptions.

## Contribution

It develops a nonparametric identification framework for peer effects in consideration sets and proposes a maximum-likelihood estimator validated through simulations and an experimental dataset.

## Key findings

- Successful recovery of preference rankings and attention mechanisms
- Estimator performs well in simulation studies
- Application to experimental data reveals peer influence on attention

## Abstract

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.