A Random Attention Model

TL;DR

This paper introduces a flexible Random Attention Model (RAM) that deduces preferences from choices under random, limited attention, providing new theoretical insights and econometric tools for empirical analysis.

Contribution

The paper develops a nonparametric RAM framework with Monotonic Attention, offering novel revealed preference theory and econometric methods for preference identification.

Findings

Established testable implications for choice probabilities.

Developed econometric methods for preference estimation.

Provided software and simulation results for validation.

Abstract

This paper illustrates how one can deduce preference from observed choices when attention is not only limited but also random. In contrast to earlier approaches, we introduce a Random Attention Model (RAM) where we abstain from any particular attention formation, and instead consider a large class of nonparametric random attention rules. Our model imposes one intuitive condition, termed Monotonic Attention, which captures the idea that each consideration set competes for the decision-maker's attention. We then develop revealed preference theory within RAM and obtain precise testable implications for observable choice probabilities. Based on these theoretical findings, we propose econometric methods for identification, estimation, and inference of the decision maker's preferences. To illustrate the applicability of our results and their concrete empirical content in specific settings, we also develop revealed preference theory and accompanying econometric methods under additional nonparametric assumptions on the consideration set for binary choice problems. Finally, we provide general purpose software implementation of our estimation and inference results, and showcase their performance using simulations.