Random Attention Span

TL;DR

This paper introduces a novel random attention span model (RAS) that uses stopping times to analyze decision-making under limited attention, allowing for preference identification without menu variation and testing with experimental data.

Contribution

The paper develops the RAS model, providing a new approach to identify preferences under limited attention using time variation and revealed preference theory.

Findings

Model aligns with observed choice data

Preference distribution estimated successfully

Supports validity of the RAS approach

Abstract

In this paper, I introduce a random attention span model (RAS) which uses stopping time to identify decision-makers' behavior under limited attention. Unlike many limited attention models, the RAS identifies preferences using time variation without any need for menu variation. In addition, the RAS allows the consideration set to be correlated with the preference. I also use the revealed preference theory that provides testable implications for observable choice probabilities. Then, I test the model and estimate the preference distribution using data from M-Turk experiments on choice behaviors that involve lotteries; there is general alignment with the distribution results from logit attention model.