# Random Utility and Limited Consideration

## TL;DR

This paper develops a unified framework for modeling decision-making with limited consideration, demonstrating that traditional random utility models cannot explain observed behavior, while the logit attention model can, based on novel experimental data.

## Contribution

It introduces a theoretical and statistical framework that unifies models of random consideration and extends them to include preference heterogeneity.

## Key findings

- RUM cannot explain the observed population behavior.
- The logit attention model fits the data well.
- Experimental data shows variation in choice sets and attention frames.

## Abstract

The random utility model (RUM, McFadden and Richter, 1990) has been the standard tool to describe the behavior of a population of decision makers. RUM assumes that decision makers behave as if they maximize a rational preference over a choice set. This assumption may fail when consideration of all alternatives is costly. We provide a theoretical and statistical framework that unifies well-known models of random (limited) consideration and generalizes them to allow for preference heterogeneity. We apply this methodology in a novel stochastic choice dataset that we collected in a large-scale online experiment. Our dataset is unique since it exhibits both choice set and (attention) frame variation. We run a statistical survival race between competing models of random consideration and RUM. We find that RUM cannot explain the population behavior. In contrast, we cannot reject the hypothesis that decision makers behave according to the logit attention model (Brade and Rehbeck, 2016).