Behavioral Foundations of Nested Stochastic Choice and Nested Logit

TL;DR

This paper introduces Nested Stochastic Choice (NSC), a behavioral model that generalizes nested logit by weakening the IIA assumption, and provides an axiomatic characterization and a data-driven method to identify nest structures.

Contribution

It offers the first behavioral axiomatic foundation for nested logit and introduces NSC, a non-parametric model that captures similarity effects in discrete choice.

Findings

NSC characterized by a single axiom weakening IIA

A practical algorithm to identify nest structures from data

Analysis of limitations in cross-nested logit generalizations

Abstract

We provide the first behavioral characterization of nested logit, a foundational and widely applied discrete choice model, through the introduction of a non-parametric version of nested logit that we call Nested Stochastic Choice (NSC). NSC is characterized by a single axiom that weakens Independence of Irrelevant Alternatives based on revealed similarity to allow for the similarity effect. Nested logit is characterized by an additional menu-independence axiom. Our axiomatic characterization leads to a practical, data-driven algorithm that identifies the true nest structure from choice data. We also discuss limitations of generalizing nested logit by studying the testable implications of cross-nested logit.