The Mixed Aggregate Preference Logit Model: A Machine Learning Approach to Modeling Unobserved Heterogeneity in Discrete Choice Analysis

Abstract

This paper introduces the Mixed Aggregate Preference Logit (MAPL, pronounced "maple'') model, a novel class of discrete choice models that leverages machine learning to model unobserved heterogeneity in discrete choice analysis. The traditional mixed logit model (also known as "random parameters logit'') parameterizes preference heterogeneity through assumptions about feature-specific heterogeneity distributions. These parameters are also typically assumed to be linearly added in a random utility (or random regret) model. MAPL models relax these assumptions by instead directly relating model inputs to parameters of alternative-specific distributions of aggregate preference heterogeneity, with no feature-level assumptions required. MAPL models eliminate the need to make any assumption about the functional form of the latent decision model, freeing modelers from potential misspecification errors. In a simulation experiment, we demonstrate that a single MAPL model specification is capable of correctly modeling multiple different data-generating processes with different forms of utility and heterogeneity specifications. MAPL models advance machine-learning-based choice models by accounting for unobserved heterogeneity. Further, MAPL models can be leveraged by traditional choice modelers as a diagnostic tool for identifying utility and heterogeneity misspecification.