Enhancing Discrete Choice Models with Representation Learning
This work addresses the issue of limited predictability and biased estimates in discrete choice models for fields like transportation or marketing, representing an incremental improvement by augmenting existing models with neural networks.
The paper tackles the problem of model misspecifications in discrete choice modeling by proposing a new approach that divides the utility specification into knowledge-driven and data-driven parts, using neural networks to learn non-linear representations, resulting in enhanced predictive performance and parameter estimation accuracy compared to traditional models like Multinomial Logit and Nested Logit.
In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specification into (i) a knowledge-driven part, and (ii) a data-driven one, which learns a new representation from available explanatory variables. Our formulation increases the predictive power of standard DCM without sacrificing their interpretability. We show the effectiveness of our formulation by augmenting the utility specification of the Multinomial Logit (MNL) and the Nested Logit (NL) models with a new non-linear representation arising from a Neural Network (NN), leading to new choice models referred to as the Learning Multinomial Logit (L-MNL) and Learning Nested Logit (L-NL) models. Using multiple publicly available datasets based on revealed and stated preferences, we show that our models outperform the traditional ones, both in terms of predictive performance and accuracy in parameter estimation. All source code of the models are shared to promote open science.