# Discrete-Choice Model with Generalized Additive Utility Network

arXiv: https://arxiv.org/abs/2309.16970 · HTML: https://mlsanity.com/paper/2309.16970/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 29 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [InterpretML: A Unified Framework for Machine Learning Interpretability](https://mlsanity.com/paper/1909.09223/) (2019)
- [GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions](https://mlsanity.com/paper/2003.07132/) (2020)
- [Choice modelling in the age of machine learning -- discussion paper](https://mlsanity.com/paper/2101.11948/) (2021)

## Built on by (most cited first, 1 of 1)

- [An analysis of machine learning approaches for enhancing decision-making in complex discrete choice tasks](https://mlsanity.com/paper/2607.28854/) (2026)

## Abstract (arXiv)

Discrete-choice models are a powerful framework for analyzing decision-making behavior to provide valuable insights for policymakers and businesses. Multinomial logit models (MNLs) with linear utility functions have been used in practice because they are ease to use and interpretable. Recently, MNLs with neural networks (e.g., ASU-DNN) have been developed, and they have achieved higher prediction accuracy in behavior choice than classical MNLs. However, these models lack interpretability owing to complex structures. We developed utility functions with a novel neural-network architecture based on generalized additive models, named generalized additive utility network ( GAUNet), for discrete-choice models. We evaluated the performance of the MNL with GAUNet using the trip survey data collected in Tokyo. Our models were comparable to ASU-DNN in accuracy and exhibited improved interpretability compared to previous models.