Designing Graph Convolutional Neural Networks for Discrete Choice with Network Effects

• URL: http://arxiv.org/abs/2503.09786v1
• Date: Wed, 12 Mar 2025 19:38:47 GMT
• Title: Designing Graph Convolutional Neural Networks for Discrete Choice with Network Effects
• Authors: Daniel F. Villarraga, Ricardo A. Daziano,
• Abstract summary: We propose a novel graph convolutional neural network architecture to model network effects in discrete choices.<n>Our architecture achieves higher predictive performance than standard discrete choice models while retaining the interpretability necessary for inference.<n>We compare our architecture to those derived from traditional discrete choice and general-purpose deep learning models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a novel model architecture that incorporates network effects into discrete choice problems, achieving higher predictive performance than standard discrete choice models while offering greater interpretability than general-purpose flexible model classes. Econometric discrete choice models aid in studying individual decision-making, where agents select the option with the highest reward from a discrete set of alternatives. Intuitively, the utility an individual derives from a particular choice depends on their personal preferences and characteristics, the attributes of the alternative, and the value their peers assign to that alternative or their previous choices. However, most applications ignore peer influence, and models that do consider peer or network effects often lack the flexibility and predictive performance of recently developed approaches to discrete choice, such as deep learning. We propose a novel graph convolutional neural network architecture to model network effects in discrete choices, achieving higher predictive performance than standard discrete choice models while retaining the interpretability necessary for inference--a quality often lacking in general-purpose deep learning architectures. We evaluate our architecture using revealed commuting choice data, extended with travel times and trip costs for each travel mode for work-related trips in New York City, as well as 2016 U.S. election data aggregated by county, to test its performance on datasets with highly imbalanced classes. Given the interpretability of our models, we can estimate relevant economic metrics, such as the value of travel time savings in New York City. Finally, we compare the predictive performance and behavioral insights from our architecture to those derived from traditional discrete choice and general-purpose deep learning models.

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