Computer Science > Machine Learning
[Submitted on 26 Jul 2022 (v1), last revised 19 Jul 2023 (this version, v2)]
Title:Representing Random Utility Choice Models with Neural Networks
View PDFAbstract:Motivated by the successes of deep learning, we propose a class of neural network-based discrete choice models, called RUMnets, inspired by the random utility maximization (RUM) framework. This model formulates the agents' random utility function using a sample average approximation. We show that RUMnets sharply approximate the class of RUM discrete choice models: any model derived from random utility maximization has choice probabilities that can be approximated arbitrarily closely by a RUMnet. Reciprocally, any RUMnet is consistent with the RUM principle. We derive an upper bound on the generalization error of RUMnets fitted on choice data, and gain theoretical insights on their ability to predict choices on new, unseen data depending on critical parameters of the dataset and architecture. By leveraging open-source libraries for neural networks, we find that RUMnets are competitive against several choice modeling and machine learning methods in terms of predictive accuracy on two real-world datasets.
Submission history
From: Antoine Desir [view email][v1] Tue, 26 Jul 2022 13:12:22 UTC (1,127 KB)
[v2] Wed, 19 Jul 2023 23:38:55 UTC (642 KB)
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