Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models

• Main Authors: Bierlaire, M. (Author), Ortelli, N. (Author), Pereira, F.C (Author), Rodrigues, F. (Author)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2022
• Subjects: automatic relevance determination; Automatic relevance determination; automatic utility specification; Automatic utility specification; Bayesian; Bayesian networks; Discrete choice; Discrete choice models; Doubly stochastic; doubly stochastic variational inference; Doubly stochastic variational inference; Function specification; Inference engines; Large dataset; Specifications; Stochastic models; Stochastic systems; Utility functions; Variational inference
• Online Access: View Fulltext in Publisher

 Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying the utility function specifications that best model and explain the observed choices can be a very challenging and time-consuming task. This paper seeks to help modellers by leveraging the Bayesian framework and the concept of automatic relevance determination (ARD), in order to automatically determine an optimal utility function specification from an exponentially large set of possible specifications in a purely data-driven manner. Based on recent advances in approximate Bayesian inference, a doubly stochastic variational inference is developed, which allows the proposed MNL-ARD model to scale to very large and high-dimensional datasets. Using semi-artificial choice data, the proposed approach is shown to be able to accurately recover the true utility function specifications that govern the observed choices. Moreover, when applied to real choice data, MNL-ARD is able discover high quality specifications that can outperform previous ones from the literature according to multiple criteria, thereby demonstrating its practical applicability. © 2000-2011 IEEE.