Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models
This work addresses the time-consuming problem of utility function specification for modellers in discrete choice analysis, offering a data-driven solution that is incremental but practical.
The paper tackles the challenge of specifying utility functions in discrete choice models by introducing a Bayesian automatic relevance determination approach, which accurately recovers true specifications in semi-artificial data and outperforms existing methods on real data according to multiple criteria.
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 DCM-ARD model to scale to very large and high-dimensional datasets. Using semi-artificial choice data, the proposed approach is shown to very accurately recover the true utility function specifications that govern the observed choices. Moreover, when applied to real choice data, DCM-ARD is shown to be able discover high quality specifications that can outperform previous ones from the literature according to multiple criteria, thereby demonstrating its practical applicability.