Assisted specification of discrete choice models. (June 2021) - Ortelli, Nicola; Hillel, Tim; Pereira, Francisco C.; de Lapparent, Matthieu; Bierlaire, Michel
Abstract: Determining appropriate utility specifications for discrete choice models is time-consuming and prone to errors. With the availability of larger and larger datasets, as the number of possible specifications exponentially grows with the number of variables under consideration, the analysts need to spend increasing amounts of time on searching for good models through trial-and-error, while expert knowledge is required to ensure these models are sound. This paper proposes an algorithm that aims at assisting modelers in their search. Our approach translates the task into a multi-objective combinatorial optimization problem and makes use of a variant of the variable neighborhood search algorithm to generate sets of promising model specifications. We apply the algorithm both to semi-synthetic data and to real mode choice datasets as a proof of concept. The results demonstrate its ability to provide relevant insights in reasonable amounts of time so as to effectively assist the modeler in developing interpretable and powerful models. Highlights: Specification of discrete choice models as a combinatorial optimization problem. Multi-objective algorithm that assists analysts in developing utility specifications. Our algorithm explores the trade-off between parsimony and goodness of fit. The Pareto front provides a broad understanding of the specification problem. Our algorithm provides a set of promising models in a reasonable amount of time.