Choice modelling in the age of machine learning - Discussion paper

• Published in: Journal of choice modelling Vol. 42; p. 100340
• Main Authors: van Cranenburgh, Sander, Wang, Shenhao, Vij, Akshay, Pereira, Francisco, Walker, Joan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2022
• Subjects: Choice modelling; Literature overview; Machine learning; Research agenda; Research agenda; Choice modelling; Literature overview; Machine learning
• ISSN: 1755-5345; 1755-5345
• DOI: 10.1016/j.jocm.2021.100340

Since its inception, the choice modelling field has been dominated by theory-driven modelling approaches. Machine learning offers an alternative data-driven approach for modelling choice behaviour and is increasingly drawing interest in our field. Cross-pollination of machine learning models, techniques and practices could help overcome problems and limitations encountered in the current theory-driven modelling paradigm, such as subjective labour-intensive search processes for model selection, and the inability to work with text and image data. However, despite the potential benefits of using the advances of machine learning to improve choice modelling practices, the choice modelling field has been hesitant to embrace machine learning. This discussion paper aims to consolidate knowledge on the use of machine learning models, techniques and practices for choice modelling, and discuss their potential. Thereby, we hope not only to make the case that further integration of machine learning in choice modelling is beneficial, but also to further facilitate it. To this end, we clarify the similarities and differences between the two modelling paradigms; we review the use of machine learning for choice modelling; and we explore areas of opportunities for embracing machine learning models and techniques to improve our practices. To conclude this discussion paper, we put forward a set of research questions which must be addressed to better understand if and how machine learning can benefit choice modelling. •Clarifies the similarities and differences between theory and data-driven paradigms.•Reviews the use of machine learning for choice modelling.•Explores opportunities for embracing machine learning to benefit choice modelling.•Puts forward research agenda.