Mapping potentials and challenges of choice modelling for social science research. (March 2021)

• Record Type: Journal Article
• Authors: Liebe, Ulf; Meyerhoff, Jürgen
• Is Part Of: Journal of choice modelling. Volume 38(2021)
• Publication Date: 2021-03
• Subjects: Causal analysis -- Context effects -- Decision rules -- Machine learning -- Network analysis -- Rational choice -- Social sciences; Decision making -- Periodicals; Social choice -- Periodicals; Decision making; Social choice; Periodicals; 302.13
• DOI: 10.1016/j.jocm.2021.100270
• Languages: English
• ISSNs: 1755-5345

Abstract

Abstract: This paper argues that choice modelling is a useful approach for all social sciences, while at the same time disciplines such as sociology and political science can contribute significantly to the future development of choice modelling. So far choice modelling has mainly been applied in disciplines that investigate types of consumption choices, be it marketing to investigate preferences for new products, transportation to analyse mode choices, or environmental economics to elicit preferences for public goods. However, using the information that can be gained from individual choices among mutually exclusive alternatives has gained increasing popularity in other disciplines as a powerful tool to test theoretical hypotheses and generate insights into individual behaviour. Examples are the acceptance of refugee shelters in peoples' neighbourhood, the choice of where to commit a crime or the evolution of social networks. A good point of departure for an expansion of choice modelling within the social sciences is the common foundation that many disciplines share that are gathered under the umbrella of social sciences. Research traditions and theoretical models include rational choice concepts, and choice modelling can be linked to cross-cutting methods, including agent-based models, network analysis, and machine learning. At the same time, disciplines can complement each other in studying choice behaviour, as they can contribute concepts and tools less familiar to the other disciplines. Finally, all social science disciplines face challenges when it comes to issues such as causal analysis, heterogeneity in decision rules, joint decision making, or big data. Choice modelling and a cross-disciplinary dialogue can contribute to meeting these challenges.