Mapping potentials and challenges of choice modelling for social science research

Abstract This paper argues that choice modelling could be a gainful approach for all social sciences, while at the same time disciplines such as sociology and political science could 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 hypothesis 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.

• Publication year

  2021

• Venue

  Journal of choice modelling

• Publication date

  2021-01-20

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1016/J.JOCM.2021.100270
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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