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 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.

本文认为，选择建模对所有社会科学都是一种有用的方法，而与此同时，诸如社会学和政治科学等学科可以为选择建模的未来发展做出重要贡献。到目前为止，选择建模主要应用于调查消费选择类型的学科，无论是市场营销以调查新产品的偏好，运输是分析模式选择，还是环境经济学来激发公共物品的偏好。但是，使用可以从互斥的替代方案中的个人选择中获得的信息，作为检验理论假设并产生对个人行为的见识的强大工具，在其他学科中也越来越受欢迎。例如在人民社区接受难民收容所，犯罪地点的选择或社交网络的发展。在社会科学中扩展选择模型的一个很好的出发点是许多学科共享的共同基础，这些基础是在社会科学的保护下收集的。研究传统和理论模型包括理性选择概念，选择模型可以与跨领域方法链接，包括基于代理的模型，网络分析和机器学习。同时，各学科可以在学习选择行为方面相互补充，因为它们可以贡献其他学科所不熟悉的概念和工具。最后，所有社会科学学科在因果分析，决策规则的异质性，联合决策或大数据等问题上都面临挑战。