Ueshima, A.(上島淳史), & Takikawa, H. (2021). 
Analyzing vaccination priority judgments for 132 occupations using word vector models. 
IEEE/WIC/ACM International Conference on Web Intelligence. 

Most human societies conduct a high degree of division of labor based on occupation. However, determining the occupational field that should be allocated a scarce resource such as vaccine is a topic of debate, especially considering the COVID-19 situation. Though it is crucial that we understand and anticipate people’s judgments on resource allocation prioritization, quantifying the concept of occupation is a difficult task. In this study, we investigated how well people’s judgments on vaccination prioritization for different occupations could be modeled by quantifying their knowledge representation of occupations as word vectors in a vector space. The results showed that the model that quantified occupations as word vectors indicated high out-of-sample prediction accuracy, enabling us to explore the psychological dimension underlying the participants’ judgments. These results indicated that using word vectors for modeling human judgments about everyday concepts allowed prediction of performance and understanding of judgment mechanisms.