TY - GEN
T1 - Analyzing Vaccination Priority Judgments for 132 Occupations UsingWord Vector Models
AU - Ueshima, Atsushi
AU - Takikawa, Hiroki
N1 - Funding Information:
This work was funded by JSPS KAKENHI grants (nos. JP20H01563 and JP21J00403).
Publisher Copyright:
© 2021 ACM.
PY - 2021/12/14
Y1 - 2021/12/14
N2 - 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.
AB - 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.
KW - Distributive Justice
KW - Judgment and Decision Making
KW - Meanings
KW - Scare Resource Allocation
KW - Word Embedding
UR - http://www.scopus.com/inward/record.url?scp=85128545812&partnerID=8YFLogxK
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U2 - 10.1145/3498851.3498933
DO - 10.1145/3498851.3498933
M3 - Conference contribution
AN - SCOPUS:85128545812
T3 - ACM International Conference Proceeding Series
SP - 76
EP - 82
BT - Proceedings of 2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops and Special Sessions, WI-IAT 2021
A2 - Gao, Xiaoying
A2 - Huang, Guangyan
A2 - Cao, Jie
A2 - Cao, Jian
A2 - Deng, Ke
PB - Association for Computing Machinery
T2 - 2021 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2021
Y2 - 14 December 2021 through 17 December 2021
ER -
