TY - JOUR
T1 - Computational socioeconomics
AU - Gao, Jian
AU - Zhang, Yi Cheng
AU - Zhou, Tao
N1 - Funding Information:
The authors would like to acknowledge Yong-Yeol Ahn, César A. Hidalgo, Manuel Sebastian Mariani, Luciano Pietronero, Didier Sornette for their valuable comments and suggestions. This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 61433014 , 61603074 , 61673086 and 61703074 ) and the Science Promotion Programme of UESTC, China (No. Y03111023901014006 ). The authors acknowledge the support of the Swiss National Science Foundation, Switzerland (Grant No. 182498 ) during this collaboration. J.G. acknowledges the China Scholarship Council for a scholarship (No. 201606070051 ) and the Collective Learning Group at the MIT Media Lab for hosting.
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/7/10
Y1 - 2019/7/10
N2 - Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies.
AB - Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies.
KW - Complex networks
KW - Data mining
KW - Economic development
KW - Machine learning
KW - Socio-economic systems
KW - Socioeconomic status
UR - http://www.scopus.com/inward/record.url?scp=85067090365&partnerID=8YFLogxK
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U2 - 10.1016/j.physrep.2019.05.002
DO - 10.1016/j.physrep.2019.05.002
M3 - Review article
AN - SCOPUS:85067090365
SN - 0370-1573
VL - 817
SP - 1
EP - 104
JO - Physics Reports
JF - Physics Reports
ER -
