Computational Social Science Fusion Analytics: Combining Machine-Based Methods with Explanatory Empiricism

Date
2017-01-04
Authors
Kauffman, Robert
Kim, Kwansoo
Lee, Sang-Yong
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This article discusses the emergence of a computational social science analytics fusion as a mainstream scientific approach involving machine-based methods and explanatory empiricism as a basis for the discovery of new policy-related insights for business, consumer and social settings. It reflects the interdisciplinary background of the new approaches that the Hawaii International Conference on Systems Science has embraced over the years, and especially some of the recent development and shifts in the scientific study of technology-related phenomena. It also has evoked new forms of research inquiry, blended approaches to research methodology, and more pointed interest in the production of research results that have direct application in various industry contexts. We review background knowledge to showcase the methods shifts, and demonstrate the new forms of research, by showcasing contemporary applications that will be interesting to the audience on the occasion of the HICSS 50th anniversary. \
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Computational social science, explanatory empiricism, machine learning, mobile-phones, stock trading
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10 pages
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Proceedings of the 50th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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