Researcher reasoning meets computational capacity: Machine learning for social science

Ian Lundberg, Jennie E. Brand and Nanum Jeon
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Jennie E. Brand: UCLA

No s5zc8, SocArXiv from Center for Open Science

Abstract: Computational power and digital data have created new opportunities to explore and understand the social world. A special synergy is possible when social scientists combine human attention to certain aspects of the problem with the power of algorithms to automate other aspects of the problem. We review selected exemplary applications where machine learning amplifies researcher coding, summarizes complex data, relaxes statistical assumptions, and targets researcher attention. We then seek to reduce perceived barriers to machine learning by summarizing several fundamental building blocks and their grounding in classical statistics. We present a few guiding principles and promising approaches where we see particular potential for machine learning to transform social science inquiry. We conclude that machine learning tools are accessible, worthy of attention, and ready to yield new discoveries.

Date: 2022-05-23
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:s5zc8

DOI: 10.31219/osf.io/s5zc8

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