Cited by 429Philosophy of Biology and Scientific ExplanationSocial Science and Policy ResearchQualitative Comparative Analysis Research

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Prediction and explanation in social systems

Hofman, Jake M., Sharma, Amit, Watts, Duncan J.·2017·Science

DOI 10.1126/science.aal3856

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Abstract

Historically, social scientists have sought out explanations of human and social phenomena that provide interpretable causal mechanisms, while often ignoring their predictive accuracy. We argue that the increasingly computational nature of social science is beginning to reverse this traditional bias against prediction; however, it has also highlighted three important issues that require resolution. First, current practices for evaluating predictions must be better standardized. Second, theoretical limits to predictive accuracy in complex social systems must be better characterized, thereby setting expectations for what can be predicted or explained. Third, predictive accuracy and interpretability must be recognized as complements, not substitutes, when evaluating explanations. Resolving these three issues will lead to better, more replicable, and more useful social science.

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Estimating the reproducibility of psychological science
Open Science Collaboration (2015) · Science · doi:10.1126/science.aac4716 · Cited by 8,515
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Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
Breiman, L. (2001) · Statistical Science · doi:10.1214/ss/1009213726 · Cited by 4,340
Computational social science
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009) · Science · doi:10.1126/science.1167742 · Cited by 3,365
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Liben‐Nowell, D., & Kleinberg, J. M. (2007) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.20591 · Cited by 3,063
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Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006) · Science · doi:10.1126/science.1121066 · Cited by 2,231
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Schapire, R. E. (2003) · Lecture notes in statistics · doi:10.1007/978-0-387-21579-2_9 · Cited by 2,101
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Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011) · Proceedings of the fourth ACM international conference on Web search and data mining · doi:10.1145/1935826.1935845 · Cited by 1,689
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Authors: Hofman, J. M., Sharma, A., & Watts, D. J.
Affiliations: Microsoft (United States) (US)
Corresponding author: Hofman, Jake M.
Sharma, Amit
Watts, Duncan J.
ORCID: Hofman, Jake M.
Sharma, Amit
Watts, Duncan J.
Venue: Science
Publisher: American Association for the Advancement of Science (AAAS)
Published: 2017-02-03
Volume: 355
Issue: 6324
Pages: 486-488
ISSN: 0036-8075 (ISSN-L)
1095-9203
DOI: 10.1126/science.aal3856
OpenAlex: W2584419678
Cited by: 429
References: 20
Citations by year: 2026: 29 · 2025: 34 · 2024: 40 · 2023: 54 · 2022: 52 · 2021: 65 · 2020: 39 · 2019: 62 · 2018: 40 · 2017: 14
FWCI: 224.18
Type: journal article
Language: en
Open access: closed
Locations: Science (publishedVersion)
PubMed (publishedVersion)
Topics: Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
Social Sciences › General Social Sciences › Social Science and Policy Research
Social Sciences › Sociology and Political Science › Qualitative Comparative Analysis Research
Keywords: explanation, causal mechanisms, prediction accuracy, computational social science, complex social systems, interpretability, replicability

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Prediction and explanation in social systems

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