The DOI matches its Crossref record; 19 of the 20 references it cites were found in a registry or an index.
Prediction and explanation in social systems
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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“Besides, publication biases that have been documented in several scientific fields90,91 can also affect ML-based science.92,93”
From Leakage and the reproducibility crisis in machine-learning-based science (2023) · cites this paper as [92]
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Cites
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The link‐prediction problem for social networks
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Self-citationThe Boosting Approach to Machine Learning: An Overview
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Cited by
Sorted by citations
Predictive model assessment in PLS-SEM: guidelines for using PLSpredict
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019) · European Journal of Marketing · doi:10.1108/ejm-02-2019-0189 · Cited by 4,618
Leakage and the reproducibility crisis in machine-learning-based science
Kapoor, S., & Narayanan, A. (2023) · Patterns · doi:10.1016/j.patter.2023.100804 · Cited by 1,387
“Besides, publication biases that have been documented in several scientific fields(90,91) can also affect ML-based science.(92,93) [R2] Treat results from ML-based science as tentative.”
BackgroundA Survey on Neural Network Interpretability
Zhang, Y., Tino, P., Leonardis, A., & Tang, K. (2021) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2021.3100641 · Cited by 923
Improving PLS-SEM Use for Business Marketing Research
Guenther, P., Guenther, M., Ringle, C. M., Zaefarian, G., & Cartwright, S. (2023) · Industrial Marketing Management · doi:10.1016/j.indmarman.2023.03.010 · Cited by 800
Artificial intelligence and illusions of understanding in scientific research
Messeri, L., & Crockett, M. J. (2024) · Nature · doi:10.1038/s41586-024-07146-0 · Cited by 707
Metadata
- Authors
- Hofman, J. M., Sharma, A., & Watts, D. J.
- Affiliations
- Microsoft (United States) (US)
- Corresponding author
- Hofman, Jake M.
- Sharma, Amit
- Watts, Duncan J.
- Venue
- Science
- Published
- 2017-02-03
- Volume
- 355
- Issue
- 6324
- Pages
- 486-488
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- 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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