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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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From Leakage and the reproducibility crisis in machine-learning-based science (2023) · cites this paper as [92]
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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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