Author
Listed:
- Jake M. Hofman
(Microsoft Research)
- Duncan J. Watts
(University of Pennsylvania
University of Pennsylvania
University of Pennsylvania)
- Susan Athey
(Stanford University)
- Filiz Garip
(Princeton University)
- Thomas L. Griffiths
(Princeton University
Princeton University)
- Jon Kleinberg
(Cornell University
Cornell University)
- Helen Margetts
(University of Oxford
The Alan Turing Institute)
- Sendhil Mullainathan
(University of Chicago)
- Matthew J. Salganik
(Princeton University)
- Simine Vazire
(University of Melbourne)
- Alessandro Vespignani
(Northeastern University)
- Tal Yarkoni
(University of Texas at Austin)
Abstract
Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyse them. It also represents a convergence of different fields with different ways of thinking about and doing science. The goal of this Perspective is to provide some clarity around how these approaches differ from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The first is a schema for thinking about research activities along two dimensions—the extent to which work is explanatory, focusing on identifying and estimating causal effects, and the degree of consideration given to testing predictions of outcomes—and how these two priorities can complement, rather than compete with, one another. Our second contribution is to advocate that computational social scientists devote more attention to combining prediction and explanation, which we call integrative modelling, and to outline some practical suggestions for realizing this goal.
Suggested Citation
Jake M. Hofman & Duncan J. Watts & Susan Athey & Filiz Garip & Thomas L. Griffiths & Jon Kleinberg & Helen Margetts & Sendhil Mullainathan & Matthew J. Salganik & Simine Vazire & Alessandro Vespignani, 2021.
"Integrating explanation and prediction in computational social science,"
Nature, Nature, vol. 595(7866), pages 181-188, July.
Handle:
RePEc:nat:nature:v:595:y:2021:i:7866:d:10.1038_s41586-021-03659-0
DOI: 10.1038/s41586-021-03659-0
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