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Nature
  • Perspective
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Integrating explanation and prediction in computational social science

Naturevolume 595, pages181–188 (2021)Cite this article

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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.

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Author information

Author notes

  1. These authors contributed equally: Jake M. Hofman, Duncan J. Watts

Authors and Affiliations

  1. Microsoft Research, New York, NY, USA

    Jake M. Hofman

  2. Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA

    Duncan J. Watts

  3. The Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA, USA

    Duncan J. Watts

  4. Operations, Information, and Decisions Department, University of Pennsylvania, Philadelphia, PA, USA

    Duncan J. Watts

  5. Graduate School of Business, Stanford University, Stanford, CA, USA

    Susan Athey

  6. Department of Sociology, Princeton University, Princeton, NJ, USA

    Filiz Garip & Matthew J. Salganik

  7. Department of Psychology, Princeton University, Princeton, NJ, USA

    Thomas L. Griffiths

  8. Department of Computer Science, Princeton University, Princeton, NJ, USA

    Thomas L. Griffiths

  9. Department of Computer Science, Cornell University, Ithaca, NY, USA

    Jon Kleinberg

  10. Department of Information Science, Cornell University, Ithaca, NY, USA

    Jon Kleinberg

  11. Oxford Internet Institute, University of Oxford, Oxford, UK

    Helen Margetts

  12. Public Policy Programme, The Alan Turing Institute, London, UK

    Helen Margetts

  13. Booth School of Business, University of Chicago, Chicago, IL, USA

    Sendhil Mullainathan

  14. Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia

    Simine Vazire

  15. Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA

    Alessandro Vespignani

  16. Department of Psychology, University of Texas at Austin, Austin, TX, USA

    Tal Yarkoni

Authors
  1. Jake M. Hofman
  2. Duncan J. Watts
  3. Susan Athey
  4. Filiz Garip
  5. Thomas L. Griffiths
  6. Jon Kleinberg
  7. Helen Margetts
  8. Sendhil Mullainathan
  9. Matthew J. Salganik
  10. Simine Vazire
  11. Alessandro Vespignani
  12. Tal Yarkoni

Contributions

J.M.H. and D.J.W. conceptualized and helped to write and prepare the manuscript. They contributed equally to these efforts. All authors were involved in and discussed the structure of the manuscript at various stages of its development.

Corresponding authors

Correspondence toJake M. Hofman orDuncan J. Watts.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review informationNature thanks Noortje Marres, Melanie Mitchell and Scott Page for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Hofman, J.M., Watts, D.J., Athey, S.et al. Integrating explanation and prediction in computational social science.Nature595, 181–188 (2021). https://doi.org/10.1038/s41586-021-03659-0

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