Integrating explanation and prediction in computational social science

Jake M Hofman^#¹, Duncan J Watts^#^{2

3

4}, Susan Athey⁵, Filiz Garip⁶, Thomas L Griffiths^{7

8}, Jon Kleinberg^{9

10}, Helen Margetts^{11

12}, Sendhil Mullainathan¹³, Matthew J Salganik⁶, Simine Vazire¹⁴, Alessandro Vespignani¹⁵, Tal Yarkoni¹⁶

Affiliations

¹ Microsoft Research, New York, NY, USA. jmh@microsoft.com.
² Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA. djwatts@seas.upenn.edu.
³ The Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA, USA. djwatts@seas.upenn.edu.
⁴ Operations, Information, and Decisions Department, University of Pennsylvania, Philadelphia, PA, USA. djwatts@seas.upenn.edu.
⁵ Graduate School of Business, Stanford University, Stanford, CA, USA.
⁶ Department of Sociology, Princeton University, Princeton, NJ, USA.
⁷ Department of Psychology, Princeton University, Princeton, NJ, USA.
⁸ Department of Computer Science, Princeton University, Princeton, NJ, USA.
⁹ Department of Computer Science, Cornell University, Ithaca, NY, USA.
¹⁰ Department of Information Science, Cornell University, Ithaca, NY, USA.
¹¹ Oxford Internet Institute, University of Oxford, Oxford, UK.
¹² Public Policy Programme, The Alan Turing Institute, London, UK.
¹³ Booth School of Business, University of Chicago, Chicago, IL, USA.
¹⁴ Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia.
¹⁵ Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
¹⁶ Department of Psychology, University of Texas at Austin, Austin, TX, USA.

^# Contributed equally.

PMID: 34194044
DOI: 10.1038/s41586-021-03659-0

Integrating explanation and prediction in computational social science

Jake M Hofman et al. Nature. 2021 Jul.

. 2021 Jul;595(7866):181-188.

doi: 10.1038/s41586-021-03659-0. Epub 2021 Jun 30.

Authors

Affiliations

¹ Microsoft Research, New York, NY, USA. jmh@microsoft.com.
² Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA. djwatts@seas.upenn.edu.
³ The Annenberg School of Communication, University of Pennsylvania, Philadelphia, PA, USA. djwatts@seas.upenn.edu.
⁴ Operations, Information, and Decisions Department, University of Pennsylvania, Philadelphia, PA, USA. djwatts@seas.upenn.edu.
⁵ Graduate School of Business, Stanford University, Stanford, CA, USA.
⁶ Department of Sociology, Princeton University, Princeton, NJ, USA.
⁷ Department of Psychology, Princeton University, Princeton, NJ, USA.
⁸ Department of Computer Science, Princeton University, Princeton, NJ, USA.
⁹ Department of Computer Science, Cornell University, Ithaca, NY, USA.
¹⁰ Department of Information Science, Cornell University, Ithaca, NY, USA.
¹¹ Oxford Internet Institute, University of Oxford, Oxford, UK.
¹² Public Policy Programme, The Alan Turing Institute, London, UK.
¹³ Booth School of Business, University of Chicago, Chicago, IL, USA.
¹⁴ Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia.
¹⁵ Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, USA.
¹⁶ Department of Psychology, University of Texas at Austin, Austin, TX, USA.

^# Contributed equally.

PMID: 34194044
DOI: 10.1038/s41586-021-03659-0

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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References

1. Watts, D. J. A twenty-first century science. Nature 445, 489 (2007). - PubMed
1. Lazer, D. et al. Computational social science. Science 323, 721–723 (2009). - PubMed - PMC
1. Salganik, M. J. Bit by Bit: Social Research in the Digital Age (Princeton Univ. Press, 2018).
1. Lazer, D. M. J. et al. Computational social science: obstacles and opportunities. Science 369, 1060–1062 (2020). - PubMed
1. Lazer, D. et al. Meaningful measures of human society in the twenty-first century. Nature https://doi.org/10.1038/s41586-021-03660-7 (2021).

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Integrating explanation and prediction in computational social science

Affiliations

Integrating explanation and prediction in computational social science

Authors

Affiliations

Abstract

References

MeSH terms

LinkOut - more resources

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