Computational Social Science for public policy

Margetts, Helen and Dorobantu, Cosmina (2023) Computational Social Science for public policy. In: Bertoni, Eleonora, Fontana, Matteo, Gabrielli, Lorenzo, Signorelli, Serena and Vespe, Michele, (eds.) Handbook of Computational Social Science for Policy. Springer, 3 - 18. ISBN 9783031166235

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Abstract

Computational Social Science (CSS), which brings together the power of computational methods and the analytical rigour of the social sciences, has the potential to revolutionise policymaking. This growing field of research can help governments take advantage of large-scale data on human behaviour and provide policymakers with insights into where policy interventions are needed, which interventions are most likely to be effective, and how to avoid unintended consequences. In this chapter, we show how Computational Social Science can improve policymaking by detecting, measuring, predicting, explaining, and simulating human behaviour. We argue that the improvements that CSS can bring to government are conditional on making ethical considerations an integral part of the process of scientific discovery. CSS has an opportunity to reveal bias and inequalities in public administration and a responsibility to tackle them by taking advantage of research advancements in ethics and responsible innovation. Finally, we identify the primary factors that prevented Computational Social Science from realising its full potential during the Covid-19 pandemic and posit that overcoming challenges linked to limited data flows, siloed models, and rigid organisational structures within government can usher in a new era of policymaking.

Item Type:	Book Section
Additional Information:	© The Author(s) 2023
Divisions:	LSE
Subjects:	H Social Sciences > H Social Sciences (General) Q Science > QA Mathematics > QA76 Computer software
Date Deposited:	20 Oct 2025 15:00
Last Modified:	23 Oct 2025 10:21
URI:	http://eprints.lse.ac.uk/id/eprint/129867

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