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R Soc Open Sci. 2018 Jun 20;5(6):172096. doi: 10.1098/rsos.172096. eCollection 2018 Jun.

Computational modelling for decision-making: where, why, what, who and how.

Author information

1
School of Computing Science, University of Glasgow, Glasgow, UK.
2
The Royal Society, London, UK.
3
Improbable, London, UK.
4
Arup, London, UK.
5
MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, UK.
6
Willis Towers Watson, London, UK.
7
Centre for Policy Modelling, Manchester Metropolitan University, Manchester, UK.
8
Centre for Research in Social Simulation, University of Surrey, Guildford, UK.
9
McLaren Applied Technologies, Woking, UK.
10
National Cyber Security Centre, UK.
11
Henley Business School, University of Reading, Reading, UK.
12
Consultant, UK.
13
School of Informatics, University of Edinburgh, Edinburgh, UK.
14
Volterra Partners, London, UK.
15
Department of Aeronautics, Imperial College London, London, UK.
16
UK Research and Innovation, London, UK.
17
The Alan Turing Institute, London, UK.

Abstract

In order to deal with an increasingly complex world, we need ever more sophisticated computational models that can help us make decisions wisely and understand the potential consequences of choices. But creating a model requires far more than just raw data and technical skills: it requires a close collaboration between model commissioners, developers, users and reviewers. Good modelling requires its users and commissioners to understand more about the whole process, including the different kinds of purpose a model can have and the different technical bases. This paper offers a guide to the process of commissioning, developing and deploying models across a wide range of domains from public policy to science and engineering. It provides two checklists to help potential modellers, commissioners and users ensure they have considered the most significant factors that will determine success. We conclude there is a need to reinforce modelling as a discipline, so that misconstruction is less likely; to increase understanding of modelling in all domains, so that the misuse of models is reduced; and to bring commissioners closer to modelling, so that the results are more useful.

KEYWORDS:

communication; complexity; data; decision-making; modelling; uncertainty

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