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Computational Modelling of Public Policy: Reflections on Practice

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Abstract

Computational models are increasingly being used to assist in developing, implementing and evaluating public policy. This paper reports on the experience of the authors in designing and using computational models of public policy (‘policy models’, for short). The paper considers the role of computational models in policy making, and some of the challenges that need to be overcome if policy models are to make an effective contribution. It suggests that policy models can have an important place in the policy process because they could allow policy makers to experiment in a virtual world, and have many advantages compared with randomised control trials and policy pilots. The paper then summarises some general lessons that can be extracted from the authors’ experience with policy modelling. These general lessons include the observation that often the main benefit of designing and using a model is that it provides an understanding of the policy domain, rather than the numbers it generates; that care needs to be taken that models are designed at an appropriate level of abstraction; that although appropriate data for calibration and validation may sometimes be in short supply, modelling is often still valuable; that modelling collaboratively and involving a range of stakeholders from the outset increases the likelihood that the model will be used and will be fit for purpose; that attention needs to be paid to effective communication between modellers and stakeholders; and that modelling for public policy involves ethical issues that need careful consideration. The paper concludes that policy modelling will continue to grow in importance as a component of public policy making processes, but if its potential is to be fully realised, there will need to be a melding of the cultures of computational modelling and policy making.

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  • Nigel Gilbert & Petra Ahrweiler & Pete Barbrook-Johnson & Kavin Preethi Narasimhan & Helen Wilkinson, 2018. "Computational Modelling of Public Policy: Reflections on Practice," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(1), pages 1-14.
  • Handle: RePEc:jas:jasssj:2018-3-1
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    1. Petra Ahrweiler & Michel Schilperoord & Andreas Pyka & Nigel Gilbert, 2015. "Modelling Research Policy: Ex-Ante Evaluation of Complex Policy Instruments," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(4), pages 1-5.
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    2. Anja Bauer & Leo Capari & Daniela Fuchs & Titus Udrea, 2023. "Diversification, integration, and opening: developments in modelling for policy," Science and Public Policy, Oxford University Press, vol. 50(6), pages 977-987.
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    12. ederico Bianchi & Flaminio Squazzoni, 2022. "Can transparency undermine peer review? A simulation model of scientist behavior under open peer review [Reviewing Peer Review]," Science and Public Policy, Oxford University Press, vol. 49(5), pages 791-800.
    13. Süsser, Diana & Gaschnig, Hannes & Ceglarz, Andrzej & Stavrakas, Vassilis & Flamos, Alexandros & Lilliestam, Johan, 2022. "Better suited or just more complex? On the fit between user needs and modeller-driven improvements of energy system models," Energy, Elsevier, vol. 239(PB).
    14. Noeldeke, Beatrice & Winter, Etti & Ntawuhiganayo, Elisée Bahati, 2022. "Representing human decision-making in agent-based simulation models: Agroforestry adoption in rural Rwanda," Ecological Economics, Elsevier, vol. 200(C).
    15. Run Tang & Xin Guan & Junfan Zhu & Bo Liu & Zeyu Wang & Fanbao Xie, 2023. "Evaluation of Sustainable City and Old-Age Security Policy Intervention in China," Sustainability, MDPI, vol. 15(7), pages 1-15, April.
    16. Louise Freebairn & Jo-An Atkinson & Nathaniel D Osgood & Paul M Kelly & Geoff McDonnell & Lucie Rychetnik, 2019. "Turning conceptual systems maps into dynamic simulation models: An Australian case study for diabetes in pregnancy," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-27, June.
    17. Martin Klein & Ulrich J. Frey & Matthias Reeg, 2019. "Models Within Models – Agent-Based Modelling and Simulation in Energy Systems Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 22(4), pages 1-6.
    18. Bernardo Alves Furtado & Gustavo Onofre Andre~ao, 2022. "Machine Learning Simulates Agent-Based Model Towards Policy," Papers 2203.02576, arXiv.org, revised Nov 2022.

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