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How to build models for government: criteria driving model acceptance in policymaking

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Listed:
  • Daniel Antony Kolkman

    (University of Surrey)

  • Paolo Campo

    (University of Surrey)

  • Tina Balke-Visser

    (University of Surrey)

  • Nigel Gilbert

    (University of Surrey)

Abstract

Models are used to inform policymaking and underpin large amounts of government expenditure. Several authors have observed a discrepancy between the actual and potential use of models in government. While there have been several studies investigating model acceptance in government, it remains unclear under what conditions models are accepted. In this paper, we address the question “What criteria affect model acceptance in policymaking?”, the answer to which will contribute to the wider understanding of model use in government. We employ a thematic coding approach to identify the acceptance criteria for the eight models in our sample. Subsequently, we compare our findings with existing literature and use qualitative comparative analysis to explore what configurations of the criteria are observed in instances of model acceptance. We conclude that model acceptance is affected by a combination of the model’s characteristics, the supporting infrastructure and organizational factors.

Suggested Citation

  • Daniel Antony Kolkman & Paolo Campo & Tina Balke-Visser & Nigel Gilbert, 2016. "How to build models for government: criteria driving model acceptance in policymaking," Policy Sciences, Springer;Society of Policy Sciences, vol. 49(4), pages 489-504, December.
    • Handle: RePEc:kap:policy:v:49:y:2016:i:4:d:10.1007_s11077-016-9250-4
    DOI: 10.1007/s11077-016-9250-4
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    References listed on IDEAS

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    Cited by:

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    2. Mitoko, Jeremiah, 2021. "Economics of Microcredit-From current crisis to new possibilities," MPRA Paper 108392, University Library of Munich, Germany.
    3. Darren Nel & Araz Taeihagh, 2024. "The soft underbelly of complexity science adoption in policymaking: towards addressing frequently overlooked non-technical challenges," Policy Sciences, Springer;Society of Policy Sciences, vol. 57(2), pages 403-436, June.
    4. 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).
    5. Harper, Alison & Mustafee, Navonil & Yearworth, Mike, 2021. "Facets of trust in simulation studies," European Journal of Operational Research, Elsevier, vol. 289(1), pages 197-213.
    6. Kolkman, Daan, 2020. "The usefulness of algorithmic models in policy making," SocArXiv hpma8, Center for Open Science.

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