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Classifying and communicating uncertainties in model-based policy analysis

Kwakkel, Jan H., Walker, Warren E., Marchau, Vincent A.W.J.·2010·International Journal of Technology, Policy and Management

Abstract

Uncertainty is of paramount importance in modern day decision making. In response to this, it has been suggested that policy analysts have to be more careful in communicating the uncertainties that are inherent in policy advice. To support policy analysts in identifying uncertainties and communicating these uncertainties to decision makers, an uncertainty matrix was proposed by Walker et al. (2003), which synthesised various taxonomies, frameworks and typologies of uncertainties from different decision support fields. Since its publication, this framework has been applied to different domains. As a result, the framework has undergone changes resulting in a proliferation of uncertainty frameworks. This proliferation runs counter to the purpose of the original framework. This paper presents an extensive review of the literature that builds on Walker et al. (2003). In light of this, a synthesis is presented, which can be used to assess and communicate uncertainties in model-based policy analysis studies.

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Metadata

Authors
Kwakkel, J. H., Walker, W. E., & Marchau, V. A. W. J.
Venue
International Journal of Technology, Policy and Management
Published
2010
Volume
10
Issue
4
Pages
299
ISSN
  • 1468-4322 (ISSN-L)
  • 1741-5292
OpenAlex
W2064829610
Cited by
231
References
2
FWCI
11.34
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Classifying and communicating uncertainties in model-based policy analysis. https://app.cochat.ai/papers/10.1504/ijtpm.2010.036918
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Kwakkel, J. H., Walker, W. E., & Marchau, V. A. W. J. (2010). Classifying and communicating uncertainties in model-based policy analysis. International Journal of Technology, Policy and Management. https://doi.org/10.1504/ijtpm.2010.036918

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.