Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support

Walker, Warren E.; Harremoës, Poul; Rotmans, Jan; Sluijs, J.P. van der; Asselt, M.B.A. van; Janssen, Peter H.; Krauss, Martin Paul Krayer von

doi:10.1076/iaij.4.1.5.16466

Cited by 1,585 publications

(1,514 citation statements)

References 18 publications

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“…At the current stage uncertainty analysis is performed as a three-levelled general uncertainty analysis for the CI model (Gissi et al, 2017) adopted from the typology development by Walker et al, (2003). In future a similar uncertainty analysis needs to be considered for the other models in order to increase the credibility of the modelling approach for stakeholders involved in the planning process.…”

Section: Model Limitationsmentioning

confidence: 99%

Multi-objective spatial tools to inform maritime spatial planning in the Adriatic Sea

Depellegrin

Menegon

Farella

et al. 2017

Science of The Total Environment

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This research presents a set of multi-objective spatial tools for sea planning and environmental management in the Adriatic Sea Basin. The tools address four objectives: 1) assessment of cumulative impacts from anthropogenic sea uses on environmental components of marine areas; 2) analysis of sea use conflicts; 3) 3-D hydrodynamic modelling of nutrient dispersion (nitrogen and phosphorus) from riverine sources in the Adriatic Sea Basin and 4) marine ecosystem services capacity assessment from seabed habitats based on an ES matrix approach. Geospatial modelling results were illustrated and analysed for three biogeographic subdivisions, Northern-Central-Southern Adriatic Sea. The paper discusses model results for their spatial implications, relevance for sea planning, limitations and concludes with an outlook towards the need for more integrated, multi-functional tools development for sea planning.

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Section: Model Limitationsmentioning

confidence: 99%

Multi-objective spatial tools to inform maritime spatial planning in the Adriatic Sea

Depellegrin

Menegon

Farella

et al. 2017

Science of The Total Environment

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“…Uncertainty in assessment outcomes that are due to how the assessment goals are framed by participating entities prior to actual analysis falls within the context uncertainty category of Walker et al (2003). This is the fundamental origin of most of the disagreements in our outcomes because it leads to Fig.…”

Section: Discussionmentioning

confidence: 99%

Triangulation in climate change vulnerability assessment: examples from Colorado, USA

2017

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Climate change vulnerability assessment is a key first step for land managers attempting to address the potential impacts of future climatic conditions on important vegetation types. We compared outcomes of three vulnerability assessments focused on major vegetation types in Colorado. Assessments differed in methods and scale, but overlapped in both qualitative and quantitative techniques, and in vegetation types assessed. Agreement on vegetation type vulnerability was 47-50% between the regional scale southwestern Colorado assessment and either of two statewide assessments. Disagreements were due to regional sampling bias, qualitative vs. quantitative climate exposure analysis, or lack of information about the vegetation type in question. The two statewidescale assessments were in agreement on 75% of vegetation types assessed in both studies; differences were due to choice and interpretation of climate projection data. Sources of variation are categorized in relation to a taxonomy of uncertainty. We compare our iterative experience of climate change vulnerability assessment using methods adapted to the needs and means of various land management agencies to the technique of triangulation used to gradually home in on the location of an object. We clarify ways in which participant choices shape the end result and mitigate the common perception that climate science is too difficult for the lay person to use or understand.

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“…If u stands for combined uncertainty which is a resultant of many input quantities with different PDFs, selection of the k x factor becomes a significant mathematical problem associated with model uncertainty [9] in estimation of tests uncertainty. Various approaches to this issue may result in significant divergences in estimated uncertainty of the test result.…”

Section: Performance Evaluation Of the Construction Products As A Resmentioning

confidence: 99%

“…Presented by Walker et al [9] typology and terminology of harmonized uncertainty provide uncertainity identification by location, including inter alia parameter uncertainty (associated with the variability of input data) and model uncertainty (associated also, but not only with lack of sufficient understanding of the system). These two kind of uncertainty sources are presented in test results and in estimation of uncertainty of the test results (uncertainty of estimated uncertainty).…”

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confidence: 99%

Performance evaluation of the construction products as a research challenge. Small error – big difference in assessment?

Szewczak

Piekarczuk

2016

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. The paper refers to the importance of test results uncertainty in the assessment of the construction product performance. Uncertainty understood as dispersion attributed to any value which is result of the tests, calculations or other evaluations, occurs at every level of assessment (material, product, construction). Authors presented the various approaches to uncertainty estimation, especially in situation of small number of tests results which is characteristic for testing of construction products. Effects of uncertainty on final assessment of construction product was analyzed using the example of bearing capacity of thin-walled structure obtained by numerical calculation. Different values of material tests uncertainty resulting from different approaches to its assessment was taken into account. It was demonstrated that the difference in the results of strength tests of a material, which falls within the limits of uncertainty, may result in a very significant difference in the evaluation of a structure.Key words: uncertainty, numerical calculation, uncertainty influence on product assessment. Each value resulting from tests may only be considered in terms of probability. Unfortunately, in a small number of cases it is possible to express this probability using a probability density function (PDF) which is characterised by the expectation value and standard deviation (SD). In all tests which yield qualitative results (ordinal scale with two categories e.g. "fulfilled", "not fulfilled") or semi-quantitative results (ordinal scale with more than two categories), which, in the authors' opinion, are the majority of tests to which construction products are subjected, the determination of a PDF is an issue requiring extensive inter-laboratory tests performed on repeatable items [5][6][7]. Usually this is not possible for tests on construction products.Linking a value being measured with a PDF that is assigned to it is much easier for quantitative methods, for which the estimation of the uncertainty of a test result, allowing one to provide a PDF, is subject to numerous guidelines and instructions. The basic principles have been developed by the Joint Committee for Guides in Metrology [8], and are commonly applied by virtually all laboratories. However, as will be demonstrated for destructive (e.g. mechanical) testing, the estimation of uncertainty may be associated with numerous ambiguities.Presented by Walker et al. [9] typology and terminology of harmonized uncertainty provide uncertainity identification by location, including inter alia parameter uncertainty (associated with the variability of input data) and model uncertainty (associated also, but not only with lack of sufficient understanding of the system). These two kind of uncertainty sources are presented in test results and in estimation of uncertainty of the test results (uncertainty of estimated uncertainty). Variability relating to laboratory testing results has many components. Some of them, e.g. metrological parameters of testing equipment, ...

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Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support

Cited by 1,585 publications

References 18 publications

Multi-objective spatial tools to inform maritime spatial planning in the Adriatic Sea

Multi-objective spatial tools to inform maritime spatial planning in the Adriatic Sea

Triangulation in climate change vulnerability assessment: examples from Colorado, USA

Performance evaluation of the construction products as a research challenge. Small error – big difference in assessment?

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