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Framework for uncertainty classification

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Uncertainty can be defined as any departure from the unachievable ideal of complete deterministic knowledge of the system. 

From the point of view of health impact assessment, uncertainty is best thought of as comprising two distinct properties: location and level.

Location of uncertainty refers to where uncertainty manifests itself within the system model that is used in the assessment.  The way location is described and classified depends both on the issue under investigation and the analytical approach that is used.  In the case of integrated environmental health impact assessment, however, two related dimensions need to be considered:

  • the analytical model - where uncertainties arise within the process from initial poblem definition to final comparison and ranking of the impacts;
  • the conceptual model - where uncertainties develop within the casual chain between source and impact.  

The former is described mainly in terms of what is referred to as inputs, parameters and outputs; the latter is characterised mainly by what is known as context and model structure uncertainty (i.e. what is included in the assessment and how these components are defined). 

In many cases, it may be appropriate to separate these, since they relate to different, and largely independent, aspects of the assessment, which pose different challenges for assessment design.  Uncertainties in the analytical model, for example, are best dealt with by improvements in the data and methods of analytical methods used for assessment.  Uncertainties in the conceptual model (e.g. in the choice of what to include or exclude from the assessment, or how the specific causal relationships are defined) imply the need to reconsider and revise the way in which the issue has been defined and framed.  

Level of uncertainty refers to the degree to which the object of study is uncertain, from the point of view of the decision maker.  The figure below provides a graphic illustration of levels of uncertainty, in which five different states can be recognised:

  1. Determinism - i.e. complete knowledge, with no uncertainty
  2. Statistical uncertainty - i.e. where likely consequences are known and their probabilities can be quantified
  3. Scenario uncertainty - i.e. where the likely consequences are known, but their probabilities cannot be quantified
  4. Recognised uncertainty - i.e. where even the likely consequences are not clear
  5. Total ignorance - i.e. where nothing is known on which to make judgements about what may happen.

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See also: 

Characterising uncertainty

References: 

Janssen, P.H.M., Petersen, A.C., van der Sluijs, J.P., Risbey, J.S. and Ravetz, J.R. 2005 A guidance for assessing and communicating uncertainties. Water Science and Technology 52 (6), 145-152.

Meyer, G., Folker, A.P., Jørgensen, R.B., Krayer von Krauss, M.P., Sandøe, P. and Tveit, G. 2005 The factualization of uncertainty: risk, politics, and genetically modified crops – a case of rape. Agriculture and Human Values, 22 (2), 235 – 242.

Walker, W., Harremoës, P., Rotmans, J., van der Sluijs, J., van Asselt, M.V.A., Janssen, P. and Krayer von Krauss, M.P. 2003 Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Journal of Integrated Assessment, 4 (1), 5-17.

Wynne, B. 1992 Uncertainty and environmental learning - reconceiving science and policy in the preventive paradigm. Global Environmental Change, 2 (2), 111-117.

Zwanenberg, P. and Millstone, E. 2001 Mad cow disease 1980s–2000: how reassurances undermined precaution. In: The precautionary principle in the 20th century. Late lessons from early warnings. (Harremoës, P., Gee, D., MacGarvin, M., Stirling, A., Keys, J., Wynne, B. and Vaz, S.G. eds.), London: Earthscan Publications.