mainlogo

Gedetailleerde leidraad

PBL Guidance for Uncertainty Assessment and Communication
0 Introduction to the Detailed Guidance
1 Problem Framing and Context
2 Process Assessment
3 Environmental Assessment Methods
4 Uncertainty Identification and Prioritization
5 Uncertainty Analysis
6 Review and Evaluation
7 Reporting
References
Acknowledgments
A Appendix: Uncertainty Matrix
A.1 Uncertainty location
A.2 Uncertainty level
A.3 Nature of uncertainty
A.4 Qualification of the knowledge base
A.5 Value-ladenness of choices
A.6 Instructions for filling out the uncertainty matrix
A.7 Uncertainty Matrix
B Appendix: Glossary

A.2 Uncertainty level

This dimension expresses how a specific uncertainty source can be classified on a gradual scale running from 'knowing for certain' to 'no know'. Use is made of three distinct classes:

  • Statistical uncertainty: this concerns the uncertainties which can adequately be expressed in statistical terms, e.g. as a range with associated probability (examples are statistical expressions for measurement inaccuracies; uncertainties due to sampling effects, uncertainties in model-parameter estimates, etc.). In the natural sciences, scientists generally refers to this category if they speak of uncertainty, thereby often implicitly assuming that the involved model-relations offer adequate descriptions of the real system under study, and that the(calibration)-data employed are representative for the situation under study. However, when this is not the case, 'deeper' forms of uncertainty are at play, which can surpass the statistical uncertainty in size and seriousness and which require adequate attention.
  • Scenario uncertainty: this concerns uncertainties which can not be depicted adequately in terms of chances, probabilities, but which can only be specified in terms of (a range of) possible outcomes. For these uncertainties it is impossible to specify a degree of probability or belief, since the mechanisms which lead to the outcome are not sufficiently known. Scenario uncertainties are often construed in terms of 'what-if' statements.
  • Recognized ignorance: this concerns those uncertainties of which we realize - some way or another - that they are present, but of which we cannot establish any useful estimate, e.g. due to limits of predictability and knowledgeability ('chaos'), or due to unknown processes.

Continuing on the scale beyond recognized ignorance, we arrive in the area of complete ignorance ('unknown unknowns') of which we cannot yet speak and where we inevitably grope in the dark.

We should notice that the uncertainties which manifest themselves at a specific location (e.g. uncertainties on model relations) can appear in each of the above-mentioned guises: while some aspects can be adequately expressed in 'statistical terms', other aspects can only be expressed in terms of 'what-if' statements; moreover there are typically aspects judged relevant but about which we know that we are (still) largely 'ignorant'. Judging which aspects manifests themselves in what forms is often a subjective (and uncertain) matter.