Gedetailleerde leidraad

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

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.3 Nature of uncertainty

This dimension expresses whether uncertainty is primarily a consequence of the incompleteness and fallibility of knowledge ( 'knowledge-related' or 'epistemic' uncertainty)or that it is primarily due to the intrinsic indeterminate and/or variable character of the system under study ( 'variability-related' or 'ontic' uncertainty). The first form of uncertainty can possibly, though not necessarily [4], be reduced by more measurements, better models and/or more knowledge; the second form of uncertainty is typically not reducible by more knowledge (e.g. inherent indeterminacy and or unpredictability; randomness, chaotic behavior [5]).

We notice that in many situations uncertainty manifests itself as a mix of both forms; not in all cases the delineation between 'epistemic' and 'ontic' can be made unequivocally. Moreover a combination of taste, tradition, specific problem features of interest and the current level of knowledge and ignorance with respect to the specific subject determines to a large part where the dividing line is drawn. In practice it is therefore the active choice of the researcher which often determines the distinction between epistemic and ontic, rather than that it is an innate and fundamental property of reality itself. Notice that this choice can be decisive for the outcomes and interpretations of the uncertainty assessment. Moreover using the distinction between 'epistemic' and 'ontic' uncertainty can render important information on the (im)possibility of reducing the uncertainties by, e.g., more research, better measurements, better models, or whether this is not possible. That is, although not being completely equivalent, this distinction reflects to a large extent the distinction between uncertainties which are 'reducible' and those which are 'not reducible' by means of further research.

[5] Although it is possible to know the characteristics of a system on a certain level of aggregation, e.g., knowing the probability distribution or the 'strange attractor', it is not always possible to predict the behavior or properties of individuals/elements which form part of the system on a lower level.