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0.4 Uncertainty Typology

A variety of different types of uncertainty has been defined and used in the literature and practice. For the purpose of this guidance, it is important to agree upon a standard nomenclature and classification of uncertainties. There is no one particular uncertainty classification or typology that is universally agreed to be 'best' for all purposes. Thus, we had to be pragmatic and sought to compile a synthesis typology that makes reasonable sense for the kinds of tasks carried out by PBL without claiming to be the only useful classification system. Use was made of an uncertainty typology recently proposed by Walker et al. 2003. Walker et al.'s typology classifies uncertainties according to three dimensions: their ‘location’ (where they occur), their ‘level’ ( where uncertainty manifests itself on the gradual spectrum between deterministic knowledge and total ignorance) and their 'nature' (whether uncertainty primarily stems from knowledge imperfection or is a direct consequence from inherent variability). Based on this typology, Walker et al. 2003, propose an uncertainty matrix as a heuristic for classifying and reporting the various dimensions of uncertainty, and to improve communication among analysts as well as between them and policymakers and stakeholders.

We have tuned the uncertainty matrix specifically for this guidance, and have explicitly extended it with two extra columns (dimensions) referring to ‘qualification of knowledge base’ and ‘value-ladenness of choices’, see appendix A. The former refers to the level of underpinning and backing of the information (e.g. data, theories, models, methods, argumentation etc.) involved in the assessment of the problem; it points at the methodological acceptability and the rigour and strength of the employed methods, knowledge and information, and thus it characterizes to a certain extent their (un)reliability. The latter category (value-ladenness of choices) refers to the presence of values and biases in the various choices involved e.g. choices concerning the way the scientific questions are framed, data are selected, interpreted and rejected, methodologies and models are devised and used, explanations and conclusions are formulated etc. These aspects have also been briefly mentioned in Walker et al. 2003 in relation to uncertainty.

The proposed uncertainty typology and uncertainty matrix provide a common language for viewing uncertainty in this guidance tool. They play an important role in e.g. the problem-framing section, and in the identification, prioritization and assessment of uncertainties, as well in their reporting. In turn, the uncertainty typology and the uncertainty matrix render useful information concerning which kinds of methods and tools can be appropriate to deal with the various kinds of uncertainties (see the Tool Catalogue for Uncertainty Assessment, van der Sluijs et al., 2003).