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| Terms |
Definitions |
| Parameter |
A quantity related to one or more variables in such a way that it remains constant for any specified
set of values of the variable or variables. |
| Pedigree |
Pedigree conveys an evaluative account of the production process of information (e.g. a number) on a quantity or phenomenon, and indicates different aspects of the underpinning of the numbers and scientific status of the knowledge used. Pedigree is expressed by means of a set of pedigree criteria to assess these different aspects. Examples of such criteria are empirical basis or degree of validation. These criteria are in fact yardsticks for strength. Many of these criteria are hard to measure in an objective way. Assessment of pedigree involves qualitative expert judgement. To minimise arbitrariness and subjectivity in measuring strength a pedigree matrix is used to code qualitative expert judgements for each criterion into a discrete numeral scale from 0 (weak) to 4 (strong) with linguistic descriptions (modes) of each level on the scale. Note that these linguistic descriptions are mainly meant to provide guidance in attributing scores to each of the criteria. It is not possible to capture all aspects that an expert may consider in scoring a pedigree in a single phrase. Therefore a pedigree matrix should be applied with some flexibility and creativity. Examples of pedigree matrices can be found in the Pedigree matrices section of the website www.nusap.net |
| Pitfall |
A pitfall is a characteristic error that commonly occurs in assessing
a problem. Such errors are typically associated with a lack of
knowledge or experience, and thus may be reduced by experience, by
consultation with others, or by following procedures designed to
highlight and avoid pitfalls. In particularly complex problems we
sometimes say that pitfalls are "dense", meaning that there are an
unusual variety and number of pitfalls. See Ravetz (1971). |
| Post-Normal Science |
Post-Normal Science is the methodology that is appropriate when "facts are uncertain, values in dispute,
stakes high and decisions urgent". It is appropriate when either "systems uncertainties" or "decision stakes"
are high. A tutorial is available on the website www.nusap.net |
| Practically immeasurable |
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| Precautionary principle |
The principle is roughly that "when an activity raises threats of harm
to human health or the environment, precautionary measures should be
taken even if some cause and effect relationships are not fully
established scientifically" (Wingspread conference, Wisconsin, 1998).
Note that this would apply to most environmental assessments since
cause-effect statements can rarely be fully established on any issue.
If the burden of proof were set such that one must demonstrate a
completely unequivocal cause-effect relationship before taking action,
then it would not be possible to take action on any meaningful
environmental issue. The precautionary principle thus relates to the
setting of burden of proof. |
| PRIMA approach |
Acronym for Pluralistic fRamework of Integrated uncertainty Management
and risk Analysis (Van Asselt, 2000). The guiding principle is that uncertainty legitimates different perspectives and that as a consequence uncertainty management should consider different perspectives.
Central to the PRIMA approach is the issue of disentangling controversies on complex issues in terms of salient uncertainties. The salient uncertainties are then 'coulored' according to various perspectives.
Starting from these perspective-based interpretations, various legitimate and consistent narratives are developed to serve as a basis for integrated analysis of autonomous and policy-driven developments
in terms of risk. |
| Probabilistic |
Based on the notion of probabilities. |
| Probability density function (PDF) |
The probability density function of a continuous random variable represents the probability that an infinitely small variable interval will fall at a given value.
The probability density function can be integrated to obtain the probability that the random variable takes a value
in a given interval. |
| Problem structuring |
An approach to analysis and decision making which assumes that participants do not have
clarity on their ends and means, and provides appropriate conceptual structures. It is a part of "soft systems
methodology". |
| Process error |
Process error arises from the fact that a model is by definition a simplification of the real system
represented by the model. Examples of such simplifications are the use of constant values for entities
that are functions in reality, or focusing on key processes that affect the modelled variables by
omitting processes that play a minor role or are considered not significant. |
| Proprietary uncertainty |
One of the seven types of uncertainty distinguished by De Marchi et al. in their checklist for characterizing uncertainty in
environmental emergencies:
institutional, legal, moral, proprietary, scientific, situational, and societal uncertainty. Proprietary uncertainty occurs due to the fact that information and
knowledge about an issue are not uniformly shared among all those who
could potentially use it. That is, some people or groups have
information that others don't and may assert ownership or control over
it. "Proprietary uncertainty becomes most salient when it is
necessary to reconcile the general needs for safety, health, and
environment protection with more sectorial needs pertaining, for
instance, to industrial production and process, or to licensing and
control procedures" (De Marchi, 1994). De Marchi notes that 'whistle
blowing' is another source of proprietary uncertainty in that there is
a need for protection of those who act in sharing information for the
public good. Proprietary uncertainty would typically be high when
knowledge plays a key role in assessment, but is not widely shared
among participants. An example of such would be
the case of external safety of military nuclear production
facilities. |
| Proxy |
Sometimes it is not possible to represent directly the quantity or phenomenon we are interested in by
a parameter so some form of proxy measure is used. A proxy can be better or worse depending on how
closely it is related to the actual quantity we intend to represent. Think of first order approximations,
over-simplifications, idealisations, gaps in aggregation levels, differences in definitions etc.. |
| Pseudo-imprecision |
Pseudo-imprecision occurs when results have been expressed
so vaguely that they are effectively immune from refutation
and criticism. |
| Pseudo-precision |
Pseudo-precision is false precision that occurs when the
precision associated with the representation of a number or
finding grossly exceeds the precision that is warranted by
closer inspection of the underlying uncertainties. |
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