Welcome to NUSAP net Glossary
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|Model-fix errors are those errors that arise from the introduction of non-existent phenomena in the
model. These phenomena are introduced in the model for a variety of reasons. They can be included
to make the model computable with today's computer technology, or to allow simplification, or to
allow modelling at a higher aggregation level, or to bridge the mismatch between model behaviour
and observation and or expectation. An example of the latter is the flux adjustment in many coupled
Atmosphere Ocean General Circulation Models used for climate projection.
The effect of such model fixes on the reliability of the model outcome will be bigger if the simulated
state of the system is further removed from the (range of) state(s) to which the model was calibrated.
It is useful to distinguish between (A) model fixes to account for well understood limitations of a model
and (B) model fixes or to account for a mismatch between model and observation that is not understood.
|Monte Carlo Simulation
|Monte Carlo Simulation is a statistical technique for stochastic model-calculations and analysis of error propagation in calculations.
It's purpose is to trace out the structure of the distributions of model output. In it's simplest form this distribution is mapped by calculating the
deterministic results (realizations) for a large number of random draws from the individual distribution functions
of input data and parameters of the model. To reduce the required number of model runs needed to get
sufficient information about the distribution in the outcome (mainly to save computation time), advanced
sampling methods have been designed such as Latin Hyper Cube sampling. The latter makes use of
stratification in the sampling of individual parameters and pre-existing information
about correlations between input variables.
|One of the seven types of uncertainty distinguished by De Marchi et al. in their checklist for characterizing uncertainty in
institutional, legal, moral, proprietary, scientific, situational, and societal uncertainty. Moral uncertainty stems from the underlying moral issues related to
action and inaction in any given case. De Marchi notes that,
though similar to legal responsibility, moral guilt may occur absent
legal responsibility when negative consequences might have been
limited by the dissemination of prior information or more effective
management for example. "Moral uncertainty is linked to the ethical
tradition of a given country be it or not enacted in legislation
(juridical and societal norms, shared moral values, mores), as well as
the psychological characteristics of persons in charge, their social
status and professional roles" (De Marchi, 1994). Moral uncertainty
would typically be high when moral and ethical dimensions of an issue
are central and participants have a range of understandings of the
moral imperatives at stake.
|Motivational bias occurs when people have an incentive to reach a certain conclusion or see things a certain way.
It is a pitfall in expert elicitation. Reasons for occurrence of motivational bias include:
a) a person may want to influence a decision to go a certain way; b) the person may perceive that he will be evaluated based
on the outcome and might tend to be conservative in his estimates; c) the person may want to suppress uncertainty that he
actually believes is present in order to appear knowledgeable or authoritative; and d) the expert has taken a strong stand in the past
and does not want to appear to contradict himself by producing a distribution that lends credence to alternative views.
|Multi-criteria decision analysis
|A method of formalising issues for decision, using both "hard" and "soft"
indicators, not intended to yield an optimum solution but rather to clarify positions and coalitions.
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