Evaluation of Calibration Variables for
Optimising Expert Judgements in Risk Assessments
Delft
University of Technology has under contract to the EC and in cooperation with
other EU institutes, completed a study with the United States Nuclear Regulatory
Commission (USNRC) using formal expert judgement methods for retrieving
uncertainty distributions over the major parameters in the dispersion and
deposition module of accident consequence assessment codes (ACA) for nuclear
risk assessments. In the study formal expert judgement methods were applied.
In
applying these methods, expert assessments need be aggregated to retrieve one
combined uncertainty distribution over each assessed variable of interest. In
these studies, two methods of combining expert assessments were applied: equal
and performance based weighting schemes. The merits and disadvantages of
different weighting schemes remain a subject of ongoing scientific discussion
and research. Increasingly the merits of performance based weighting are being
recognized where appropriate seed variables can be developed for their
application.
Seed variables are variables which originate from
experimental evidence and of which the true values are unknown to the experts,
but are known to the analysts performing the expert judgement studies. The seed
variables serve as a basis for calibrating the experts! performance on the
variables of interest. In the equal weighting scheme, the experts are assigned
equal weights for each assessment. In the performance based weighting scheme
experts are weighted according to their performance on the seed variables.
Because
the choice of weighting schemes is likely to be subject of continuous discussion
and debate and in some cases may become contentious, there was a need in
parallel to undertake a more formal analysis of the merits of different schemes
so that the optimum choice of weighting scheme can be made in each expert
judgement exercise.
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