4 th ANZ SRA Conference Uncertainty analysis workshop Keith R Hayes CSIRO Division of Mathematical...
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- Slide 1
- 4 th ANZ SRA Conference Uncertainty analysis workshop Keith R
Hayes CSIRO Division of Mathematical and Information Sciences 28 th
September 2009, Wellington
- Slide 2
- Overview Part I : Introduction and linguistic uncertainty
uncertainty and its many sources identifying and treating
linguistic uncertainty issues for qualitative risk assessment
models and quantitative risk assessment Part II: Uncertainty
analysis methods methods for representing variability methods for
treating epistemic uncertainty pros and cons of different
approaches Part III: Model structure uncertainty qualitative and
quantitative approaches
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- Acknowledgements People who have helped: Simon Barry -
statistics and general mentoring Scott Ferson - R functions for
pba, helping me out of tight spots Mark Burgman case study material
and elicitation Petra Kuhnert elicitation and pooling discussions
Greg Hood R programming tips Funding that has helped: attendance
and research partially funded by the Australian Centre of
Excellence for Risk Assessment (ACERA)
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- Part I: What is uncertainty?
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- Why worry about uncertainty? Risk and uncertainty are
intimately linked Risk occurs because the past and present can be
uncertain, and the future is uncertain Reasons why you may want to
address uncertainty perform an honest risk assessment (Burgman,
2005) ensure that the wheat remain separated from the chaff
separate knowledge gaps from variability predict, measure, learn
transparency Reasons why you may not want to address uncertainty
takes more time and resources paralysis through analysis results
may span decision criteria transparency
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- What is uncertainty? Some definitions: a degree of ignorance
(Beven, 2009), a state of incomplete knowledge (Cullen and Frey,
1999) insufficient information (Murray, 2002) a departure from the
unattainable state of complete determinism (Walker et al., 2003).
Large number of taxonomies and classification schemes but
basically: linguistic uncertainty, epistemic uncertainty
variability
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- Terminology!
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- How do we represent uncertainty? Using language highly certain,
low uncertainty Numerically probability imprecise probability
Dempster-Shafer belief functions possibility measures ranking
functions plausibility measures In practice probability far and
away the most popular
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- Linguistic uncertainty
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- Ambiguity arises when words have more than one meaning and it
is not clear which one is meant Context dependence caused by a
failure to specify the context in which a term is to be understood:
large scale escape Underspecificity occurs when there is unwanted
generality: in a small percentage (generally