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Tool Catalogue for Uncertainty Assessment
Introduction
1. Sensitivity analysis
Description
Goals and use
Sorts and locations of uncertainty addressed
Required resources
Strengths and limitations
Guidance on application
Pitfalls
References
2. Error propagation equation ("TIER 1")
3. Monte Carlo analysis ("TIER 2")
4. NUSAP
5. Expert elicitation for uncertainty quantification
6. Scenario analysis
7. PRIMA: A framework for perspective-based uncertainty management
8. Checklist for model quality assistance
9. A method for critical review of assumptions in model-based assessments

Strengths and limitations

Typical strengths of Sensitivity Analysis are:

  • Gives insight in the potential influences of all sorts of changes in inputs
  • Helps discriminating across parameters according to importance for the accuracy of the outcome
  • Software for sensitivity analysis is freely available
    (e.g. SIMLAB: http://sensitivity-analysis.jrc.cec.eu.int/default2.asp?page=SIMLAB)
  • Easy to use 

Typical weaknesses of Sensitivity Analysis are:

  • Has a tendency to yield an overload of information.
  • Sensitivity analysis does not require one to assess how likely it is that specific values of the parameters will actually occur.
  • Sensitivity testing does not encourage the analyst to consider dependencies between parameters and probabilities that certain values will occur together.
  • (Morris:) interactions and non-linearity are hard to distinguish with the Morris method.

These weaknesses can be partly overcome by a skilled design of the SA experiments, taking into account dependencies and restrictions and by being creative in structuring, synthesizing and communicating the information captured in the large amount of numbers produced by the sensitivity analysis.