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Tool Catalogue for Uncertainty Assessment
Introduction
1. Sensitivity analysis
2. Error propagation equation ("TIER 1")
3. Monte Carlo analysis ("TIER 2")
Description
Goals and use
Sorts and locations of uncertainty addressed
Required resources
Strengths and limitations
Guidance on application
Pitfalls
References
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 Monte Carlo simulation

  • Provides comprehensive insight in how specified uncertainty in inputs propagates through a model.
  • Forces analysts to explicitly consider uncertainty and interdependencies among different inputs.
  • Is capable to cope with any conceivable shape of PDF and can account for correlations.
  • Can be used in 2-dimensional mode to separately assess variability and epistemological uncertainty.

Typical weaknesses of Monte Carlo simulation

  • Monte Carlo assessment is limited to those uncertainties that can be quantified and expressed as probabilities.
  • One may not have any reasonable basis on which to ascribe a parameterised probability distribution to parameters
  • May take large run-time for computational intensive models. This can partly be remedied by using more efficient sampling techniques (e.g. Latin Hypercube Sampling).

·  The interpretation of a probability distribution of the model output by decision makers is not always straightforward; there is no single rule arising out of such a distribution that can guide decision-makers concerning the acceptable balance between for instance expected return and the variance of that return.