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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

Sorts and locations of uncertainty addressed

Monte Carlo analysis typically addresses statistical uncertainty (stochastic inexactness) in inputs and parameters. Although it is rarely used this way, it is possible to use Monte Carlo analysis also for assessing model structure uncertainty, by introducing one or more "switch parameter" to switch between different model structures with probabilities attached for each position of the switch.

Two-dimensional Monte Carlo Analysis allows for a separate treatment of knowledge related uncertainty and variability related uncertainty (see below under guidance for application). In this two-dimensional mode, Monte Carlo Analysis provides some insight in the quality of the knowledge base. It does not address issues of value loading.