# Toolcatalogus

## Strengths and limitationsTypical 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. |