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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.