ABSTRACT
Quantitative risk analysis plays an important part in the assessment of the risks and uncertainties surrounding a decision problem. Monte Carlo simulation is a very powerful and flexible way of performing such quantitative risk analyses. It allows the analyst to assign probability distributions to all uncertain components of a mathematical model of the problem and then, through random sampling of these distributions, determine the distribution of all potential outcomes that could occur under these uncertainties. This chapter offers a brief overview of the techniques involved in Monte Carlo risk analysis modeling including how to structure the model, assign distributions to the uncertain components within the model, model dependencies between uncertain components of the model, and finally how to present and interpret the results of the model.
