Risk analysts must often consider two sources of variation in their models:
Uncertainty — Assumptions that are uncertain because you have insufficient information about a true, but unknown, value. Examples of uncertainty include the reserve size of an oil field and the prime interest rate in 12 months. You can describe an uncertainty assumption with a probability distribution. Theoretically, you can eliminate uncertainty by gathering more information. Practically, information can be missing because you haven’t gathered it or because it is too costly to gather.
Variability — Assumptions that change because they describe a population with different values. Examples of variability include the individual body weights in a population or the daily number of products sold over a year. You can describe a variability assumption with a discrete distribution (or approximate one with a continuous distribution). Variability is inherent in the system, and you cannot eliminate it by gathering more information.
For many types of risk assessments, it is important to clearly distinguish between uncertainty and variability (see Hoffman and Hammonds reference in the Bibliography). Separating these concepts in a simulation lets you more accurately detect the variation in a forecast due to lack of knowledge and the variation caused by natural variability in a measurement or population. In the same way that a one-dimensional simulation is generally better than single-point estimates for showing the true probability of risk, a two-dimensional simulation is generally better than a one-dimensional simulation for characterizing risk.
The 2D Simulation tool runs an outer loop to simulate the uncertainty values, and then freezes the uncertainty values while it runs an inner loop (of the whole model) to simulate the variability. This process repeats for some number of outer simulations, providing a portrait of how the forecast distribution varies due to the uncertainty.
The primary output of this process is a chart depicting a series of cumulative frequency distributions. You can interpret this chart as the range of possible risk curves associated with a population.