23. Sensitivity Analysis
Within this section you can details about all types of Sensitivity Analysing including: 1way, Tornado, PSA, and other uncertainty analysis.
Background
Sensitivity analysis gives a means of assessing the extent to which a model’s calculations and recommendations are affected by uncertainty.
One method for a model in decision analysis to deal with uncertainty is to reflect it explicitly in the model’s structure. Events which have a significant impact on outcomes, and which are not under the decision maker’s control, can be described using chance nodes and incorporated into the model calculation. A problem may involve numerous uncertainties; not all of them can or should be represented in the structure of the model. To deal with this, deterministic sensitivity analysis and probabilistic sensitivity analysis are used to examine the potential impact of changing the assumptions related to the parameters.
Deterministic sensitivity analysis can take a variety of forms including 1way, 2way, and 3way sensitivity analysis and tornado diagrams. It can be used to identify critical uncertainties by examining the extent to which a model’s calculations and recommendations are affected as a consequence of changing selected assumptions. Probabilistic sensitivity analysis (PSA) can incorporate all parameter uncertainties. PSA quantifies the level of confidence that can be placed in the model’s results.
Sensitivity analysis and other analytical tools can also be used to improve decision making. This is done by determining the potential value of obtaining various kinds of information (perfect, imperfect, or sampling information) that might help resolve critical uncertainties.
Specific questions about the model that sensitivity analysis can help answer are:

Is a model sensitive to a particular uncertainty — e.g., does varying a parameter’s value result in changes in optimal strategy?

If a model is sensitive to a particular uncertainty, at what value(s) of the parameter does the model recommend a change in strategy?

Does the sensitivity analysis result make sense? (This is a model debugging question.)
The Sensitivity Analysis (Deterministic) section shows how to perform a oneway sensitivity analysis, and how to interpret the results. The Probabilistic Sensitivity Analysis section covers the use of probability distributions and Monte Carlo simulation to analyze models with complex or numerous uncertainties.
Variables and sensitivity analysis
When you develop your model, you do base case analysis on the decision trees with numeric values for payoffs and probability values. In order to perform sensitivity analysis on an uncertain quantity, its numeric value must be represented with a variable — a named parameter.
Refer back to the Variables: Named Model Values section which provides important details on working efficiently with variables in decision trees. The information in these chapters can help you improve your productivity when building complex decision trees, and also insure against costly modeling errors.