@RISK is available with companion product PrecisionTree in the DecisionToolsSuite. PrecisionTree creates decision trees in Excel to allow you to map and understand the complex decision problems. @RISK functions are recognized by PrecisionTree, and the two may be launched from a common Excel toolbar.
@RISK allows you to:
- quantify the uncertainty that exists in the values and probabilities which define your decision trees, and
- more accurately describe chance events as a continuous range of possible outcomes.
Using this information, @RISK performs a Monte-Carlo simulation on your decision tree, analyzing every possible outcome and graphically illustrating the risks you face.
With @RISK, all uncertain values and probabilities for branches in your decision trees, and supporting spreadsheet models, can be defined with distribution functions. When a branch from a decision or chance node has an uncertain value, for example, this value can be described by an @RISK distribution function. During a normal decision analysis, the expected value of the distribution function will be used as the value for the branch. The expected value for a path in the tree will be calculated using this value.
However, when a simulation is run using @RISK, a sample will be drawn from each distribution function during each iteration of the simulation. The value of the decision tree, and its nodes, will then be recalculated using the new set of samples and the results recorded by @RISK. A range of possible values will then be displayed for the decision tree. Instead of seeing a risk profile with a discrete set of possible outcomes and probabilities, a continuous distribution of possible outcomes is generated by @RISK. You can see the chance of any result occurring.
In decision trees, chance events must be described in terms of discrete outcomes (a chance node with a finite number of outcome branches). But, in real life, many uncertain events are continuous, meaning that any value between a minimum and maximum can occur. Using @RISK with PrecisionTree makes modeling continuous events easier, using distribution functions. Also, @RISK functions can make your decision tree smaller and easier to understand!