Solving the Decision Tree

Decision analysts are divided as to whether solving the decision tree or running a Monte Carlo simulation is the better way to develop an s-curve. Both methods work. If you have time, using both methods has the advantage of providing a step of quality assurance versus an input or computational error. We’ll start with solving the decision tree.

To solve the tree, we

build out the tree using each uncertainty with corresponding p10/50/90 branches,

compute the measure of value (usually NPV) and probability for each end point branch,

build a table of these results,

sort from high to low NPV,

calculate the cumulative probability of each branch, and

plot the cumulative probability versus the sorted values using ...

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