Which of the following is NOT a listed method to analyze decision trees?

• A. Fold back
• B. Sensitivity analysis
• C. Risk profile analysis
• D. Monte Carlo simulation

Answer: (D)

In decision-tree analysis, the standard way to evaluate the tree is backward induction, often called fold back. You start at the terminal outcomes, combine them with their probabilities, and move step by step toward the root to compute the expected value of each decision. This analytic process directly yields the choice that maximizes expected value at each decision node.

Sensitivity analysis is used to see how robust the recommended decision is when inputs like probabilities or payoffs change. It helps you understand the impact of uncertainty on the decision without changing the calculation method itself. Risk profile analysis, on the other hand, focuses on presenting the full distribution of possible outcomes for each decision, giving a sense of risk in addition to the expected value.

Monte Carlo simulation isn’t part of the standard toolbox for analyzing decision trees. The tree is solved analytically through fold back, using exact probabilities and payoffs. Monte Carlo would involve simulating many random paths, which is a broader stochastic method more suited to complex models than the classic decision-tree analysis. So it’s not a listed method for analyzing decision trees.