Decision Tree Analysis
Decision tree analysis is a diagramming and quantitative technique used to evaluate multiple decision alternatives, each with associated costs, probabilities, and outcomes, to select the option with the best expected monetary value.
Explanation
A decision tree is a branching diagram that models a sequence of decisions and chance events. Decision nodes (squares) represent choices the project manager can make. Chance nodes (circles) represent uncertain events with associated probabilities. Each path through the tree ends with a payoff or cost value.
To analyze a decision tree, you work backward from the end outcomes (right to left). At each chance node, you calculate the EMV by multiplying probabilities by payoff values and summing them. At each decision node, you select the alternative with the best (highest for gains, lowest for costs) EMV. This process is called "folding back" the tree.
Decision tree analysis is particularly useful when deciding between alternatives such as build vs. buy, different vendors, or whether to invest in risk mitigation. It makes the decision logic transparent and quantifies the trade-offs, making it easier to justify the chosen path to stakeholders.
Key Points
- •Uses decision nodes (squares) and chance nodes (circles) in a branching diagram
- •EMV is calculated at each chance node and the best option is selected at decision nodes
- •Analyzed by "folding back" from right to left
- •Useful for build-vs-buy, make-or-buy, and go/no-go decisions
Exam Tip
Decision tree questions on the exam require you to calculate EMV at each branch and select the path with the best overall value. Practice reading decision tree diagrams and folding back.
Frequently Asked Questions
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