Page 202 · Module 08, Decide with Confidence · this screen continues that page
All 108 frameworksDecision Tree Analysis
You are facing a complex decision with multiple possible outcomes at each stage. The
The prompt
Copy & paste readyLabelsReplace theseSwap the role below
Pro tip, from the book
The most valuable output from a Decision Tree is not the expected value calculation it is the sensitivity analysis. Find the probability estimate that, if wrong by 20%, changes which option is best. That is your most critical assumption. Validate it before committing to the path with the highest expected value.
Who should run this prompt
Module role profileWho should stress-test your most important decisions? Most bad decisions were not obviously bad at the time — they had a good first-order case and a hidden second-order cost. The roles on this page are designed to surface what you cannot see from inside your own reasoning. Some will challenge your logic. Some will challenge your assumptions. One will challenge whether you are even asking the right question.
Choose any role to drop it into the prompt above. Only the highlighted opening clause changes — the rest of the prompt stays exactly as printed.
◆◆◆◆◆ → ◆ seniority, board level down to specialist◈ outside the organisation
How to make any role sharper
- Add years of experience: "...with 15 years in enterprise SaaS" produces different depth than just the title.
- Add what they care most about: "You care most about [X]" shapes every word of the output.
- Add their communication style: "Be direct. Flag risks first. No jargon." changes the tone entirely.
Use this when
The problem it solvesdecision is not one choice but a sequence of choices, each dependent on what happens before it. You cannot see the whole picture clearly in your head.
How the framework works
From the printed pageDecision Tree Analysis is an operations research methodology developed in the 1960s that maps decisions as branching trees. Each node represents a decision point or chance event. Each branch represents an outcome with an associated probability and value. Working through the tree, from branches back to the root - allows you to calculate the expected value of each initial choice and identify the path with the highest expected outcome. AI can build a decision tree description and calculate expected values when given decision options, probabilities, and outcome values.
The method, in four moves
Do these in orderDefine your decision options and the key uncertainties before prompting.
Estimate probabilities honestly, if you don't know, use 50/50 and note the sensitivity.
Include the cost of making the decision itself (time, money, opportunity) in the tree.
After building the tree, test it: how much does the best path change if the key probability shifts by 20%? Map every path your decision could take and the odds at each branch A number on every branch feels rigorous, but a guessed 50/50 dressed up in a formal tree is still a guess. The tree organizes your thinking, it doesn't upgrade a bad probability estimate into a good one. Precision Isn't Certainty
Where the framework comes from
Operations research methodology — Decision Tree Analysis formalised 1960s
Pairs well with
As printed with this frameworkModule 08 — Decide with Confidence
Make better decisions faster, with less regret.
10 frameworks, printed on pages 185–204.
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