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8.9AdvancedModule 08 · Decide with Confidence

Decision Tree Analysis

You are facing a complex decision with multiple possible outcomes at each stage. The

The prompt

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8.9Decision Tree Analysis
Act as a decision analysis consultant using Decision Tree Analysis. Decision : [DESCRIBE THE MAIN CHOICE YOU ARE FACING] Options : [LIST YOUR 2–4 ALTERNATIVE PATHS] Key uncertainties : [WHAT OUTCOMES DEPEND ON CHANCE OR EXTERNAL FACTORS YOU CAN'T CONTROL] Values: [WHAT EACH OUTCOME IS WORTH, REVENUE, COST, TIME, OR STRATEGIC VALUE] Build a Decision Tree: LEVEL 1: The initial decision - my options LEVEL 2: For each option - the key chance events and their estimated probabilities LEVEL 3: For each outcome - the value or consequence CALCULATE: Expected Value for each initial option = Sum of (probability × outcome value) per path RECOMMEND: Which option has the highest expected value? Which has the best worst-case outcome? What single probability estimate most changes the recommendation if it turns out to be wrong? Decision Tree Analysis Impacts Outcomes Outcome Node 1 Outcome Node 2 First possible result Second possible result Decision Node Starting point of analysis
LabelsReplace theseSwap the role below

LabelsReplace 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 profile

Who 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.

Internal rolesThink from inside the organisation
Outside perspectivesChallenge your blind spots

◆◆◆◆◆ → ◆ seniority, board level down to specialist outside the organisation

How to make any role sharper

  1. Add years of experience: "...with 15 years in enterprise SaaS" produces different depth than just the title.
  2. Add what they care most about: "You care most about [X]" shapes every word of the output.
  3. Add their communication style: "Be direct. Flag risks first. No jargon." changes the tone entirely.
See also: 1.3 Role Prompting - The Expert Chair

Use this when

The problem it solves

decision 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 page

Decision 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 order
1

Define your decision options and the key uncertainties before prompting.

2

Estimate probabilities honestly, if you don't know, use 50/50 and note the sensitivity.

3

Include the cost of making the decision itself (time, money, opportunity) in the tree.

4

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

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Framework8.9 of 108
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Prompt extracted18 Sept 2026
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