Build a decision tree with probabilities and expected values
Maps a business decision as a decision tree with chance outcomes, shows the expected-value math, and tests how sensitive the answer is.
The prompt
Help me build a decision tree for a business decision.
The decision: [WHAT WE'RE CHOOSING BETWEEN - e.g. "launch now, delay to fix features, or cancel"]
Options: [LIST]
Uncertain events that affect the outcome: [e.g. "competitor launches first", "pilot succeeds", "regulation passes"]
My estimates: [PROBABILITIES, COSTS, REVENUES, OR VALUES for each outcome - rough is fine; leave blank where unknown]
What matters besides money: [REPUTATION, TEAM, STRATEGIC FIT]
Steps:
1. Lay out the tree: decision nodes (our choices), chance nodes (uncertain events with probabilities that add up to 100%), and end outcomes with their values.
2. Where I haven't given a number, insert a clearly marked placeholder [ADD ESTIMATE] and tell me what it would take to estimate it. Don't make up numbers.
3. Roll back the tree: calculate the expected value of each option and show the arithmetic step by step.
4. Run a sensitivity check: which probability or value, if wrong, would flip the best option, and at what threshold.
5. Note the downside risk of each option (worst realistic outcome), since expected value alone can hide a result we can't afford.
6. Weigh the non-money factors separately rather than forcing them into numbers.
Return:
1. The tree as an indented text outline.
2. A Mermaid flowchart version I can render or paste into slides.
3. Expected value per option with the working.
4. Sensitivity findings and break-even points.
5. A short summary of what the numbers suggest and what they don't capture.
The numbers are only as good as my estimates; the final call is mine.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Thinking & Decisions prompts
What to fill in
- LIST
- REPUTATION, TEAM, STRATEGIC FIT
- ADD ESTIMATE
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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