Decision Trees

A way to map choices and possible outcomes

A decision tree breaks a decision into branches. Each branch represents a choice, condition, or possible outcome.

Decision trees can be useful when a choice leads to different paths depending on what happens next. AI can help you identify the branches, possible outcomes, and questions that need to be answered.

When to use it

Use a decision tree when:

• a decision has several possible paths

• the result depends on what happens next

• different choices lead to different consequences

• you need to identify conditions that could change the decision

• you want to see where additional information is needed

Try this with AI

Help me build a decision tree for [decision]. Start with the main choice and identify the important branches that could follow. For each branch, show the possible outcomes, conditions, and consequences. Identify information I would need before choosing a path. Point out any assumptions or missing branches that could affect the result.

Then ask a follow-up

Which branches in this decision tree are most uncertain or could have the greatest consequences? What information would help me evaluate them?

You can then ask AI to examine a particular branch in greater detail or show how the tree changes if one of your assumptions changes.

A useful caution

A decision tree is only as good as the branches you include.

AI may leave out an important possibility or create branches that seem plausible but are not supported by the facts. A complicated tree can give the impression that every possible outcome has been considered when it has not.

Review the branches yourself. Ask what is missing before relying on the tree.

Remember

A decision tree helps you see where a choice can lead. It does not tell you which path to take.