Decision Tree

A branching representation of sequential choices and uncertain events, evaluated with a stated objective and available information.

A decision tree lays out choices, uncertain events, and later choices in the order they can occur. Decision nodes represent actions available to the decision maker. Chance nodes represent events outside that person’s control. Outcomes carry relevant consequences, such as cost, time, or utility.

Work backward from the outcomes. At a chance node, combine values using conditional probabilities; at a decision node, select the feasible action that best meets the chosen objective. Expected money is only one objective and can conceal unacceptable downside. A tree must respect what information will be known when each choice is made. It is a structured aid to judgment, and differs from a machine-learning classification tree.

When to use it

Useful when a decision contains consequential uncertainty or later choices that depend on earlier outcomes.

How it can help

Make timing, contingencies, costs, and information-dependent choices explicit.