Decision Trees
Visualize choices, chance nodes, and payoffs to structure decisions.
Explanation
When facing complex decisions with multiple steps and uncertain outcomes, decision trees help you visualize all the possible paths. You draw out each choice you could make, what random events might happen next, and what outcomes those could lead to. By mapping this out, you can calculate which initial choice gives you the best expected outcome.
Real-World Example
Launch product: Small launch (70% mild success → expand; 30% fail → stop) vs Big launch (40% huge success; 60% expensive failure). Tree shows small launch has better expected value. Negotiate job: Accept offer vs Counter (50% they agree; 30% original offer; 20% rescind).
How to Apply
Draw square for decisions, circle for chance events. Label probabilities (must sum to 100%). Add values at endpoints. Calculate backwards: multiply probability × value. At decision nodes, choose highest value. At chance nodes, sum all branches. Update probabilities as you learn.