Developed by Howard Raiffa and Robert Schlaifer (Applied Statistical Decision Theory, 1961) and Ronald A. Howard (Decision Analysis: Applied Decision Theory, 1966), on the expected-utility theory of John von Neumann and Oskar Morgenstern (Theory of Games and Economic Behavior, 1944).
Draws the choices and chance events of a decision as branches, puts a probability and value on each, and rolls them back to compare options by expected value.
When to reach for it
Go or no-go, pricing, or investment decisions that depend on uncertain events whose odds and payoffs can be estimated.
How it runs
- Draw each decision as a square node.
- Branch the chance events that follow as circles.
- Assign probabilities to each chance branch and values to each outcome.
- Roll back from the right, averaging at chance nodes.
- Take the best branch at each decision node.
- Vary the key estimates to see where the answer flips.
Where it sits in SOLVE eX
Go to the source
- Applied Statistical Decision Theory, 1961
- Decision Analysis: Applied Decision Theory, 1966
- Theory of Games and Economic Behavior, 1944