Lays out a staged bet as a decision tree, rolls it back to expected values and prices the information a pilot or test would buy — expected value of perfect information (EVPI), expected value of sample information (EVSI) and a one-way sensitivity (tornado) with switching probabilities (Raiffa 1968). Use when a go/no-go, invest/wait or pilot-then-commit choice hinges on chance outcomes — "is the pilot worth running before committing?", "expected value of perfect information", "which uncertainty is the decision most sensitive to?", "build the decision tree". Not for designing the experiment itself (`cheapest-experiment`) or revising one probability (`bayesian-update`).
