Probabilistic Analysis and Decision Trees

Define probability and describe its role in quantifying uncertainty in project outcomes.

Calculate and interpret the expected value for projects with probabilistic outcomes.

Construct a decision tree to model a sequence of decisions and chance events.

Identify decision nodes, chance nodes, and payoffs within a decision tree.

Analyze a decision tree by applying the rollback procedure to determine the optimal decision path.

Apply decision tree analysis to a technology investment problem with uncertain outcomes.