ABSTRACT
The Expected Value Decision criterion is often used by decision makers to choose the best action in a stochastic environment. The Expected Value Decision chooses the action which provides the best expected return for the decision maker. The Maximin Decision criterion is often used for decisions under uncertainty where the stochastic outcome probabilities cannot be satisfactorily assessed. Decision makers seek to increase their confidence in which outcome state will occur so that a decision can be made with greater certainty. Decision analysis selects the best action among a set of alternative actions. Decision matrices and decision trees are both useful for single decisions where each action has the same set of possible outcomes and probabilities. Decision trees have the additional capability of solving multiple stage decisions where there may be different actions, outcomes, probabilities, and payoffs to consider at each stage of the decision.
