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Expected Value Decision Analysis

Decision alternatives Options available to the decision maker.
Chance event An uncertain future event affecting the consequence, or payoff, associated with a decision.
Consequence The result obtained when a decision alternative is chosen and a chance event occurs. A measure of the consequence is often called a payoff.
States of nature The possible outcomes for chance events that affect the payoff associated with a decision alternative.
Influence diagram A graphical device that shows the relationship among decisions, chance events, and consequences for a decision problem.
Node An intersection or junction point of an influence diagram or a decision tree.
Decision nodes Nodes indicating points where a decision is made.
Chance nodes Nodes indicating points where an uncertain event will occur.
Consequence nodes Nodes of an influence diagram indicating points where a payoff will occur.
Payoff A measure of the consequence of a decision such as profit, cost, or time. Each combination of a decision alternative and a state of nature has an associated payoff (consequence).
Payoff table A tabular representation of the payoffs for a decision problem.
Decision tree A graphical representation of the decision problem that shows the sequential nature of the decision-making process.
Branch Lines showing the alternatives from decision nodes and the outcomes from chance nodes.
Optimistic approach An approach to choosing a decision alt. w/o using probabilities. For a max.problem, it leads to choosing the decision alt.corresponding to the largest payoff; for a min. problem, it leads to choosing the decision alt. corresponding to the smallest payoff.
Conservative approach An approach to choosing a decision alt. w/o using probabilities. For a max.problem, it leads to choosing the decision alt.that max. the min. payoff; for a min. problem, it leads to choosing the decision alternative that minimizes the maximum payoff.
Minimax regret approach An approach to choosing a decision alternative without using probabilities. For each alternative, the maximum regret is computed, which leads to choosing the decision alternative that minimizes the maximum regret.
Opportunity loss, or regret The amount of loss (lower profit or higher cost) from not making the best decision for each state of nature.
Expected value approach An approach to choosing a decision alternative based on the expected value of each decision alternative. The recommended decision alternative is the one that provides the best expected value.
Expected value (EV) For a chance node, it is the weighted average of the payoffs. The weights are the state-of-nature probabilities.
Expected value of perfect information (EVPI) The expected value of information that would tell the decision maker exactly which state of nature is going to occur (i.e., perfect information).
Risk analysis The study of the possible payoffs and probabilities associated with a decision alternative or a decision strategy.
Sensitivity analysis The study of how changes in the probability assessments for the states of nature or changes in the payoffs affect the recommended decision alternative.
Risk profile The probability distribution of the possible payoffs associated with a decision alternative or decision strategy.
Prior probabilities The probabilities of the states of nature prior to obtaining sample information.
Sample information New information obtained through research or experimentation that enables an updating or revision of the state-of-nature probabilities.
Posterior (revised) probabilities The probabilities of the states of nature after revising the prior probabilities based on sample information.
Decision strategy A strategy involving a sequence of decisions and chance outcomes to provide the optimal solution to a decision problem.
Expected value of sample information (EVSI) The difference between the expected value of an optimal strategy based on sample information and the “best” expected value without any sample information.
Efficiency The ratio of EVSI to EVPI as a percentage; perfect information is 100% efficient.
Bayes’ theorem A theorem that enables the use of sample information to revise prior probabilities.
Conditional probabilities The probability of one event given the known outcome of a (possibly) related event.
Joint probabilities The probabilities of both sample information and a particular state of nature occurring simultaneously.
Created by: mmoreno12
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