In decision theory terminology, a course of action or a strategy that may be chosen by a decision maker is called

1. a payoff.

2. an alternative.

3. a state of nature.

4. none of the above.

In decision theory, probabilities are associated with

1. payoffs.

2. alternatives.

3. states of nature.

4. none of the above.

If probabilities are available to the decision maker, then the decision-making environment is called

1. certainty.

2. uncertainty.

3. risk.

4. none of the above.

Which of the following is a decision-making criterion that is used for decision making under risk?

1. expected monetary value criterion

2. Hurwicz criterion (criterion of realism)

3. optimistic (maximax) criterion

4. equally likely criterion

The minimum expected opportunity loss is

1. equal to the highest expected payoff.

2. greater than the expected value with perfect information.

3. equal to the expected value of perfect information.

4. computed when finding the minimax regret decision.

In using the criterion of realism (Hurwicz criterion), the coefficient of realism ($\alpha$)

1. is the probability of a good state of nature.

2. describes the degree of optimism of the decision maker.

3. describes the degree of pessimism of the decision maker.

4. is usually less than zero.

The most that a person should pay for perfect information is

1. the EVPI.

2. the maximum EMV minus the minimum EMV.

3. the maximum EOL.

4. the maximum EMV.

The minimum EOL criterion will always result in the same decision as

1. the maximax criterion.

2. the minimax regret criterion.

3. the maximum EMV criterion.

4. the equally likely criterion.

A decision tree is preferable to a decision table when

1. a number of sequential decisions are to be made.

2. probabilities are available.

3. the maximax criterion is used.

4. the objective is to maximize regret.

Bayes’ Theorem is used to revise probabilities. The new (revised) probabilities are called

1. prior probabilities.

2. sample probabilities.

3. survey probabilities.

4. posterior probabilities.

On a decision tree, at each state-of-nature node,

1. the alternative with the greatest EMV is selected.

2. an EMV is calculated.

3. all probabilities are added together.

4. the branch with the highest probability is selected.

The EVSI

1. is found by subtracting the EMV without sample information from the EMV with sample information.

2. is always equal to the expected value of perfect information.

3. equals the EMV with sample information assuming no cost for the information minus the EMV without sample information.

4. is usually negative.

The efficiency of sample information

1. is the EVSI/(maximum EMV without SI) expressed as a percentage.

2. is the EVPI/EVSI expressed as a percentage.

3. would be 100% if the sample information were perfect.

4. is computed using only the EVPI and the maximum EMV.

On a decision tree, once the tree has been drawn and the payoffs and probabilities have been placed on the tree, the analysis (computing EMVs and selecting the best alternative) is done by

1. working backward (starting on the right and moving to the left).

2. working forward (starting on the left and moving to the right).

3. starting at the top of the tree and moving down.

4. starting at the bottom of the tree and moving up.

In assessing utility values,

1. the worst outcome is given a utility of $\mathrm{-}1.$

2. the best outcome is given a utility of 0.

3. the worst outcome is given a utility of 0.

4. the best outcome is given a value of $\mathrm{-}1.$

If a rational person selects an alternative that does not maximize the EMV, we would expect that this alternative

1. minimizes the EMV.

2. maximizes the expected utility.

3. minimizes the expected utility.

4. has zero utility associated with each possible payoff.