190 Ch 4 – Flashcards

1. The options from which a decision maker chooses a course of action are a. called the decision alternatives. b. under the control of the decision maker. c. not the same as the states of nature. d. All of the alternatives are true.

States of nature a. can describe uncontrollable natural events such as floods or freezing temperatures. b. can be selected by the decision maker. c. cannot be enumerated by the decision maker. d. All of the alternatives are true.

3. A payoff a. is always measured in profit. b. is always measured in cost. c. exists for each pair of decision alternative and state of nature. d. exists for each state of nature.

4. Making a good decision a. requires probabilities for all states of nature. b. requires a clear understanding of decision alternatives, states of nature, and payoffs. c. implies that a desirable outcome will occur. d. All of the alternatives are true.

5. A decision tree a. presents all decision alternatives first and follows them with all states of nature. b. presents all states of nature first and follows them with all decision alternatives. c. alternates the decision alternatives and states of nature. d. arranges decision alternatives and states of nature in their natural chronological order.

6. Which of the methods for decision making without probabilities best protects the decision maker from undesirable results? a. the optimistic approach b. the conservative approach c. minimum regret d. minimax regret

7. Sensitivity analysis considers a. how sensitive the decision maker is to risk. b. changes in the number of states of nature. c. changes in the values of the payoffs. d. changes in the available alternatives.

8. To find the EVSI, a. use the EVPI to calculate sample information probabilities. b. use indicator probabilities to calculate prior probabilities. c. use prior and sample information probabilities to calculate revised probabilities. d. use sample information to revise the sample information probabilities.

9. If P(high) = .3, P(low) = .7, P(favorable|high) = .9, and P(unfavorable|low) = .6, then P(favorable) = a. .10 b. .27 c. .30 d. .55

10. The efficiency of sample information is a. EVSI*(100%) b. EVSI/EVPI*(100%) c. EVwoSI/EVwoPI*(100%) d. EVwSI/EVwoSI*(100%)

11. Decision tree probabilities refer to a. the probability of finding the optimal strategy b. the probability of the decision being made c. the probability of overlooked choices d. the probability of an uncertain event occurring

12. For a maximization problem, the conservative approach is often referred to as the a. minimax approach b. maximin approach c. maximax approach d. minimin approach

13. For a minimization problem, the optimistic approach is often referred to as the a. minimax approach b. maximin approach c. maximax approach d. minimin approach

14. For a maximization problem, the optimistic approach is often referred to as the a. minimax approach b. maximin approach c. maximax approach d. minimin approach

15. For a minimization problem, the conservative approach is often referred to as the a. minimax approach b. maximin approach c. maximax approach d. minimin approach

(T/F) Sample information with an efficiency rating of 100% is perfect information.

(T/F) States of nature should be defined so that one and only one will actually occur.

(T/F) Decision alternatives are structured so that several could occur simultaneously.

(T/F) Square nodes in a decision tree indicate that a decision must be made.

(T/F) Circular nodes in a decision tree indicate that it would be incorrect to choose a path from the node.

(T/F) Risk analysis helps the decision maker recognize the difference between the expected value of a decision alternative and the payoff that may actually occur.

(T/F) The expected value of an alternative can never be negative.

(T/F) Expected value is the sum of the weighted payoff possibilities at a circular node in a decision tree.

(T/F) EVPI is always greater than or equal to EVSI.

(T/F) After all probabilities and payoffs are placed on a decision tree, the decision maker calculates expected values at state of nature nodes and makes selections at decision nodes.

(T/F) A decision strategy is a sequence of decisions and chance outcomes, where the decisions chosen depend on the yet to be determined outcomes of chance events.

(T/F) EVPI equals the expected regret associated with the minimax decision.

(T/F) The expected value approach is more appropriate for a one-time decision than a repetitive decision.

(T/F) Maximizing the expected payoff and minimizing the expected opportunity loss result in the same recommended decision.

(T/F) The expected value of sample information can never be less than the expected value of perfect information.