Chapter 21

An intersection or junction point of a decision tree is called a (n) a. junction b. intersection c. intersection point d. node

node

Refer to Exhibit 21-1. The expected value of perfect information is a. 6.2 b. 2.0 c. 13.6 d. 4.8

2.0

For a decision alternative, the weighted average of the payoffs is known as a. the expected value of perfect information b. the expected value c. the expected probability d. perfect information

the expected value

A graphic presentation of the expected gain from the various options open to the decision maker is called a. a payoff table b. a decision tree c. the expected opportunity loss d. the expected value of perfect information

a decision tree

Refer to Exhibit 21-4. The expected value of perfect information is a. 1.5 b. 1.2 c. 1.0 d. 4.8

1.2

Refer to Exhibit 21-4. The expected monetary value of alternative C is a. 10.2 b. 13.2 c. 12.9 d. 26

10.2

Below you are given a payoff table involving two states of nature and three decision alternatives. Refer to Exhibit 21-1. The expected monetary value of the best alternative is a. 8.8 b. 7.4 c. 9.6 d. 11.6

11.6

Refer to Exhibit 21-5. The expected value of perfect information is a. 18.2 b. 11.7 c. 51 d. 37

11.7

Refer to Exhibit 21-3. The expected value of perfect information equals a. 13,000 b. 14,000 c. 15,000 d. 16,000

13,000

Below you are given a payoff table involving two states of nature and three decision alternatives. The probability of the occurrence of S1 = 0.3. Refer to Exhibit 21-4. The expected value of the best alternative is a. 12.9 b. 13.2 c. 10.2 d. 28.0

13.2

Refer to Exhibit 21-3. The expected monetary value of the best alternative equals a. 13,000 b. 14,000 c. 15,000 d. 16,000

16,000

Refer to Exhibit 21-2. The expected value of the best alternative equals a. 29 b. 105 c. 12 d. 38.5

38.5

Refer to Exhibit 21-2. The expected value of perfect information equals a. 12 b. 4 c. 37 d. 29

4

Refer to Exhibit 21-5. The expected monetary value of alternative C is a. 30 b. 6.5 c. 5.7 d. 5.5

5.5

Below you are given a payoff table involving three states of nature and three decision alternatives. The probability of occurrence of S1 is 0.2 and the probability of occurrence of S2 is 0.3. Refer to Exhibit 21-5. The expected monetary value of the best alternative is a. 5.0 b. 6.5 c. 7.5 d. 9.0

6.5

Refer to Exhibit 21-1. The expected monetary value of alternative A is a. 7.4 b. 11.6 c. 8.8 d. 13

7.4

Refer to Exhibit 21-5. The recommended decision alternative based on the expected monetary value is a. A b. B c. C d. All alternatives are the same.

A

Refer to Exhibit 21-1. The recommended decision alternative based on the expected monetary value is a. A b. B c. C d. All alternatives are the same.

B (10-12)

Refer to Exhibit 21-4. The recommended decision alternative based on the expected monetary value is a. A b. B c. C d. None of these alternatives is correct.

B (16-12)

Below you are given a payoff table involving three states of nature and two decision alternatives. The probability that S1 will occur is 0.1; the probability that S2 will occur is 0.6. Refer to Exhibit 21-2. The recommended decision based on the expected value criterion is a. A b. B c. Both alternatives are the same. d. None of these alternatives is correct

B (40-50-15)

The process of revising prior probabilities to create posterior probabilities based on sample information is a a. revision process b. sampling revision c. Bayesian revision d. posterior revision

Bayesian revision

Below you are given a payoff table involving two states of nature and three decision alternatives. The probability of the occurrence of state of nature S1 is 0.4. Refer to Exhibit 21-3. The recommended decision based on the expected monetary value criterion is a. A b. B c. C d. All alternatives are the same.

C (25,000 - 10,000)

The efficiency of information is the ratio of a. EOL to EVSI b. EOL to EVPI c. EVPI to EVSI d. EVSI to EVPI

EVSI to EVPI

The probability of both sample information and a particular state of nature occurring simultaneously is a. unconditional probability b. joint probability c. marginal probability d. conditional probability

Joint probability

A tabular presentation of the expected gain from the various options open to a decision maker is called a. a payoff table b. a decision tree c. the expected opportunity loss d. the expected value of perfect information

a payoff table

Information about a state of nature is known as a. natural information b. states information c. a sampling method d. an indicator

an indicator

A line or arc connecting the nodes of a decision tree is called a(n) a. junction b. intersection c. branch d. node

branch

An uncertain future event affecting the consequence, or payoff, associated with a decision is known as a. unconditional probability b. unknown probability c. chance event d. uncertain probability

chance event

Nodes indicating points where an uncertain event will occur are known as a. decision nodes b. chance nodes c. marginal nodes d. conditional nodes

chance nodes

The probability of one event given the known outcome of a (possibly) related event is known as a. unconditional probability b. joint probability c. marginal probability d. conditional probability

conditional probability

The probability of the states of nature, after use of Bayes' theorem to adjust the prior probabilities based upon given indicator information, is called a. marginal probability b. conditional probability c. posterior probability d. None of these alternatives is correct.

conditional probability

The result obtained when a decision alternative is chosen and a chance event occurs is known as a. happenstance b. consequence c. alternative probability d. conditional probability

consequence

Nodes indicating points where a decision is made are known as a. decision nodes b. chance nodes c. marginal nodes d. conditional nodes

decision nodes

A decision criterion which weights the payoff for each decision by its probability of occurrence is known as the a. Payoff criterion b. expected value criterion c. probability d. expected value of perfect information

expected value criterion

The expected opportunity loss of the best decision alternative is the a. expected monetary value b. payoff c. expected value of perfect information d. None of these alternatives is correct.

expected value of perfect information

The difference between the expected value of an optimal strategy based on sample information and the "best" expected value without any sample information is called the a. optimal information b. expected value of sample information c. expected value of perfect information d. efficiency of information

expected value of sample information

A tabular representation of the payoffs for a decision problem is a a. decision tree b. payoff table c. matrix d. sequential matrix

payoff table

Prior probabilities are the probabilities of the states of nature a. after obtaining sample information b. prior to obtaining of perfect information c. prior to obtaining sample information d. after obtaining perfect information

prior to obtaining sample information

New information obtained through research or experimentation that enables an updating or revision of the state-of-nature probabilities is a. population information b. sampling without replacement c. sample information d. conditional information

sample information

Future events that cannot be controlled by the decision maker are called a. indicators b. states of nature c. prior probabilities d. posterior probabilities

states of nature

The uncontrollable future events that can affect the outcome of a decision are known as a. alternatives b. decision outcome c. payoff d. states of nature

states of nature

The expected value of information that would tell the decision maker exactly which state of nature is going to occur is a. the expected value of sample information b. the expected value of perfect information c. the maximum information d. the expected value

the expected value of perfect information

The probabilities of states of nature after revising the prior probabilities based on given indicator information are a. the expected probabilities b. the posterior probabilities c. the prior probabilities

the posterior probabilities

In computing an expected value (EV), the weights are a. decision alternative probabilities b. in pounds or some unit of weight c. in dollars or some units of currency d. the state-of-nature probabilities

the state-of-nature probabilities