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In decision theory terminology, a course of action or a strategy that may be chosen by a decision maker is called A payoff An alternative
- In decision theory terminology, a course of action or a strategy that may be chosen by a decision maker is called
- A payoff
- An alternative
- A state of nature
- None of the above
- In decision theory, probabilities are associated with
- Payoffs
- An alternative
- State of nature
- None of the above
- If probabilities are available to the decision maker, then the decision-making environment is called
- Certainty
- Uncertainty
- Risk
- None of the above
- Which of the following is a decision-making criterion that is used for decision making under risk?
- Expected monetary value criterion
- Hurwicz criterion (criterion of realism)
- Optimistic (maximax) criterion
- Equally likely criterion
- The minimum expected opportunity loss
- Is equal to the highest expected payoff
- Is greater than the expected value with perfect information
- Is equal to the expected value of perfect information
- Is computed when finding the minimax regret decision
- In using the criterion of realism (Hurwicz criterion), the coefficient of realism
- Is the probability of a good state of nature
- Describes the degree of optimism of the decision maker
- Describes the degree of pessimism of the decision maker
- Is usually less than zero
- The most that a person should pay for perfect information is
- The EVPI
- The maximum EMV minus the minimum EMV
- The maximum EOL
- The maximum EMV
- The minimum EOL criterion will always result in the same decision as
- The maximax criterion
- The minimax regret criterion
- The maximum EMV criterion
- The equally likely criterion
- A decision tree is preferable to a decision table when
- A number of sequential decisions are to be made
- Probabilities are available
- The maximax criterion is used
- The objective is to maximize regret
- Bayes' theorem is used to revise probabilities. The new (revised) probabilities are called
- Prior probabilities
- Sample probabilities
- Survey probabilities
- Posterior probabilities
- On a decision tree, at each state of nature node,
- The alternative with the greatest EMV is selected
- An EMV is calculated
- All probabilities are added together
- The branch with the highest probability is selected
- The EVSI
- Is found by subtracting the EMV without sample information from the EMV with sample information
- Is always equal to the expected value of perfect information
- Equals the EMV with sample information assuming no cost for the information minus the EMV without sample information.
- Is usually negative
- The efficiency of sample information
- Is the EVSI/(Maximum EMV without SI) expressed as a percentage
- Is the EVP/EVSI expressed as a percentage
- Would be 100% if the sample information were perfect
- 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 working backward (starting on the right and moving to the left)
- Is done by working forward (starting on the left and moving to the right)
- Is done by starting at the top of the tree and moving down
- Is done by starting at the bottom of the tree and moving down
- In assessing utility values,
- The worst outcome is given a utility of -1
- The best outcome is given a utility of 0
- The worst outcome is given a utility of 0
- The best outcome is given a value of -1
- If a rational person selects an alternative that does not maximize the EMV, we would expect that this alternative
- Minimizes the EMV
- Maximizes the expected utility
- Minimizes the expected utility
- Has zero utility associated with each possible payoff.
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