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1 | |
Rules for selecting the best alternative are called: |
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| | A) | Outcomes. |
| | B) | Objectives. |
| | C) | Events. |
| | D) | Criteria. |
| | E) | Decisions. |
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2 | |
Constraints help us to limit the decision model to what is feasible. |
|
| | A) | TRUE |
| | B) | FALSE |
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3 | |
In a decision tree, the points at which decisions are made or events happen are called: |
|
| | A) | Criteria. |
| | B) | Nodes. |
| | C) | Probabilities. |
| | D) | Branches. |
| | E) | Payoffs. |
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4 | |
For the purposes of mathematical modelling of decisions, what is a decision under certainty? |
|
| | A) | A decision where the answer is immediately obvious. |
| | B) | A decision where all the possible information is known. |
| | C) | A decision involving the use of probabilities. |
| | D) | A decision where outcomes following certain actions are known. |
| | E) | A decision where success is certain. |
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5 | |
What is the Laplace decision criterion? |
|
| | A) | If the probabilities of events are unknown then treat them as equally likely. |
| | B) | Devise a priori estimates of unknown probabilities, based on the situation. |
| | C) | Estimate unknown probabilities empirically. |
| | D) | If in doubt, roll a dice. |
| | E) | Only tackle decisions where the probabilities of events are known. |
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6 | |
In using the Wald (maximin) criterion, a decision maker finds the best possible outcome for each alternative and then selects the worst among these. |
|
| | A) | TRUE |
| | B) | FALSE |
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7 | |
What in decision analysis is the meaning of the term ‘regret’? |
|
| | A) | The anguish felt by a decision maker after getting it wrong. |
| | B) | The loss made by the decision maker |
| | C) | The difference between the actual payoff and the maximum payoff following an event |
| | D) | The difference between the outcomes of different events |
| | E) | The minimax solution of a payoff matrix |
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8 | |
Which of the following considerations are relevant in choosing whether to apply Laplace, Wald or Savage decision criteria under uncertainty? |
|
| | A) | Your views about the probabilities of certain events. |
| | B) | How averse you are to risk-taking. |
| | C) | The size of the relative payoffs. |
| | D) | The size of the penalty for getting the decision wrong. |
| | E) | All the above. |
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9 | |
In a decision tree for a sequential decision under risk, where do you show the probabilities? |
|
| | A) | On the nodes. |
| | B) | On the origins. |
| | C) | Next to the payoffs. |
| | D) | On the branches after the decisions. |
| | E) | On the branches after the events. |
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10 | |
In decision making under risk, what is the expected value of perfect information? |
|
| | A) | The money that companies pay to market research agencies. |
| | B) | The difference between the payoff under certainty and the expected payoff following the best alternative. |
| | C) | The inverse of the minimax regret criterion. |
| | D) | The expected opportunity cost of a decision. |
| | E) | (One minus the expected value of imperfect information) |
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11 | |
If payoff is plotted on the horizontal axis and utility is plotted on the vertical axis, then typically the utility curve for a decision maker is: |
|
| | A) | A straight line. |
| | B) | An s-shaped curve sloping downwards from left to right. |
| | C) | A flat line. |
| | D) | An s-shaped curve sloping upwards from left to right. |
| | E) | A vertical line. |
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12 | |
Why is utility an important concept in decision analysis? |
|
| | A) | It is an alternative measure of opportunity cost. |
| | B) | It provides an tool for estimating probabilities. |
| | C) | It allows us to distinguish between monetary value and the perceived value of payoffs. |
| | D) | It separates the values of different events as we construct the decision model. |
| | E) | It means that we do not have to estimate probabilities. |
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13 | |
Decision models do not always give us the maximum payoff. |
|
| | A) | TRUE |
| | B) | FALSE |
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14 | |
What does the cell  <![CDATA[<a onClick="window.open('/olcweb/cgi/pluginpop.cgi?it=gif::::/sites/dl/free/0077098056/70736/ch14q02eq01.GIF','popWin', 'width=NaN,height=NaN,resizable,scrollbars');" href="#"><img valign="absmiddle" height="16" width="16" border="0" src="/olcweb/styles/shared/linkicons/image.gif"> (0.0K)</a>]]> in a payoff matrix tell us? |
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| | A) | The payoff following action j and event i |
| | B) | The payoff following objective i and event j |
| | C) | The payoff following decisions i and j |
| | D) | The payoff following event i and action j |
| | E) | The payoff following criterion i and decision j |
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15 | |
The formula for expected value of an alternative <![CDATA[<a onClick="window.open('/olcweb/cgi/pluginpop.cgi?it=gif::::/sites/dl/free/0077098056/70736/ch14q02eq01.GIF','popWin', 'width=NaN,height=NaN,resizable,scrollbars');" href="#"><img valign="absmiddle" height="16" width="16" border="0" src="/olcweb/styles/shared/linkicons/image.gif"> (0.0K)</a>]]> is defined as  <![CDATA[<a onClick="window.open('/olcweb/cgi/pluginpop.cgi?it=gif::::/sites/dl/free/0077098056/70736/ch14q10eq01.GIF','popWin', 'width=NaN,height=NaN,resizable,scrollbars');" href="#"><img valign="absmiddle" height="16" width="16" border="0" src="/olcweb/styles/shared/linkicons/image.gif"> (1.0K)</a>]]> |
|
| | A) | TRUE |
| | B) | FALSE |
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