Chapter14HWSolutions
Chapter 14
Decision Analysis
Learning Objectives
1. Learn how to describe a problem situation in terms of decisions to be made, chance events and consequences.
2. Be able to analyze a simple decision analysis problem from both a payoff table and decision tree point of view.
3. Be able to develop a risk profile and interpret its meaning.
4. Be able to use sensitivity analysis to study how changes in problem inputs affect or alter the recommended decision.
5. Be able to determine the potential value of additional information.
6. Learn how new information and revised probability values can be used in the decision analysis approach to problem solving.
7. Understand what a decision strategy is.
8. Learn how to evaluate the contribution and efficiency of additional decision making information.
9. Be able to use a Bayesian approach to computing revised probabilities.
10. Know what is meant by utility.
11. Understand why utility could be preferred to monetary value in some situations.
12. Be able to use expected utility to select a decision alternative.
13. Be able to use TreePlan software for decision analysis problems.
14. Understand the following terms:
decision alternatives decision strategy chance events risk profile states of nature sensitivity analysis influence diagram prior probabilities payoff table posterior probabilities decision tree expected value of sample information (EVSI) optimistic approach efficiency of sample information conservative approach
Bayesian revision minimax regret approach utility opportunity loss or regret lottery expected value approach expected utility expected value of perfect information (EVPI)
Solutions:
1. a.
b.
Decision
Maximum Profit
Minimum Profit d1 250
25
d2
100
75
Optimistic approach: select d1 Conservative approach: select d2
Regret or opportunity loss table:
s1 s2 s3 d1 0
0
50 d2 150
0
0
Decision Analysis
Learning Objectives
1. Learn how to describe a problem situation in terms of decisions to be made, chance events and consequences.
2. Be able to analyze a simple decision analysis problem from both a payoff table and decision tree point of view.
3. Be able to develop a risk profile and interpret its meaning.
4. Be able to use sensitivity analysis to study how changes in problem inputs affect or alter the recommended decision.
5. Be able to determine the potential value of additional information.
6. Learn how new information and revised probability values can be used in the decision analysis approach to problem solving.
7. Understand what a decision strategy is.
8. Learn how to evaluate the contribution and efficiency of additional decision making information.
9. Be able to use a Bayesian approach to computing revised probabilities.
10. Know what is meant by utility.
11. Understand why utility could be preferred to monetary value in some situations.
12. Be able to use expected utility to select a decision alternative.
13. Be able to use TreePlan software for decision analysis problems.
14. Understand the following terms:
decision alternatives decision strategy chance events risk profile states of nature sensitivity analysis influence diagram prior probabilities payoff table posterior probabilities decision tree expected value of sample information (EVSI) optimistic approach efficiency of sample information conservative approach
Bayesian revision minimax regret approach utility opportunity loss or regret lottery expected value approach expected utility expected value of perfect information (EVPI)
Solutions:
1. a.
b.
Decision
Maximum Profit
Minimum Profit d1 250
25
d2
100
75
Optimistic approach: select d1 Conservative approach: select d2
Regret or opportunity loss table:
s1 s2 s3 d1 0
0
50 d2 150
0
0