1. Decision Analysis

2. How to make a difficult decision?  Uncertainty regarding the future  Conflicting values or objectives  Goal of Decision Analysis: –Help people make good decisions

3. Good Decisions vs Good Outcomes  Good decisions don’t always result in good outcomes  Not necessarily the fault of the decision maker  A structured approach to decision making should lead to more good outcomes than if decisions were made haphazardly

4. Characteristics  Decisions  Alternatives  States of nature

5. Decisions  Decision must involve at least 2 alternatives –Alternative: course of action intended to solve a problem

6. Alternatives  Alternatives are evaluated on the basis of the value added to one or more decision criteria –Criteria: various factors that are important to the decision maker and influenced by the alternatives

7. States of Nature  States of nature correspond to future events that are not under the decision maker’s control

8. Magnolia Inns Example  Decision Alternatives –Buy at A –Buy at B –Buy at A & B –Buy nothing  States of Nature –Airport built at A –Airport built at B

9. Magnolia Inns Cash Flow

10. Payoff Matrix

11. Maximax Decision Rule  Alternative with largest payoff

12. Maximin  Alternative with largest minimum payoff –Hedge against worst possible outcome

13. Expected Monetary Value (EMV)  Decision alternative with largest EMV  Example: 40% chance A, 60% chance B

14. Sensitivity Analysis

15. Expected Value of Perfect Info  EV of PI = EV with PI – max EMV

16. Decision Trees  Decision nodes  Event nodes  Terminal nodes  Magnolia Inns Example (page 731)

17. TreePlan  Excel add-in from Prof. Mike Middleton