1. Chapter 5: Decision-making Concepts Quantitative Decision Making with Spreadsheet Applications 7 th ed. By Lapin and Whisler Sec 5.5 : Other Decision Criteria Sec 5.6: Opportunity Loss and the Expected Value of Perfect Information

2. The Maximin Payoff Criterion The maximin payoff criterion is a procedure that guarantees that the decision maker can do no worse than achieve the best of the poorest outcomes. The maximin payoff criterion is a procedure that guarantees that the decision maker can do no worse than achieve the best of the poorest outcomes.

3. Example: Tippi-Toes Payoff Table Event (level of demand) Act (choice of movement) Gears and Levers Spring Action Weights and Pulleys Light$25,000-$10,000-$125,000 Moderate$400,000$440,000$400,000 Heavy$650,000$740,000$750,000

4. Example Goal: Ensure a favorable outcome no matter what happens. Goal: Ensure a favorable outcome no matter what happens. Determine the worst outcome for each act regardless of the event. Determine the worst outcome for each act regardless of the event. Gears and Levers Light demand $25,000 Spring Action Light demand -$10,000 Weights and Pulleys Light demand -$125,000

5. Example Choose an act with the largest lowest payoff. This guarantees a minimum return that is the best of the poorest outcomes possible. Choose an act with the largest lowest payoff. This guarantees a minimum return that is the best of the poorest outcomes possible. Gears and Levers will guarantee the toy manufacturer a payoff of at least $25,000. Gears and Levers will guarantee the toy manufacturer a payoff of at least $25,000. Gears and Levers is the maximin payoff act. Gears and Levers is the maximin payoff act.

6. Example: Tippi-Toes Payoff Table Event (level of demand) Act (choice of movement) Gears and Levers Spring Action Weights and Pulleys Light$25,000-$10,000-$125,000 Moderate$400,000$440,000$400,000 Heavy$650,000$740,000$750,000 Column Minimums $25,000-$10,000-$125,000

7. Risk vs. Reward EventAct A1A1A1A1 A2A2A2A2 E1E1E1E1$0-$1 E2E2E2E2110,000 Column Minimums $0-$1EventAct B1B1B1B1 B2B2B2B2 E1E1E1E1$1$10,000 E2E2E2E2 - 10,000 Column Minimums -$1-$10,000

8. Deficiencies of Maximin Payoff Criterion It is an extremely conservative decision criterion and may lead to some bad decisions. It is an extremely conservative decision criterion and may lead to some bad decisions. It is primarily suited to decision problems with unknown probabilities that cannot be reasonably assessed. It is primarily suited to decision problems with unknown probabilities that cannot be reasonably assessed.

9. The Maximum Likelihood Criterion The maximum likelihood criterion focuses on the most likely event to the exclusion of all others. The maximum likelihood criterion focuses on the most likely event to the exclusion of all others.

10. Event (level of demand) Probability Act (Choice of Movement) Gears & Levers Spring Action Weights & Pulleys Light.10$25,000-$10,000-$125,000 Moderate.70400,000440,000400,000 Heavy.20650,000740,000750,000 Maximum Likelihood Act

11. Maximum Likelihood Criterion Ignores most of other possible outcomes. Ignores most of other possible outcomes. Prevalent decision-making behavior. Prevalent decision-making behavior.

12. The Criterion of Insufficient Reason Used when decision maker has no information about the event probabilities. Used when decision maker has no information about the event probabilities. Assumes each event has a probability of 1/(number of events) of occuring. Assumes each event has a probability of 1/(number of events) of occuring. Some knowledge of the probability of an event is almost always available. Some knowledge of the probability of an event is almost always available.

13. The Bayes Decision Rule The Bayes decision rule chooses the act maximizing expected payoff. The Bayes decision rule chooses the act maximizing expected payoff. It makes the greatest use of all available information. It makes the greatest use of all available information. Its major deficiency occurs when alternatives involve different magnitudes of risk. Its major deficiency occurs when alternatives involve different magnitudes of risk.

14. EventProbability Act C 1 Act C 2 Payoff Payoff x Prob Payoff E1E1E1E1.5-$1,000,000-$500,000$250,000$125,000 E2E2E2E2.52,000,0001,000,000750,000375,000 Expected Payoff $500,000$500,000

15. Opportunity Loss Opportunity loss is the amount of payoff that is forgone by not selecting the act that has the greatest payoff for the event that actually occurs. Opportunity loss is the amount of payoff that is forgone by not selecting the act that has the greatest payoff for the event that actually occurs. To calculate opportunity losses the maximum payoff for each row is determined and it’s then subtracted from its respective row maximum. To calculate opportunity losses the maximum payoff for each row is determined and it’s then subtracted from its respective row maximum.

16. Event (level of demand) Payoff Row Maximum Gears & Levers Spring Action Weights & Pulleys Light$25,000-$10,000-$125,000$25,000 Moderate400,000440,000400,000440,000 Heavy650,000740,000750,000750,000 Row maximum-Payoff = Opportunity Loss (in thousands of dollars) Light25-25=025-(-10)=3525-(-125)=150 Moderate440-400=40440-440=0440-400=40 Heavy750-650=100750-740=10750-750=0

17. Opportunity Loss Table Event (level of demand) Act (choice of movement) Gears & Levers Spring Action Weights & Pulleys Light$0$35,000150,000 Moderate40,000040,000 Heavy100,00010,0000

18. The Bayes Decision Rule and Opportunity Loss The Bayes decision rule is to select the act that has the maximum expected payoff or the minimum expected opportunity loss. The Bayes decision rule is to select the act that has the maximum expected payoff or the minimum expected opportunity loss.

19. Event (level of demand) Probability Act (choice of movement) Gears & Levers Spring Action Weights & Pulleys Loss Loss x Prob Loss Loss Light.10$0$0$35,000$3,500$150,000$15,000 Moderate.7040,00028,0000040,00028,000 Heavy.20100,00020,00010,0002,00000 Expected Opportunity Loss $48,000$5,500$43,000

20. The Expected Value of Perfect Information When the decision maker can acquire perfect information the decision will be made under certainty. Then the decision maker can guarantee the best decision. When the decision maker can acquire perfect information the decision will be made under certainty. Then the decision maker can guarantee the best decision. We want to investigate the worth of such information before it is obtained, so we will determine the expected payoff once perfect information is obtained. We want to investigate the worth of such information before it is obtained, so we will determine the expected payoff once perfect information is obtained. This quantity is called the expected payoff under certainty. This quantity is called the expected payoff under certainty.

21. Calculating Expected Payoff Under Certainty 1. Determine the highest payoff for each event. 2. Multiply the maximum payoffs with their respective event probabilities. Then sum these amounts. 3. Determine the worth of perfect information to the decision maker.

22. Example: Highest Payoff for each Event Event(l evel of deman d) Probabi lity Act Under Certainty Gears & Levers Spring Action Weights & Pulleys Maxim um Payoff Chosen Act Payoff x Prob Light.10$25,000-$10,000-$125,000$25,000G&L$2,500 Moderate.70400,000440,000400,000440,000SA308,000 Heavy.20650,000740,000750,000750,000W&P150,000 Expected Payoff under certainty 460,500

23. Expected Value of Perfect Information (EVPI) EVPI = Expected payoff under certainty EVPI = Expected payoff under certainty - Maximum expected payoff. Our example: Our example: EVPI = $460,500-$455,000 = $5,500. EVPI = $460,500-$455,000 = $5,500. This is the greatest amount of money the decision maker would be willing to pay to obtain perfect information about what demand will be. This is the greatest amount of money the decision maker would be willing to pay to obtain perfect information about what demand will be.