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Unformatted text preview: Chapter 14 Decision Analysis – Part 2 Introduction Review Payoff Table development Decision Analysis under Uncertainty Decision Criteria (Risk) Expected Monetary Value (EMV) Select alternative with highest expected payoff Maximum Likelihood Select best of payoffs that are most likely to occur Dominance Models Expected Monetary Value Sum of weighted payoffs associated with a specific alternative EMV (Alt) = EMV (Alt) = Σ Σ [ CP (AltState [ CP (AltState i )P(State )P(State i )] )] i Payoff Table 5 10 15 20 25 5 20 101020 10 5 40 30 20 10 1510 25 60 50 40 2025 10 45 80 70 25405 30 65 100 Demand Stock .1 .2 .1 .4 .2 Probabilities of =Demand Levels; sum = 1 EMV Calculations EMV 5 = .1(20) + .2(10) + .1(0) + .4(10) + .2(20) = EMV 10 = .1(5) + .2(40) + .1(30) + .4(20) + .2(10) = EMV 15 = .1(10) + .2(25) + .1(60) + .4(50) + .2(40) = EMV 20 = .1(25) + .2(10) + .1(45) + .4(80) + .2(70) = EMV 25 = .1(40) + .2(5) + .1(30) + .4(65) + .2(100) = EMV (Alt) = EMV (Alt) = Σ Σ [ CP (AltState [ CP (AltState i )P(State )P(State i )] )] i Payoff Table Best Worst Avg EMV 5 20204 10 40 5 21 21.5 15 6010 33 38 20 8025 36 50 25 10040 30 44 Demand Stock Value of Perfect Information How much would it be worth to us to know the state of nature ahead of time (would we change our decision)? Specifically, how much additional profit could we make if we knew exactly what demand would be? Value of Perfect Information EPPI = Expected Payoff Under Perfect Information EPPP = Expected Payoff with Perfect Prediction EPUC = Expected Payoff Under Certainty Payoff Table 5 10 15 20 25 5 20 101020 10 5 40 30 20 10 1510 25 60 50 40 2025 10 45 80 70 25405 30 65 100 Demand Stock .1 .2 .1 .4 .2 If we knew demand would be 5 shirts, how much would we stock?...
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 Fall '08
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 Decision Analysis, Influence diagram, EMVs

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