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Decision Analysis Chapter 15: Hillier and Lieberman Dr. Hurley’s AGB 328 Course

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Terms to Know Alternative, State of Nature, Payoff, Payoff Table, Prior Distribution, Prior Probabilities, Maximin Payoff Criterion, Maximum Likelihood Criterion, Bayes’ Decision Rule, Crossover Point, Posterior Probabilities, Probability Tree Diagram, Expected Value of Perfect Information, Expected Value of Experimentation, Nodes, Branches, Decision Node, Event Node

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Terms to Know Cont. Backward Induction Procedure, Spider Chart, Tornado Chart, Utility Function for Money, Decreasing Marginal Utility for Money, Risk Averse, Increasing Marginal Utility of Money, Risk Neutral, Risk Seekers, Exponential Utility Function

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Goferbroke Company Example Trying to maximize payoff from land that may have oil given The company has two options: drill or sell the land If the company drills for oil and oil exists, they expect a payoff of $700K If the company drills for oil and oil does not exist, they expect a payoff of -$100K If the company sells the land it receives $90K whether the oil exists or not There is a 1 in 4 chance that oil exists

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Payoff Table for Goferbroke Nature AlternativesOil ExistsOil Does Not Exist Drill$700K-$100K Sell$90K Prior Probability25%75%

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Maximin Payoff Criterion This criterion identifies the worst payoff for each decision that you could make and maximizes the highest of these amounts ◦ For Goferberoke this would be to sell the land This criterion is for the very cautious

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Maximum Likelihood Criterion This criterion requires you to select the best payoff from the highest likelihood state of nature For Goferbroke, the best decision based on this criterion is to sell the land

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Bayes’ Decision Rule This criterion calculates the expected value of each decision and then chooses the maximum of these expected values For Goferbroke, the expected payoff for drilling is 100K while for selling it is 90K A nice attribute about Bayes decision rule is that you can conduct a sensitivity analysis to find what probability would cause you to change your decision from the given prior probabilities ◦ You can do this by finding the probability that will cause one decisions expected payoff to equal another decisions expected payoff

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Bayes Theorem

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Bayes Theorem Using a Tree Diagram P(A 1 ) P(A 2 ) P(B 2 |A 1 ) P(B 1 |A 1 ) P(B 1 |A 2 ) P(B 2 |A 2 ) P(B 1 |A 1 )P(A 1 ) P(B 2 |A 1 )P(A 1 ) P(B 1 |A 2 )P(A 2 ) P(B 2 |A 2 )P(A 2 )

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Using Bayes Theorem Using a Tree Diagram for Goferrbroke Let the probability of finding oil be 25% and 75% for not finding oil given no prior information Let the probability of finding oil be 60% given information that is favorable to finding oil Let the probability of finding oil be 40% given information that is not favorable to finding oil

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Using Bayes Theorem Using a Tree Diagram for Goferrbroke Cont. Let the probability of not finding oil be 20% given information that is favorable to finding oil Let the probability of not finding oil be 80% given information that is not favorable to finding oil

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Using Bayes Theorem Using a Tree Diagram for Goferrbroke P(Oil)=0.25 P(No Oil)=0.75 P(Unfavorable |Oil)=0.4 P(Favorable |Oil)=0.6 P(Favorable |No Oil)= 0.2 P(Unfavorable |No Oil)=0.8 P(Favorable|Oil)P(Oil) = 0.25*0.6 = 0.15 P(Unfavorable|Oil)P(Oil) = 0.25*0.4 = 0.1 P(Favorable|No Oil)P(No Oil) = 0.75*0.2 = 0.15 P(Unfavorable |No Oil)P(No Oil) = 0.75*0.8 = 0.6

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In-Class Activity (Not Graded) What are: ◦ P(Oil|Unfavorable) ◦ P(No Oil|Favorable) ◦ P(No Oil|Unfavorable)

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Calculating Expected Payoffs of the Alternatives Given Information from Seismic Study

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Expected Payoff with Perfect Information (EPPI) ◦ EPPI calculates the expected value of the decisions made given perfect information This measures assumes that you will have chosen the best alternative given the state of nature that occurs Hence you will multiply the probability of the state of nature by the best payoff achievable in that state For the Goferbroke example, if oil exists you would choose to drill receiving 700 and if oil does not exists you would choose to sell receiving 90 ◦ Goferbroke’s EPPI = 0.25*700+0.75*90 = 242.5

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Expected Value of Perfect Information (EVPI) EVPI = Expected Payoff with Perfect Information – Expected Payoff without Perfect Information ◦ Expected Payoff without Perfect Information is just the value you get by using Bayes Decision Rule of maximizing expected payoff Goferbroke’s EVPI = 242.5-100=142.5 If the seismic survey was a perfect indicator, you would choose to do it because the EVPI is greater than the cost of the survey

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Expected Payoff with Experimentation (EPE)

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Expected Value of Experimentation (EVE) EVE = expected payoff with information – expected payoff without experimentation Goferbroke’s EVE = 153 – 100 = 53 ◦ Since this exceeds the cost of the information, Goferbroke would proceed with undergoing the survey

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Decision Trees Decision trees can be a useful tool when examining how to make the optimal decisions when there is multiple alternatives to choose from ◦ In the trees, you have decision nodes which are represented as squares and event/chance nodes that are represented by circles ◦ You also have the payoffs that occur due to a sequence of decision and event nodes occurring To solve these decision trees you work your way from the end of the tree to the beginning of the tree

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Goferbroke’s Decision Tree Example Discussed in class

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In-Class Activity (Not Graded) Do problem 15.2-7 Do Problem 15.4-3

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