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Chapter 3 Decision Analysis

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Decision Theory Decision theory is the analytic and systematic approach for making the best decision.

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Features of Decision Making Decision making is for __________. a. past b. futurec. both past and future A decision is about a (an) _________. a.statusb. actionc. condition The process of making decision is a process of __________. a.producingb. manufacturingc. creating d. cookinge. selectingf. fabricating

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Components in Decision Making (1 of 2) Alternatives of a decision –A list of choices, one of which will be selected as the decision by the decision maker. States of Nature –Possible conditions that may actually occur in the future, which will affect the outcome of your decision but are beyond your control.

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Components in Decision Making (2 of 2) Payoffs –a payoff is the outcome of a decision alternative under a state of nature. The larger the payoff the better. The decision alternatives, states of nature and payoffs are organized in a decision table.

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Decision Table for the Thompson Lumber Example Decision Alternatives States of Nature Favorable Market Unfavorable Market Build a large plant$200,000-$180,000 Build a small plant$100,000-$20,000 Doing nothing$0

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Types of Decision Making Decision making under certainty –The outcome of a decision alternative is known (i.e., there is only one state of nature.) Decision making under risk –The outcome of a decision alternative is not known, but its probability is known. Decision making under uncertainty –The outcome of a decision alternative is not known, and even its probability is not known.

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Decision Making under Uncertainty The outcome of a decision alternative is not known, and even its probability is not known. A few criteria (approaches) are available for the decision makers to select according to their preferences and personalities.

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Criterion 1: Maximax (Optimistic) Step 1. Pick maximum payoff of each alternative. Step 2. Pick maximum of those maximums in Step 1; its corresponding alternative is the decision. “Best of bests”.

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Maximax Decision for Thompson Lumber States of Nature Row Decision Favorable Unfavorable Maximum Alternatives market market Large plant 200,000–180,000200,000 Small plant 100,000 –20,000100,000 Do nothing Max(Row max’s) = Max(200,000, 100,000, 0) = 200,000. So, the decision is ‘Large plant’.

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For Whom? MaxiMax is an approach for: –Risk taker who tends not to give up attractive opportunities regardless of possible failures, or –Optimistic decision maker in whose eyes future is bright.

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Criterion 2: Maximin (Pessimistic) Step 1. Pick minimum payoff of each alternative Step 2. Pick the maximum of those minimums in Step 1, its corresponding alternative is the decision “Best of worsts”

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Maximin Decision for Thompson Lumber States of Nature Row Decision Favorable Unfavorable Minimum Alternatives market marketpayoffs Large plant 200,000–180,000 –180,000 Small plant 100,000 –20,000 –20,000 Do nothing Max(Row Min’s) = Max( –180,000, –20,000, 0) = 0. So, the decision is ‘do nothing’.

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For Whom? MaxiMin is an approach for: –Risk averter who tends to avoid bad outcomes despite of some possible attractive outcomes; or –Pessimistic decision maker in whose eyes future is obscure.

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Criterion 3: Hurwicz (Realism) Step 1. Calculate Hurwicz value for each alternative Step 2. Pick the alternative of largest Hurwicz value as the decision.

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Hurwicz Value Hurwicz value of an alternative = (row max)( ) + (row min)(1- ) where (0 1) is called coefficient of realism.

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Decision by Hurwicz Value for Thompson Lumber =0.8 States of Nature Decision Favorable Unfavorable Hurwicz Alternatives market market values Large plant 200,000 –180, ,000 Small plant 100,000 –20,000 76,000 Do nothing Max(Hurwicz values) = Max(124, 000,76, 000,0) = 124,000. So, the decision is ‘large plant’.

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For Whom? Hurwicz method can be used by decision makers with different preferences on risks. –For a person who tends to take risk, a larger is used; –For a person who tends to be conservative, a smaller is used. What if = 1? What if = 0?

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Criterion 4: Equally Likely Step 1. Calculate the average payoff for each alternative. Step 2. The alternative with highest average if the decision.

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Decision by Equally Likely for Thompson Lumber States of Nature Row Decision Favorable Unfavorable Average Alternatives market market Large plant 200,000 –180,000 10,000 Small plant 100,000 –20,000 40,000 Do nothing Max(Row avg’s) = Max(10, 000, 40, 000, 0) = 40,000. So, the decision is ‘small plant’.

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For Whom? Equally Likely method is for the decision maker who does not have particular preference on taking or avoiding risks.

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Criterion 5: Minimax Regret Step 1. Construct a ‘regret table’, Step 2. Pick maximum regret of each row in regret table, Step 3. Pick minimum of those maximums in Step 2, its corresponding alternative is the decision.

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Regret Regret is amount you give up due to not picking the best alternative in a given state of nature. Regret = Opportunity cost = Opportunity loss

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Payoff Table for Thompson Lumber and Column Maximums States of Nature Decision FavorableUnfavorable Alternatives market market Large plant $200,000-$180,000 Small plant $100,000 -$20,000 Doing nothing $0 $0 Column Max$200,000 $0

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Regret Table for Thompson Lumber States of Nature Decision FavorableUnfavorable Alternatives market market Large plant $0 $180,000 Small plant $100,000 $20,000 Doing nothing $200,000 $0

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Minimax Regret Decision for Thompson Lumber Regret Table States of Nature Decision Favorable Unfavorable Row Alternatives market market Maximum Large plant 0 180, ,000 Small plant 100,000 20, ,000 Do nothing 200, ,000 Min(Row max’s) = Min{180,000, 100,000, 200,000} = 100,000. So, the minimax regret decision is ‘small plant’.

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For Whom? MiniMax Regret is an approach for the decision maker who hates the feeling of having regrets.

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Decision Making under Risk The outcome of a decision alternative is not known, but its probability is known.

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Max EMV Approach Step 1. Calculate EMV for each alternative. Step 2. Pick the alternative with highest EMV as the decision.

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EMV – expected monetary value EMV of an alternative is the expected value of possible payoffs of that alternative. EMV n=number of states of nature P(X i )=probability of the i-th state of nature X i =payoff of the alternative under the i-th state of nature

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Example of Thompson Lumber States of Nature Decision Favorable Unfavorable Alternatives market marketEMV Large plant $200,000 -$180,00010,000 Small plant $100,000 -$20,00040,000 Doing nothing $0 $0 0

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Minimum EOL Approach Step 1. Generate the opportunity loss table. Step 2. Calculate the expected value (EOL) for each alternative in the opportunity loss table. Step 3. Pick up the alternative with the minimum EOL.

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Opportunity Loss Table Opportunity loss = Regret = Opp. cost Opportunity loss table = Regret Table

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Payoff Table for the Thompson Lumber Example Decision Alternatives States of Nature Favorable Market Unfavorable Market Build a large plant$200,000-$180,000 Build a small plant$100,000-$20,000 Doing nothing$0

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Opportunity Loss table and EOL for Thompson Lumber States of Nature Decision Favorable Unfavorable Alternatives market marketEOL Large plant $0 $180,000 Small plant $100,000 $20,000 Doing nothing $200,000 $0

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Expected Value of Perfect Information (EVPI) It is value of additional information for better decision making. It is an upper bound on how much to pay for the additional information.

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Calculating EVPI EVPI = (Exp. payoff with perfect information) – (Exp. payoff without perfect information) = EV w PI – EV w/o PI

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EV w/o PI EV w/o PI is the average payoff you expect to get based only on the information given in the decision table without the help of additional information. EV w/o PI = Max (EMV)

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EV w/o PI = Maximum EMV States of Nature Decision Favorable Unfavorable Alternatives market marketEMV Large plant $200,000 -$180,00010,000 Small plant $100,000 -$20,00040,000 Doing nothing $0 $0 0 Since Max EMV = 40,000, EV w/o PI = 40,000.

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EV w PI EV w PI is the average payoff you can get if following the perfect information about the state of nature in the future. EV w PI where n=number of states of nature b i =best payoff of i-th state of nature P i =probability of i-th state of nature

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EV w PI for the Example of Thompson States of Nature Decision Favorable Unfavorable Alternatives market marketEMV Large plant $200,000 -$180,00010,000 Small plant $100,000 -$20,00040,000 Doing nothing $0 $0 0 b i $200,000 $0 EV w PI = 200,000*0.5+0*0.5 = 100,000

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EVPI for Thompson Lumber EV w PI = 200,000* *0.50 = $100,000 EV w/o PI = Maximum EMV = $40,000 EVPI = EV w PI – EV w/o PI = $100,000 – $40,000 = $60,000

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EVPI is a Benchmark in Purchasing Additional Information EVPI is the maximum $ amount the decision maker would pay to purchase the additional information about the states of nature (from a consulting firm, for example).

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What if Information Is Not Perfect? In most cases, information about future is not “perfect”. We need to discount EV w PI properly in those cases. If you have 80% of confidence on the information, then Expected Value of Additional Information = EVAI = EVwPI * 80% - EVw/oPI

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Maximum EMV, Minimum EOL, and EVPI The decision selected by the Maximum EMV approach is always the same as the decision selected by the Minimum EOL approach. (why?) The value of EVPI is equal to the value of minimum EOL. (why?)

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An Example You can play the game for many times. Someone offers you perfect information about “landing” at the price of $65 per time. Do you take it? If not, how much would you pay? (See the handout of class work) Land on ‘Head’Land on ‘Tail’ Guess ‘Head’$100- $60 Guess ‘Tail’- $80$150

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In the Tossing Coin Example EMV for “guess Head” = $20. EMV for “guess Tail” = $35 * (Max EMV). EOL for “guess Head” = $105 EOL for “guess Tail” = $90 * (Min EOL) EV w PI = $125, EV w/o PI=$35 EVPI = $90

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Maximum average payoff per game Alt. 1, Guess “Head” Alt. 2, Guess “Tail” EMV regret average payoff average payoff EOL Alternatives $

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How to Set Up a Decision Table A decision table is set up by the decision maker. Determine decision alternatives and states of nature. Determine the payoffs of each alternative under the states of nature. See case “Garden Salad” in class work.

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