# 1 1 Slide © 2000 South-Western College Publishing/ITP Slides Prepared by JOHN LOUCKS.

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1 1 Slide © 2000 South-Western College Publishing/ITP Slides Prepared by JOHN LOUCKS

2 2 Slide DECISION ANALYSIS

3 3 Slide Decision Analysis n Structuring the Decision Problem / Developing a Decision Strategy n Decision Making Without Probabilities n Decision Making with Probabilities n Expected Value of Perfect Information n Decision Analysis with Sample Information n Expected Value of Sample Information

4 4 Slide Structuring the Decision Problem n A decision problem is characterized by decision alternatives, states of nature, and resulting payoffs. The decision alternatives are the different possible strategies the decision maker can employ. The decision alternatives are the different possible strategies the decision maker can employ. The states of nature refer to future events, not under the control of the decision maker, which may occur. States of nature should be defined so that they are mutually exclusive and collectively exhaustive. The states of nature refer to future events, not under the control of the decision maker, which may occur. States of nature should be defined so that they are mutually exclusive and collectively exhaustive. For each decision alternative and state of nature, there is a resulting payoff. These are often represented in mat rix form called a payoff table. For each decision alternative and state of nature, there is a resulting payoff. These are often represented in mat rix form called a payoff table.

5 5 Slide Decision Trees n A decision tree is a chronological representation of the decision problem. n Each decision tree has two types of nodes: round nodes correspond to the states of nature - square nodes correspond to the decision alternatives. n The branches leaving round nodes represent the different states of nature while the branches leaving square nodes represent different decision alternatives. n At the end of each limb of a tree are the payoffs attained from the series of branches making up that limb.

6 6 Slide Decision Making Under Uncertainty n If the decision maker does not know with certainty which state of nature will occur, then he is said to be doing decision making under uncertainty. n Three commonly used criteria for decision making under uncertainty when probability information regarding the likelihood of the states of nature is unavailable are: the optimistic approachthe optimistic approach the conservative approachthe conservative approach the minimax regret approach.the minimax regret approach.

7 7 Slide The Optimistic Approach n The optimistic approach would be used by an optimistic decision maker. n The decision with the largest possible payoff is chosen. n If the payoff table was in terms of costs, the decision with the lowest possible cost is chosen.

8 8 Slide The Conservative Approach n The conservative approach would be used by a conservative decision maker. n For each decision the minimum payoff is listed and then the decision corresponding to the maximum of these minimum payoffs is selected. (Hence, the minimum possible payoff is maximized.) n If the payoff was in terms of costs, the maximum costs would be determined for each decision and then the decision corresponding to the minimum of these maximum costs is selected. (Hence, the maximum possible cost is minimized.)

9 9 Slide The Minimax Regret Approach n The minimax regret approach requires the construction of a regret table or an opportunity loss table. n This is done by calculating for each state of nature the difference between each payoff and the largest payoff for that state of nature. n Then, using this regret table, the maximum regret for each possible decision is listed. n The decision chosen is the one corresponding to the minimum of the maximum regrets.

10 Slide Expected Value Approach n If probabilistic information regarding the states of nature is available, one may use the expected value (EV) approach. n Here the expected return for each decision is calculated by summing the products of the payoff under each state of nature and the probability of the respective state of nature occurring. n The decision yielding the best expected return is chosen.

11 Slide Expected Value of Perfect Information - EVPI n Frequently information is available which can improve the probability estimates for the states of nature. n The expected value of perfect information (EVPI) is the increase in the expected profit that would result if one knew with certainty which state of nature would occur. n The EVPI provides an upper bound on the expected value of any sample or survey information.

12 Slide Expected Value of Perfect Information - EVPI n EVPI Calculation Step 1:Step 1: Determine the optimal return corresponding to each state of nature. Determine the optimal return corresponding to each state of nature. Step 2:Step 2: Compute the expected value of these optimal returns. Compute the expected value of these optimal returns. Step 3:Step 3: Subtract the EV of the optimal decision from the amount determined in Step 2. Subtract the EV of the optimal decision from the amount determined in Step 2.

13 Slide Bayes’ Theorem and Posterior Probabilities n Knowledge of sample or survey information can be used to revise the probability estimates for the states of nature. n Prior to obtaining this information, the probability estimates for the states of nature are called prior probabilities. n With knowledge of conditional probabilities for the outcomes or indicators of the sample or survey information, these prior probabilities can be revised by employing Bayes' Theorem. n The outcomes of this analysis are called posterior probabilities.

14 Slide Posterior Probabilities n Posterior Probabilities Calculation Step 1:Step 1: For each state of nature, multiply the prior probability by its conditional probability for the indicator -- this gives the joint probabilities for the states and indicator. For each state of nature, multiply the prior probability by its conditional probability for the indicator -- this gives the joint probabilities for the states and indicator. Step 2:Step 2: Sum these joint probabilities over all states -- this gives the marginal probability for the indicator. Sum these joint probabilities over all states -- this gives the marginal probability for the indicator. Step 3:Step 3: For each state, divide its joint probability by the marginal probability for the indicator -- this gives the posterior probability distribution. For each state, divide its joint probability by the marginal probability for the indicator -- this gives the posterior probability distribution.

15 Slide Expected Value of Sample Information - EVSI n The expected value of sample information (EVSI) is the additional expected profit possible through knowledge of the sample or survey information.

16 Slide Expected Value of Sample Information n EVSI Calculation Step 1:Step 1: Determine the optimal decision and its expected return for the possible outcomes of the sample or survey using the posterior probabilities for the states of nature. Determine the optimal decision and its expected return for the possible outcomes of the sample or survey using the posterior probabilities for the states of nature. Step 2:Step 2: Compute the expected value of these optimal returns. Compute the expected value of these optimal returns. Step 3:Step 3: Subtract the EV of the optimal decision obtained without using the sample information from the amount determined in Step 2. Subtract the EV of the optimal decision obtained without using the sample information from the amount determined in Step 2.

17 Slide Efficiency of Sample Information n Efficiency of sample information is the ratio of EVSI to EVPI. n As the EVPI provides an upper bound for the EVSI, efficiency is always a number between 0 and 1.

18 Slide PROBLEMS / EXAMPLES

19 Slide Example Consider the following problem with three decision alternatives and three states of nature with the following payoff table representing profits: States of Nature States of Nature s 1 s 2 s 3 s 1 s 2 s 3 d 1 4 4 -2 d 1 4 4 -2 Decisions d 2 0 3 -1 Decisions d 2 0 3 -1 d 3 1 5 -3 d 3 1 5 -3

20 Slide Example n Optimistic Approach An optimistic decision maker would use the optimistic approach. All we really need to do is to choose the decision that has the largest single value in the payoff table. This largest value is 5, and hence the optimal decision is d 3. Maximum Maximum Decision Payoff Decision Payoff d 1 4 d 1 4 d 2 3 d 2 3 choose d 3 d 3 5 maximum choose d 3 d 3 5 maximum

21 Slide Example n Formula Spreadsheet for Optimistic Approach

22 Slide Example n Spreadsheet for Optimistic Approach

23 Slide Example n Conservative Approach A conservative decision maker would use the conservative approach. List the minimum payoff for each decision. Choose the decision with the maximum of these minimum payoffs. Minimum Minimum Decision Payoff Decision Payoff d 1 -2 d 1 -2 choose d 2 d 2 -1 maximum choose d 2 d 2 -1 maximum d 3 -3 d 3 -3

24 Slide Example n Formula Spreadsheet for Conservative Approach

25 Slide Example n Spreadsheet for Conservative Approach

26 Slide Example n Minimax Regret Approach For the minimax regret approach, first compute a regret table by subtracting each payoff in a column from the largest payoff in that column. In this example, in the first column subtract 4, 0, and 1 from 4; in the second column, subtract 4, 3, and 5 from 5; etc. The resulting regret table is: s 1 s 2 s 3 s 1 s 2 s 3 d 1 0 1 1 d 1 0 1 1 d 2 4 2 0 d 2 4 2 0 d 3 3 0 2 d 3 3 0 2

27 Slide Example n Minimax Regret Approach (continued) For each decision list the maximum regret. Choose the decision with the minimum of these values. Decision Maximum Regret Decision Maximum Regret choose d 1 d 1 1 minimum choose d 1 d 1 1 minimum d 2 4 d 2 4 d 3 3 d 3 3

28 Slide Example n Formula Spreadsheet for Minimax Regret Approach

29 Slide Example n Spreadsheet for Minimax Regret Approach

30 Slide Example n Decision Tree Using a decision tree to find the optimal decision if P( s 1 ) =.20, P( s 2 ) =.50, P( s 3 ) =.30: 11 4 4 -2 -2 0 3 1 5 -3 d1d1d1d1 d2d2d2d2 d3d3d3d3 s3s3s3s3 Payoffs 44 22 33 s2s2s2s2 s1s1s1s1 s1s1s1s1 s3s3s3s3 s2s2s2s2 s1s1s1s1 s3s3s3s3 s2s2s2s2

31 Slide Example n Expected Value To calculate the expected values at nodes 2, 3, and 4, multiply the payoffs by the corresponding probabilities and then sum. The decision with the maximum expected value of 2.2, d 1, is then chosen. EV(Node 2) =.20(4) +.50(4) +.30(-2) = EV(Node 3) =.20(0) +.50(3) +.30(-1) = 1.2 EV(Node 4) =.20(1) +.50(5) +.30(-3) = 1.8 2.22.2

32 Slide Example n Expected Value of Perfect Information The EVPI is calculated by multiplying the maximum payoff for each state by the corresponding probability, summing these values, and then subtracting the expected value of the optimal decision from this sum. Thus, the EVPI = [.20(4) +.50(5) +.30(-1)] - 2.2 =.8

33 Slide Example: Burger Prince Burger Prince Restaurant is contemplating opening a new restaurant on Main Street. It has three different models, each with a different seating capacity. Burger Prince estimates that the average number of customers per hour will be 80, 100, or 120. The payoff table for the three models is as follows: Average Number of Customers Per Hour Average Number of Customers Per Hour s 1 = 80 s 2 = 100 s 3 = 120 s 1 = 80 s 2 = 100 s 3 = 120 Model A \$10,000 \$15,000 \$14,000 Model A \$10,000 \$15,000 \$14,000 Model B \$ 8,000 \$18,000 \$12,000 Model B \$ 8,000 \$18,000 \$12,000 Model C \$ 6,000 \$16,000 \$21,000 Model C \$ 6,000 \$16,000 \$21,000

34 Slide Example: Burger Prince n Expected Value Approach Calculate the expected value for each decision. The decision tree on the next slide can assist in this calculation. Here d 1, d 2, d 3 represent the decision alternatives of models A, B, C, and s 1, s 2, s 3 represent the states of nature of 80, 100, and 120.

35 Slide Example: Burger Prince n Decision Tree 11.2.4.4.4.2.4.4.2.4 d1d1d1d1 d2d2d2d2 d3d3d3d3 s1s1s1s1 s1s1s1s1 s1s1s1s1 s2s2s2s2 s3s3s3s3 s2s2s2s2 s2s2s2s2 s3s3s3s3 s3s3s3s3 Payoffs 10,000 15,000 14,000 8,000 18,000 12,000 6,000 16,000 21,000 22 33 44

36 Slide Example: Burger Prince n Expected Value For Each Decision Choose the model with largest EV -- Model C. 33 44 d1d1d1d1 d2d2d2d2 d3d3d3d3 EMV =.4(10,000) +.2(15,000) +.4(14,000) = \$12,600 = \$12,600 EMV =.4(8,000) +.2(18,000) +.4(12,000) = \$11,600 = \$11,600 EMV =.4(6,000) +.2(16,000) +.4(21,000) = \$14,000 = \$14,000 Model A Model B Model C 22 11

37 Slide Example: Burger Prince n Formula Spreadsheet for Expected Value Approach

38 Slide Example: Burger Prince n Spreadsheet for Expected Value Approach

39 Slide Example: Burger Prince n Expected Value of Perfect Information Calculate the expected value for the optimum payoff for each state of nature and subtract the EV of the optimal decision. Calculate the expected value for the optimum payoff for each state of nature and subtract the EV of the optimal decision. EVPI=.4(10,000) +.2(18,000) +.4(21,000) - 14,000 = \$2,000 EVPI=.4(10,000) +.2(18,000) +.4(21,000) - 14,000 = \$2,000

40 Slide Example: Burger Prince n Spreadsheet for Expected Value of Perfect Information

41 Slide Example: Burger Prince n Sample Information Burger Prince must decide whether or not to purchase a marketing survey from Stanton Marketing for \$1,000. The results of the survey are "favorable" or "unfavorable". The conditional probabilities are : P(favorable ¦ 80 customers per hour) =.2 P(favorable ¦ 100 customers per hour) =.5 P(favorable ¦ 120 customers per hour) =.9 Should Burger Prince have the survey performed by Stanton Marketing?

42 Slide Example: Burger Prince n Posterior Probabilities Favorable State Prior Conditional Joint Posterior State Prior Conditional Joint Posterior 80.4.2.08.148 80.4.2.08.148 100.2.5.10.185 100.2.5.10.185 120.4.9.36.667 120.4.9.36.667 Total.54 1.000 Total.54 1.000 P(favorable) =.54 P(favorable) =.54

43 Slide Example: Burger Prince n Posterior Probabilities Unfavorable State Prior Conditional Joint Posterior State Prior Conditional Joint Posterior 80.4.8.32.696 80.4.8.32.696 100.2.5.10.217 100.2.5.10.217 120.4.1.04.087 120.4.1.04.087 Total.46 1.000 Total.46 1.000 P(unfavorable) =.46 P(unfavorable) =.46

44 Slide Example: Burger Prince n Formula Spreadsheet for Posterior Probabilities

45 Slide Example: Burger Prince n Spreadsheet for Posterior Probabilities

46 Slide Example: Burger Prince n Decision Tree (top half) s 1 (.148) s 2 (.185) s 3 (.667) \$10,000 \$15,000 \$14,000 \$8,000 \$18,000 \$12,000 \$6,000 \$16,000 \$21,000 I 1 I 1(.54) d1d1d1d1 d2d2d2d2 d3d3d3d3 22 44 55 66 11

47 Slide Example: Burger Prince n Decision Tree (bottom half) s 1 (.696) s 2 (.217) s 3 (.087) \$10,000 \$15,000 \$18,000 \$14,000 \$8,000 \$12,000 \$6,000 \$16,000 \$21,000 I 2 I 2(.46) d1d1d1d1 d2d2d2d2 d3d3d3d3 77 99 88 33 11

48 Slide Example: Burger Prince I 2 I 2(.46) d1d1d1d1 d2d2d2d2 d3d3d3d3 EMV =.696(10,000) +.217(15,000) +.087(14,000)= \$11,433 +.087(14,000)= \$11,433 EMV =.696(8,000) +.217(18,000) +.087(12,000) = \$10,554 +.087(12,000) = \$10,554 EMV =.696(6,000) +.217(16,000) +.087(21,000) = \$9,475 +.087(21,000) = \$9,475 I 1 I 1(.54) d1d1d1d1 d2d2d2d2 d3d3d3d3 EMV =.148(10,000) +.185(15,000) +.667(14,000) = \$13,593 +.667(14,000) = \$13,593 EMV =.148 (8,000) +.185(18,000) +.667(12,000) = \$12,518 +.667(12,000) = \$12,518 EMV =.148(6,000) +.185(16,000) +.667(21,000) = \$17,855 +.667(21,000) = \$17,855 44 55 66 77 88 99 22 33 11 \$17,855 \$11,433

49 Slide Example: Burger Prince n Expected Value of Sample Information If the outcome of the survey is "favorable" choose Model C. If it is unfavorable, choose Model A. EVSI =.54(\$17,855) +.46(\$11,433) - \$14,000 = \$900.88 Since this is less than the cost of the survey, the survey should not be purchased.

50 Slide Example: Burger Prince n Efficiency of Sample Information The efficiency of the survey: EVSI/EVPI = (\$900.88)/(\$2000) =.4504

51 Slide The End