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Readings. Readings. Chapter 13 Decision Analysis. Overview. Overview. Overview. Expected Value of Perfect Information. Expected Value of Perfect Information. Expected Value of Perfect Information. Overview

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Readings

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  1. Readings Readings Chapter 13 Decision Analysis

  2. Overview Overview

  3. Overview

  4. Expected Value of Perfect Information Expected Value of Perfect Information

  5. Expected Value of Perfect Information Overview Expected Value of Perfect Information is the increase in the expected profit that would result if one knew with certainty which state of nature would occur. It provides an upper bound on the expected value of any sample or survey information that better estimates the probability estimates for the states of nature.

  6. Expected Value of Perfect Information • Frequently, information is available that can better estimate the probabilities for the states of nature. (For example, you can take the time to listen to the radio to find out about the weather.) • In the extreme, you can find the state of nature with certainty. (For example, you can call the Pepperdine hotline to see if PCH is open.) • 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. • The EVPI provides an upper bound on the expected value of any sample or survey information that better estimates the probability estimates for the states of nature.

  7. Expected Value of Perfect Information • EVPI Calculation • Step 1: Determine the optimal return corresponding to each state of nature. • Step 2: Compute the expected value of those optimal returns. • Step 3: Subtract the EV of the optimal decision without knowing the state of nature from the amount determined in Step 2.

  8. Expected Value of Perfect Information • How much should Burger King be willing to pay to determine the average number of customers per hour (the state of nature)? • The probabilities of states s1, s2, s3 were .4, .2, and .4: • Maximum expected profitwas EV(C) = .4($6,000) + .2($16,000) + .4($21,000) = $14,000 Average Number of Customers Per Hour s1 = 80 s2 = 100 s3 = 120 Model A $10,000 $15,000 $14,000 Model B $ 8,000 $18,000 $12,000 Model C$ 6,000 $16,000 $21,000

  9. Expected Value of Perfect Information • If you knew the state were s1, choose A and earn $10,000. • If you knew the state were s2, choose B and earn $18,000 • If you knew the state were s3, choose C and earn $21,000 • EV = .4(10,000) + .2(18,000) + .4(21,000) = $16,000 • EV(perfect information) is $2,000 than EV(no info). Average Number of Customers Per Hour s1 = 80 s2 = 100 s3 = 120 Model A $10,000 $15,000 $14,000 Model B $ 8,000 $18,000 $12,000 Model C$ 6,000 $16,000 $21,000

  10. Bayes’ Rule Bayes’ Rule

  11. Bayes’ Rule Overview Bayes’ Rule revises prior probability estimates for the states of nature into posterior probabilities. The rule uses conditional probabilities for the outcomes or indicators of the sample or survey information.

  12. Bayes’ Rule • Knowledge of sample (survey) information can better estimate the probabilities for the states of nature. • Before getting this information, probability estimates for the states of nature are called prior probabilities. • 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. (The accuracy of a sample or survey is measured by its conditional probabilities.) • The revised probabilities are called posterior probabilities or branch probabilities for decision trees.

  13. Bayes’ Rule • For example, suppose a certain drug test will correctly identify a drug user as testing positive 99% of the time, and will correctly identify a non-user as testing negative 99% of the time. • Suppose a corporation decides to test its employees for opium use, and the corporation believes 0.5% of the employees use the drug. We want to know the probability that, given a positive drug test, an employee is actually a drug user.

  14. Bayes’ Rule • Let "D" be the event of being a drug user; and "N“, being a non-user. Let "+" be the event of a positive drug test. • The population is defined by P(D), or the prior probability that the employee is a drug user. This is 0.005, since 0.5% of the employees are drug users. Hence, P(N), or the probability that the employee is not a drug user, is 1-P(D), or 0.995. • The accuracy of the drug test is defined by two numbers: • P(+|D), or the probability that the test is positive, given that the employee is a drug user. This is 0.99, since the test will correctly identify a drug user as testing positive 99% of the time. • P(+|N), or the probability that the test is positive, given the employee is not a user. This is 0.01, since the test produces a false positive for 1% of non-users.

  15. Bayes’ Rule Hence, follow a sequence of steps to compute the probability a person is a drug user given a positive test: • The probability a person is a drug user and tests positive is P(D&+) = P(+|D) x P(D) = 0.99 x 0.005 = 0.00495 = 0.495% • The probability a person is a non-drug user but tests positive is P(N&+) = P(+|N) x P(N) = 0.01 x 0.995 = 0.00995 = 0.995% • The probability a person tests positive P(+) = P(D&+) + P(N&+) = 0.0149 = 1.49%. • The probability a person is a drug user given a positive test P(D|+) = P(D&+)/P(+) = 0.00495/0.0149 = .3322. • Even though the test is 99% accurate, P(D|+) = .3322, only 33.22% of those testing positive are drug users!

  16. Expected Value of Sample Information Expected Value of Sample Information

  17. Expected Value of Sample Information Overview Expected Value of Sample Information is the additional expected profit possible through knowledge of the sample or survey information. It is less than the Expected Value of Perfect Information when samples and surveys are imperfect.

  18. Expected Value of Sample Information • In general, the expected value of sample information (EVSI) is the additional expected profit possible through knowledge of the sample or survey information. • EVSI calculation • Step 1: Determine the optimal decision and its expected profit for the possible outcomes of the sample using the posterior probabilities for the states of nature. • Step 2:Compute the expected value of these optimal profits. • Step 3: Subtract the EV of the optimal decision obtained without using the sample information from the amount determined in Step 2.

  19. Expected Value of Sample Information Burger King must decide whether or not to spend $1,000 for a marketing survey from Stanton Marketing. The two possible 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 King spend $1,000 for the survey from Stanton Marketing?

  20. Expected Value of Sample Information Favorable survey result StatePriorConditionalJointPosterior 80 .4 .2 .08 .148 100 .2 .5 .10 .185 120 .4 .9 .36.667 Total .54 1.000 P(favorable) = .54 • For the first state (80 customers), • Joint probability = 0.08 = 0.4 x 0.2 = Prior x Conditional • Marginal P(favorable) = 0.54 = 0.08+0.10+0.36 = S Joints • Posterior probability = 0.148 = 0.08/0.54 = Joint/Marginal

  21. Expected Value of Sample Information Unfavorable survey result StatePriorConditionalJointPosterior 80 .4 .8 .32 .696 100 .2 .5 .10 .217 120 .4 .1 .04.087 Total .46 1.000 P(unfavorable) = .46 • For the first state (80 customers), • Joint probability = 0.32 = 0.4 x 0.8 = Prior x Conditional • Marginal P(unfavorable) = 0.46 = 0.32+0.10+0.04 = S Joints • Posterior probability = 0.696 = 0.32/0.46 = Joint/Marginal

  22. Expected Value of Sample Information • Top half (favorable survey result) s1 (.148) $10,000 s2 (.185) 4 $15,000 d1 s3 (.667) $14,000 s1 (.148) $8,000 d2 s2 (.185) 5 2 $18,000 s3 (.667) I1 (.54) $12,000 d3 s1 (.148) $6,000 s2 (.185) 6 $16,000 s3 (.667) 1 $21,000

  23. Expected Value of Sample Information • Bottom half (unfavorable survey result) 1 s1 (.696) $10,000 I2 (.46) s2 (.217) 7 $15,000 d1 s3 (.087) $14,000 s1 (.696) $8,000 d2 s2 (.217) 8 3 $18,000 s3 (.087) $12,000 d3 s1 (.696) $6,000 s2 (.217) 9 $16,000 s3 (.087) $21,000

  24. Expected Value of Sample Information • EMV = .148(10,000) + .185(15,000) • + .667(14,000) = $13,593 d1 4 • $17,855 d2 • EMV = .148 (8,000) + .185(18,000) • + .667(12,000) = $12,518 5 2 I1 (.54) d3 • EMV = .148(6,000) + .185(16,000) • +.667(21,000) = $17,855 6 1 • EMV = .696(10,000) + .217(15,000) • +.087(14,000)= $11,433 7 d1 I2 (.46) d2 • EMV = .696(8,000) + .217(18,000) • + .087(12,000) = $10,554 8 3 d3 • $11,433 • EMV = .696(6,000) + .217(16,000) • +.087(21,000) = $9,475 9

  25. 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 that is less than the cost of the survey, the survey should not be purchased.

  26. Review Questions • Review Questions • You should try to answer some of the following questions before the next class. • You will not turn in your answers, but students may request to discuss their answers to begin the next class. • Your upcoming Final Exam will contain some similar questions, so you should eventually consider every review question before taking your exams.

  27. Review 1: Expected Value of Sample Information Review 1: Expected Value of Sample Information

  28. Review 1: Expected Value of Sample Information The Gorman Manufacturing Company must decide whether to make a component part at its Milan, Michigan, plan or buy the part from a supplier. The profit depends on the demand for the product. The following table shows the profit (in thousands of dollars): States of Demand s1(low) s2(med) s3 (high) Manufacture, d1 -20 40 100 Purchase,d2 10 45 70

  29. Review 1: Expected Value of Sample Information States of Demand s1(low) s2(med) s3 (high) Manufacture, d1 -20 40 100 Purchase,d2 10 45 70 Compute the optimal decision (which maximizes expected value) given priors P(s1) = 0.35, P(s2) = 0.35, P(s3) = 0.30. EV(d1) = 0.35 x (-20) + 0.35 x (40) + 0.30 x (100) = 37. EV(d2) = 0.35 x (10) + 0.35 x (45) + 0.30 x (70) = 40.25. So, choose Purchase, d2.

  30. Review 1: Expected Value of Sample Information States of Demand s1 (low) s2 (med) s3 (high) Manufacture, d1 -20 40 100 Purchase, d2 10 45 70 Use EVPI (expected value of perfect information) to determine whether Gorman should try to better estimate demand. If you had perfect information, then in state s1, pick d2 for payoff 10; in state s2, pick d2 for payoff 45; and in state s3, pick d1 for payoff 100. Hence, the expected payoff is 0.35 x (10) + 0.35 x (45) + 0.30 x (100) = 49.25, which is 9 more than the expected payoff EV(d2) = 40.25 from the optimum given the priors. EVPI thus = 9 (that is, 9 thousand), which is positive so information is valuable.

  31. Review 1: Expected Value of Sample Information States of Demand s1 (low) s2 (med) s3 (high) Manufacture, d1 -20 40 100 Purchase, d2 10 45 70 What is Gorman’s optimal strategy (which maximizes expected value) if it has access to a market study that concludes a F = Favorable or U = Unfavorable outcome with the following conditional probabilities? P(favorable | low state s1) = 0.1 P(favorable | med state s2) = 0.4 P(favorable | high state s3) = 0.6

  32. Review 1: Expected Value of Sample Information States of Demand s1 (low) s2 (med) s3 (high) Manufacture, d1 -20 40 100 Purchase, d2 10 45 70 Favorable survey result StatePrior P(si)Cond. P(F|si)Joint P(si&F)Post P(si|F) s1 0.35 0.1 0.035 0.0986 s2 0.35 0.4 0.140 0.3944 s3 0.30 0.6 0.1800.5070 P(favorable) = 0.355 • EV(d1) = 0.0986 x (-20) + 0.3944 x (40) + 0.5070 x (100) = 64.51. • EV(d2) = 0.0986 x (10) + 0.3944 x (45) + 0.5070 x (70) = 54.23. • So, if the survey is Favorable, choose Manufacture, d1.

  33. Review 1: Expected Value of Sample Information States of Demand s1 (low) s2 (med) s3 (high) Manufacture, d1 -20 40 100 Purchase, d2 10 45 70 Unfavorable survey result StatePrior P(si)Cond. P(U|si)Joint P(si&U)Post P(si|U) s1 0.35 0.9 0.315 0.4884 s2 0.35 0.6 0.210 0.3256 s3 0.30 0.4 0.1200.1860 P(Unfavorable) = 0.645 • EV(d1) = 0.4884 x (-20) + 0.3256 x (40) + 0.1860 x (100) = 21.86. • EV(d2) = 0.4884 x (10) + 0.3256 x (45) + 0.1860 x (70) = 32.56. • So, if the survey is Unfavorable, choose Purchase, d2.

  34. Review 1: Expected Value of Sample Information States of Demand s1 (low) s2 (med) s3 (high) Manufacture, d1 -20 40 100 Purchase, d2 10 45 70 • The survey is Favorable with probability P(Favorable) = 0.355. And contingent on Favorable, the expected payoff is 64.51. • The survey is Unfavorable with probability P(Unfavorable) = 0.645. And contingent on Unfavorable, the expected payoff is 32.56. • Therefore, the un-contingent expected payoff is 0.355 x 64.51 + 0.645 x 32.56 = 43.90. • The expected payoff with no information (priors) is 40.25. • Therefore, the EVSI = 43.90-40.25 = 3.65 ($3,650).

  35. Review 2: Expected Value of Sample Information Review 2: Expected Value of Sample Information

  36. Review Problems • Dollar Department Stores has received an offer from Harris Diamonds to purchase Dollar’s store on Grove Street for $120,000. Dollar is an expected-value maximizer. Dollar has determined probability estimates of the store's future profitability, based on economic outcomes, as: • P($80,000) = .2, P($100,000) = .3, P($120,000) = .1, and P($140,000) = .4. • Should Dollar sell the store on Grove Street? • What is the EVPI? • Dollar can have an economic forecast performed. The forecast indicates either G = Good business conditions or B = Bad business conditions. Probabilities of the indicators conditional on future profitability are P(G|$80,000) = .1; P(G|$100,000) = .2; P(G|$120,000) = .6; P(G|$140,000) = .3. Should Dollar purchase that forecast for $10,000? For $1,000?

  37. Review Problems • This problem can be solved like the previous problem by first converting the profit data and selling price into a payoff table: • P(s1 = $80,000) = .2, P(s2 = $100,000) = .3, P(s3 = $120,000) = .1, and P(s4 = $140,000) = .4 • Offer to sell for $120,000. States of Demand s1s2s3 s4 Do not sell, d180,000 100,000 120,000 140,000 Sell, d2120,000120,000 120,000 120,000

  38. Review Problems • Consider priors • P($80,000) = .2, P($100,000) = .3, P($120,000) = .1, and P($140,000) = .4. • Should Dollar sell the store on Grove Street? • Expected profit (in thousands of $) = .2 x 80 + .3 x 100 + .1 x 120 + .4 x 140 = 114, so selling for $120,000 increases value from $114,000. • Here is a more elaborate way to say the same thing: • EV(d1=Not sell) = .2x80 + .3x100 + .1x120 + .4x140 = 114. • EV(d2=Sell) = .2x120 + .3x120 + .1x120 + .4x120 = 120. • So, choose Sell, d2.

  39. Review Problems • Consider priors • P($80,000) = .2, P($100,000) = .3, P($120,000) = .1, and P($140,000) = .4. • What is the EVPI? Expected value of perfect information is what you can expect if you know the store’s future profitability before you have to decide whether to sell. • If future profit is either $80,000 or $100,000, then you sell for $120,000 (choose d2); if future profit is $120,000, then it does not matter whether you sell or not (choose d1 or d2); and if future profit is $140,000, then you do not sell (choose d1). • So, with perfect information, you earn $120,000, except in the probability P($140,000) = .4 state 4 event, when you earn $140,000. So you can expect .6 x $120,000 + .4 x $140,000 = $128,000, which is $8,000 more than selling for $120,000. • Therefore, EVPI = $8,000.

  40. Review Problems • Consider priors • P($80,000) = .2, P($100,000) = .3, P($120,000) = .1, and P($140,000) = .4. • EVPI = $8,000. • Should Dollar purchase the forecast for $10,000? • No, because the value of the forecast is at most EVPI, which is less than $10,000.

  41. Review Problems • Should Dollar purchase the forecast for $1,000? • First, compute posterior probabilities under the two possible forecasts: G = Good business conditions or B = Bad business conditions. • If G, then • Priors Conditional Joint Posterior • P(s1) = .2 P(G|s1) = .1 P(G&s1) = .02 P(s1|G) = .02/.26 = .077 • P(s2) = .3 P(G|s2) = .2 P(G&s2) = .06 P(s2|G) = .06/.26 = .231 • P(s3) = .1 P(G|s3) = .6 P(G&s3) = .06 P(s3|G) = .06/.26 = .231 • P(s4) = .4 P(G|s4) = .3 P(G&s4) = .12 P(s4|G) = .12/.26 = .462 P(G) = .26 • Expected Value of Store Given G = .077x$80,000 + .231x$100,000+.231x$120,000+.462x$140,000=$121,660 • Since that value is greater than $120,000, you do not sell the store if the report is G = Good business conditions.

  42. Review Problems • Should Dollar purchase the forecast for $1,000? • If B, then • Priors Conditional Joint Posterior • P(s1) = .2 P(B|s1) = .9 P(B&s1) = .18 P(s1|B) = .18/.74 = .243 • P(s2) = .3 P(B|s2) = .8 P(B&s2) = .24 P(s2|B) = .24/.74 = .324 • P(s3) = .1 P(B|s3) = .4 P(B&s3) = .04 P(s3|B) = .04/.74 = .054 • P(s4) = .4 P(B|s4) = .7 P(B&s4) = .28 P(s4|B) = .28/.74 = .378 P(B) = .74 • Expected Value of Store Given B = .243x$80,000 + .324x$100,000+.054x$120,000+.378x$140,000=$111,240 • Since that value is less than $120,000, you do sell the store if the report is B = Bad business conditions.

  43. Review Problems • Putting it all together, • P(G) = .26 of a Good report and expected profit =$121,660. • P(B) = .74 of a Bad report and expected profit $120,000. • Overall, expected profit is .26x$121,660 + .74$120,000 = $120,431. • Therefore, EVSI = $120,431-$120,000 = $431, and you should not pay $1,000 for the economic forecast.

  44. BA 452 Quantitative Analysis End of Lesson III.4

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