# Decision Analysis Chapter 12.

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Decision Analysis Chapter 12

Chapter Topics Components of Decision Making
Decision Making without Probabilities Decision Making with Probabilities Decision Analysis with Additional Information Utility Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Analysis Overview
Previous chapters used an assumption of certainty with regards to problem parameters. This chapter relaxes the certainty assumption Two categories of decision situations: Probabilities can be assigned to future occurrences Probabilities cannot be assigned to future occurrences Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Components of Decision Making
Decision Analysis Components of Decision Making A state of nature is an actual event that may occur in the future. A payoff table is a means of organizing a decision situation, presenting the payoffs from different decisions given the various states of nature. Table Payoff table Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Analysis Decision Making Without Probabilities
Figure 12.1 Decision situation with real estate investment alternatives Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making without Probabilities
Decision Analysis Decision Making without Probabilities Decision-Making Criteria maximax maximin minimax minimax regret Hurwicz equal likelihood Table 12.2 Payoff table for the real estate investments Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making without Probabilities Maximax Criterion
In the maximax criterion the decision maker selects the decision that will result in the maximum of maximum payoffs; an optimistic criterion. Table Payoff table illustrating a maximax decision Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making without Probabilities Maximin Criterion
In the maximin criterion the decision maker selects the decision that will reflect the maximum of the minimum payoffs; a pessimistic criterion. Table Payoff table illustrating a maximin decision Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making without Probabilities Minimax Regret Criterion
Regret is the difference between the payoff from the best decision and all other decision payoffs. Example: under the Good Economic Conditions state of nature, the best payoff is \$100,000. The manager’s regret for choosing the Warehouse alternative is \$100,000-\$30,000=\$70,000 Table Regret table Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making without Probabilities Minimax Regret Criterion
The manager calculates regrets for all alternatives under each state of nature. Then the manager identifies the maximum regret for each alternative. Finally, the manager attempts to avoid regret by selecting the decision alternative that minimizes the maximum regret. Table Regret table illustrating the minimax regret decision Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making without Probabilities Hurwicz Criterion
The Hurwicz criterion is a compromise between the maximax and maximin criteria. A coefficient of optimism, , is a measure of the decision maker’s optimism. The Hurwicz criterion multiplies the best payoff by  and the worst payoff by 1- , for each decision, and the best result is selected. Here,  = 0.4. Decision Values Apartment building \$50,000(.4) + 30,000(.6) = 38,000 Office building \$100,000(.4) - 40,000(.6) = 16,000 Warehouse \$30,000(.4) + 10,000(.6) = 18,000 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making without Probabilities Equal Likelihood Criterion
The equal likelihood ( or Laplace) criterion multiplies the decision payoff for each state of nature by an equal weight, thus assuming that the states of nature are equally likely to occur. Decision Values Apartment building \$50,000(.5) + 30,000(.5) = 40,000 Office building \$100,000(.5) - 40,000(.5) = 30,000 Warehouse \$30,000(.5) + 10,000(.5) = 20,000 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making without Probabilities Summary of Criteria Results
A dominant decision is one that has a better payoff than another decision under each state of nature. The appropriate criterion is dependent on the “risk” personality and philosophy of the decision maker. Criterion Decision (Purchase) Maximax Office building Maximin Apartment building Minimax regret Apartment building Hurwicz Apartment building Equal likelihood Apartment building Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making without Probabilities
Solution with QM for Windows (1 of 3) Exhibit 12.1 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making without Probabilities
Solution with QM for Windows (2 of 3) Equal likelihood weight Exhibit 12.2 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making without Probabilities
Solution with QM for Windows (3 of 3) Exhibit 12.3 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making without Probabilities Solution with Excel
=MIN(C7,D7) =MAX(E7,E9) =MAX(F7:F9) =MAX(C18,D18) =MAX(C7:C9)-C9 =C7*C25+D7*C26 =C7*0.5+D7*0.5 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Exhibit 12.4

Decision Making with Probabilities Expected Value
Expected value is computed by multiplying each decision outcome under each state of nature by the probability of its occurrence. EV(Apartment) = \$50,000(.6) + 30,000(.4) = \$42,000 EV(Office) = \$100,000(.6) - 40,000(.4) = \$44,000 EV(Warehouse) = \$30,000(.6) + 10,000(.4) = \$22,000 Table 12.7 Payoff table with probabilities for states of nature Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making with Probabilities Expected Opportunity Loss
The expected opportunity loss is the expected value of the regret for each decision. The expected value and expected opportunity loss criterion result in the same decision. EOL(Apartment) = \$50,000(.6) + 0(.4) = 30,000 EOL(Office) = \$0(.6) + 70,000(.4) = 28,000 EOL(Warehouse) = \$70,000(.6) + 20,000(.4) = 50,000 Table 12.8 Regret table with probabilities for states of nature Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Expected Value Problems Solution with QM for Windows
Expected values Exhibit 12.5 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Expected Value Problems Solution with Excel and Excel QM (1 of 2)
Expected value for apartment building Exhibit 12.6 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Expected Value Problems Solution with Excel and Excel QM (2 of 2)

Decision Making with Probabilities
Expected Value of Perfect Information The expected value of perfect information (EVPI) is the maximum amount a decision maker would pay for additional information. EVPI equals the expected value given perfect information minus the expected value without perfect information. EVPI equals the expected opportunity loss (EOL) for the best decision. Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Table 12.9 Payoff table with decisions, given perfect information
Decision Making with Probabilities EVPI Example (1 of 2) Table Payoff table with decisions, given perfect information Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making with Probabilities EVPI Example (2 of 2)
Decision with perfect information: \$100,000(.60) + 30,000(.40) = \$72,000 Decision without perfect information: EV(office) = \$100,000(.60) - 40,000(.40) = \$44,000 EVPI = \$72, ,000 = \$28,000 EOL(office) = \$0(.60) + 70,000(.4) = \$28,000 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making with Probabilities EVPI with QM for Windows
The expected value, given perfect information, in Cell F12 =MAX(E7:E9) =F12-F11 Exhibit 12.8 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making with Probabilities Decision Trees (1 of 4)
A decision tree is a diagram consisting of decision nodes (represented as squares), probability nodes (circles), and decision alternatives (branches). Table Payoff table for real estate investment example Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making with Probabilities Decision Trees (2 of 4)
Figure Decision tree for real estate investment example Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making with Probabilities Decision Trees (3 of 4)
The expected value is computed at each probability node: EV(node 2) = .60(\$50,000) + .40(30,000) = \$42,000 EV(node 3) = .60(\$100,000) + .40(-40,000) = \$44,000 EV(node 4) = .60(\$30,000) + .40(10,000) = \$22,000 Branches with the greatest expected value are selected. Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making with Probabilities Decision Trees (4 of 4)
Figure Decision tree with expected value at probability nodes Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making with Probabilities Decision Trees with QM for Windows
Select node to add from Number of branches from node 1 Add branches from node 1 to 2, 3, and 4 Exhibit 12.9 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making with Probabilities
Decision Trees with Excel and TreePlan (1 of 4) Exhibit 12.10 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making with Probabilities
Decision Trees with Excel and TreePlan (2 of 4) To create another branch, click “B5,” then the “Decision Tree” menu, and select “Add Branch” Invoke TreePlan from the “Add Ins” menu Exhibit 12.11 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making with Probabilities
Decision Trees with Excel and TreePlan (3 of 4) Click on cell “F3,” then “Decision Tree” Select “Change to Event Node” and add two new branches Exhibit 12.12 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making with Probabilities
Decision Trees with Excel and TreePlan (4 of 4) Add numerical dollar and probability values in these cells in column H Exhibit 12.13 These cells contain decision tree formulas; do not type in these cells in columns E and I Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Sequential Decision Tree Analysis Solution with QM for Windows
Cell A16 contains the expected value of \$44,000 Exhibit 12.14 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making with Probabilities Sequential Decision Trees (1 of 4)
A sequential decision tree is used to illustrate a situation requiring a series of decisions. Used where a payoff table, limited to a single decision, cannot be used. The next slide shows the real estate investment example modified to encompass a ten-year period in which several decisions must be made. Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making with Probabilities Sequential Decision Trees (2 of 4)
Figure Sequential decision tree Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making with Probabilities Sequential Decision Trees (3 of 4)
Expected value of apartment building is: \$1,290, ,000 = \$490,000 Expected value if land is purchased is: \$1,360, ,000 = \$1,160,000 The decision is to purchase land; it has the highest net expected value of \$1,160,000. Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Making with Probabilities Sequential Decision Trees (4 of 4)
Figure Sequential decision tree with nodal expected values Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Sequential Decision Tree Analysis Solution with Excel QM
Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Exhibit 12.15

Sequential Decision Tree Analysis Solution with TreePlan
Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Exhibit 12.16

Bayesian Analysis (1 of 3) Bayesian analysis uses additional information to alter the marginal probability of the occurrence of an event. In the real estate investment example, using the expected value criterion, the best decision was to purchase the office building with an expected value of \$444,000, and EVPI of \$28,000. Table Payoff table for real estate investment Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Bayesian Analysis (2 of 3) A conditional probability is the probability that an event will occur given that another event has already occurred. An economic analyst provides additional information for the real estate investment decision, forming conditional probabilities: g = good economic conditions p = poor economic conditions P = positive economic report N = negative economic report P(Pg) = .80 P(NG) = .20 P(Pp) = .10 P(Np) = .90 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Bayesian Analysis (3 of 3) A posterior probability is the altered marginal probability of an event based on additional information. Prior probabilities for good or poor economic conditions in the real estate decision: P(g) = .60; P(p) = .40 Posterior probabilities by Bayes’ rule: (gP) = P(PG)P(g)/[P(Pg)P(g) + P(Pp)P(p)] = (.80)(.60)/[(.80)(.60) + (.10)(.40)] = .923 Posterior (revised) probabilities for decision: P(gN) = .250 P(pP) = P(pN) = .750 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Trees with Posterior Probabilities (1 of 4) Decision trees with posterior probabilities differ from earlier versions in that: Two new branches at the beginning of the tree represent report outcomes. Probabilities of each state of nature are posterior probabilities from Bayes’ rule. Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Trees with Posterior Probabilities (2 of 4) Figure Decision tree with posterior probabilities Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Trees with Posterior Probabilities (3 of 4) EV (apartment building) = \$50,000(.923) + 30,000(.077) = \$48,460 EV (strategy) = \$89,220(.52) + 35,000(.48) = \$63,194 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Trees with Posterior Probabilities (4 of 4) Figure Decision tree analysis for real estate investment Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Computing Posterior Probabilities with Tables Table Computation of posterior probabilities Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Analysis with Additional Information Computing Posterior Probabilities with Excel
Exhibit 12.17 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Expected Value of Sample Information The expected value of sample information (EVSI) is the difference between the expected value with and without information: For example problem, EVSI = \$63, ,000 = \$19,194 The efficiency of sample information is the ratio of the expected value of sample information to the expected value of perfect information: efficiency = EVSI /EVPI = \$19,194/ 28,000 = .68 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Analysis with Additional Information Utility (1 of 2)
Table Payoff table for auto insurance example Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Decision Analysis with Additional Information Utility (2 of 2)
Expected Cost (insurance) = .992(\$500) (500) = \$500 Expected Cost (no insurance) = .992(\$0) (10,000) = \$80 The decision should be do not purchase insurance, but people almost always do purchase insurance. Utility is a measure of personal satisfaction derived from money. Utiles are units of subjective measures of utility. Risk averters forgo a high expected value to avoid a low-probability disaster. Risk takers take a chance for a bonanza on a very low-probability event in lieu of a sure thing. Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Example Problem Solution (1 of 9)
Decision Analysis Example Problem Solution (1 of 9) A corporate raider contemplates the future of a recent acquisition. Three alternatives are being considered in two states of nature. The payoff table is below. Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Example Problem Solution (2 of 9)
Decision Analysis Example Problem Solution (2 of 9) Determine the best decision without probabilities using the 5 criteria of the chapter. Determine best decision with probabilities assuming .70 probability of good conditions, .30 of poor conditions. Use expected value and expected opportunity loss criteria. Compute expected value of perfect information. Develop a decision tree with expected value at the nodes. Given the following, P(Pg) = .70, P(Ng) = .30, P(Pp) = 20, P(Np) = .80, determine posterior probabilities using Bayes’ rule. Perform a decision tree analysis using the posterior probability obtained in part e. Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Example Problem Solution (3 of 9)
Decision Analysis Example Problem Solution (3 of 9) Step 1 (part a): Determine decisions without probabilities. Maximax Decision: Maintain status quo Decisions Maximum Payoffs Expand \$800,000 Status quo 1,300,000 (maximum) Sell ,000 Maximin Decision: Expand Decisions Minimum Payoffs Expand \$500,000 (maximum) Status quo -150,000 Sell ,000 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Example Problem Solution (4 of 9)
Decision Analysis Example Problem Solution (4 of 9) Minimax Regret Decision: Expand Decisions Maximum Regrets Expand \$500,000 (minimum) Status quo 650,000 Sell ,000 Hurwicz ( = .3) Decision: Expand Expand \$800,000(.3) + 500,000(.7) = \$590,000 Status quo \$1,300,000(.3) - 150,000(.7) = \$285,000 Sell \$320,000(.3) + 320,000(.7) = \$320,000 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Example Problem Solution (5 of 9)
Decision Analysis Example Problem Solution (5 of 9) Equal Likelihood Decision: Expand Expand \$800,000(.5) + 500,000(.5) = \$650,000 Status quo \$1,300,000(.5) - 150,000(.5) = \$575,000 Sell \$320,000(.5) + 320,000(.5) = \$320,000 Step 2 (part b): Determine Decisions with EV and EOL. Expected value decision: Maintain status quo Expand \$800,000(.7) + 500,000(.3) = \$710,000 Status quo \$1,300,000(.7) - 150,000(.3) = \$865,000 Sell \$320,000(.7) + 320,000(.3) = \$320,000 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Example Problem Solution (6 of 9)
Decision Analysis Example Problem Solution (6 of 9) Expected opportunity loss decision: Maintain status quo Expand \$500,000(.7) + 0(.3) = \$350,000 Status quo (.7) + 650,000(.3) = \$195,000 Sell \$980,000(.7) + 180,000(.3) = \$740,000 Step 3 (part c): Compute EVPI. EV given perfect information = 1,300,000(.7) + 500,000(.3) = \$1,060,000 EV without perfect information = \$1,300,000(.7) - 150,000(.3) = \$865,000 EVPI = \$1,060, ,000 = \$195,000 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Example Problem Solution (7 of 9)
Decision Analysis Example Problem Solution (7 of 9) Step 4 (part d): Develop a decision tree. Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Example Problem Solution (8 of 9)
Decision Analysis Example Problem Solution (8 of 9) Step 5 (part e): Determine posterior probabilities. P(gP) = P(Pg)P(g)/[P(Pg)P(g) + P(Pp)P(p)] = (.70)(.70)/[(.70)(.70) + (.20)(.30)] = .891 P(pP) = .109 P(gN) = P(Ng)P(g)/[P(Ng)P(g) + P(Np)P(p)] = (.30)(.70)/[(.30)(.70) + (.80)(.30)] = .467 P(pN) = .533 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Example Problem Solution (9 of 9)
Decision Analysis Example Problem Solution (9 of 9) Step 6 (part f): Decision tree analysis. Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

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