Download presentation

Presentation is loading. Please wait.

1
**QUANTITATIVE METHODS FOR BUSINESS 8e**

Anderson Sweeney Williams QUANTITATIVE METHODS FOR BUSINESS 8e Slides Prepared by JOHN LOUCKS © South-Western College Publishing/Thomson Learning

2
**Chapter 4 Decision Analysis, Part B**

Expected Value of Perfect Information Decision Analysis with Sample Information Developing a Decision Strategy Expected Value of Sample Information

3
**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 The payoff table for the three models is as follows: Average Number of Customers Per Hour s1 = s2 = s3 = 120 Model A $10, $15, $14,000 Model B $ 8, $18, $12,000 Model C $ 6, $16, $21,000

4
**Example: Burger Prince**

Expected Value Approach Calculate the expected value for each decision. The decision tree on the next slide can assist in this calculation. Here d1, d2, d3 represent the decision alternatives of models A, B, C, and s1, s2, s3 represent the states of nature of 80, 100, and 120.

5
**Example: Burger Prince**

Decision Tree Payoffs .4 s1 10,000 s2 .2 2 15,000 s3 .4 d1 14,000 .4 s1 8,000 d2 1 3 s2 .2 18,000 d3 s3 .4 12,000 .4 s1 6,000 4 s2 .2 16,000 s3 .4 21,000

6
**Example: Burger Prince**

Expected Value For Each Decision Choose the model with largest EV, Model C. EMV = .4(10,000) + .2(15,000) + .4(14,000) = $12,600 2 d1 Model A d2 EMV = .4(8,000) + .2(18,000) + .4(12,000) = $11,600 Model B 1 3 d3 Model C EMV = .4(6,000) + .2(16,000) + .4(21,000) = $14,000 4

7
**Expected Value of Perfect Information**

Frequently information is available which can improve the probability estimates for the states of nature. 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.

8
**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 these optimal returns. Step 3: Subtract the EV of the optimal decision from the amount determined in step (2).

9
**Example: Burger Prince**

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. EVPI= .4(10,000) + .2(18,000) + .4(21,000) - 14,000 = $2,000

10
**Example: Burger Prince**

Spreadsheet for Expected Value of Perfect Information

11
Risk Analysis Risk analysis helps the decision maker recognize the difference between: the expected value of a decision alternative and the payoff that might actually occur The risk profile for a decision alternative shows the possible payoffs for the decision alternative along with their associated probabilities.

12
**Example: Burger Prince**

Risk Profile for the Model C Decision Alternative .50 .40 Probability .30 .20 .10 Profit ($thousands)

13
Sensitivity Analysis Sensitivity analysis can be used to determine how changes to the following inputs affect the recommended decision alternative: probabilities for the states of nature values of the payoffs If a small change in the value of one of the inputs causes a change in the recommended decision alternative, extra effort and care should be taken in estimating the input value.

14
**Bayes’ Theorem and Posterior Probabilities**

Knowledge of sample or survey information can be used to revise the probability estimates for the states of nature. Prior to obtaining this information, the 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 outcomes of this analysis are called posterior probabilities or branch probabilities for decision trees.

15
**Computing Branch Probabilities**

Branch (Posterior) Probabilities Calculation 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. Step 2: Sum these joint probabilities over all states -- this gives the marginal probability for the indicator. Step 3: For each state, divide its joint probability by the marginal probability for the indicator -- this gives the posterior probability distribution.

16
**Expected Value of Sample Information**

The expected value of sample information (EVSI) is the additional expected profit possible through knowledge of the sample or survey information.

17
**Expected Value of Sample Information**

EVSI Calculation 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. Step 2: Compute the expected value of these optimal returns. Step 3: Subtract the EV of the optimal decision obtained without using the sample information from the amount determined in step (2).

18
**Efficiency of Sample Information**

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

19
**Example: Burger Prince**

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?

20
**Example: Burger Prince**

Influence Diagram Legend: Decision Chance Consequence Market Survey Results Avg. Number of Customers Per Hour Market Survey Restaurant Size Profit

21
**Example: Burger Prince**

Posterior Probabilities Favorable State Prior Conditional Joint Posterior Total P(favorable) = .54

22
**Example: Burger Prince**

Posterior Probabilities Unfavorable State Prior Conditional Joint Posterior Total P(unfavorable) = .46

23
**Example: Burger Prince**

Formula Spreadsheet for Posterior Probabilities

24
**Example: Burger Prince**

Solution Spreadsheet for Posterior Probabilities

25
**Example: Burger Prince**

Decision Tree (top half) s1 (.148) $10,000 s2 (.185) 4 $15,000 d1 s3 (.667) $14,000 s1 (.148) $8,000 d2 5 s2 (.185) 2 $18,000 s3 (.667) d3 $12,000 I1 (.54) s1 (.148) $6,000 6 s2 (.185) $16,000 s3 (.667) 1 $21,000

26
**Example: Burger Prince**

Decision Tree (bottom half) 1 s1 (.696) $10,000 s2 (.217) 7 $15,000 I2 (.46) d1 s3 (.087) $14,000 s1 (.696) $8,000 d2 s2 (.217) 8 3 $18,000 s3 (.087) d3 $12,000 s1 (.696) $6,000 s2 (.217) 9 $16,000 s3 (.087) $21,000

27
**Example: Burger Prince**

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

28
**Example: Burger Prince**

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.

29
**Example: Burger Prince**

Efficiency of Sample Information The efficiency of the survey: EVSI/EVPI = ($900.88)/($2000) =

30
**The End of Chapter 4, Part B**

Similar presentations

Presentation is loading. Please wait....

OK

Lesson 04 Decision Making

Lesson 04 Decision Making

© 2018 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google