1 Decision Analysis with Sample Information. 2 I begin here with our familiar example. States of Nature Decision Alternatives s1s2 d187 d2145 d320-9 At.

Slides:



Advertisements
Similar presentations
QUANTITATIVE METHODS FOR BUSINESS 8e
Advertisements

1 Decision Analysis. 2 I begin here with an example. In the table below you see that a firm has three alternatives that it can choose from, but it does.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or.
Chapter 12: Testing hypotheses about single means (z and t) Example: Suppose you have the hypothesis that UW undergrads have higher than the average IQ.
Decision Making Under Risk Continued: Bayes’Theorem and Posterior Probabilities MGS Chapter 8 Slides 8c.
Chapter 8: Decision Analysis
1 1 Slide © 2004 Thomson/South-Western Payoff Tables n The consequence resulting from a specific combination of a decision alternative and a state of nature.
Chapter 18 Statistical Decision Theory Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Statistics for Business and Economics 7 th.
Decision Theory.
Chapter 21 Statistical Decision Theory
Decision Analysis. What is Decision Analysis? The process of arriving at an optimal strategy given: –Multiple decision alternatives –Uncertain future.
Managerial Decision Modeling with Spreadsheets
1 1 Slide © 2000 South-Western College Publishing/ITP Slides Prepared by JOHN LOUCKS.
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.
Dr. C. Lightner Fayetteville State University
Decision analysis: part 2
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or.
Chapter 7 Decision Analysis
Slides prepared by JOHN LOUCKS St. Edward’s University.
Chapter 4 Decision Analysis.
1 1 Slide Decision Analysis n Structuring the Decision Problem n Decision Making Without Probabilities n Decision Making with Probabilities n Expected.
Decision Analysis A method for determining optimal strategies when faced with several decision alternatives and an uncertain pattern of future events.
Chapter 8 Decision Analysis MT 235.
1 1 Slide Decision Analysis Professor Ahmadi. 2 2 Slide Decision Analysis Chapter Outline n Structuring the Decision Problem n Decision Making Without.
1 1 Slide © 2009 South-Western, a part of Cengage Learning Slides by John Loucks St. Edward’s University.
Business 260: Managerial Decision Analysis
MGS3100_06.ppt/Nov 3, 2014/Page 1 Georgia State University - Confidential MGS 3100 Business Analysis Decision Analysis Nov 3, 2014.
Decision analysis: part 1 BSAD 30 Dave Novak Source: Anderson et al., 2013 Quantitative Methods for Business 12 th edition – some slides are directly from.
1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
Statistical Techniques I EXST7005 Lets go Power and Types of Errors.
BA 452 Lesson C.4 The Value of Information ReadingsReadings Chapter 13 Decision Analysis.
Monopsony Monopsony is a situation where there is one buyer – you have seen Monopoly, a case of one seller. Here we want to explore the impact on the.
1 Production and Supply. 2 When trying to understand the supply decisions of firms, the concept called opportunity cost is important. Here I reproduce.
1 1 Slide © 2009 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 16-1 Chapter 16 Decision Making Statistics for Managers Using Microsoft.
1 Labor Markets. 2 Review and overview In this section we want to look at various environments in which suppliers and demanders of labor interact. When.
1 1 Slide © 2005 Thomson/South-Western EMGT 501 HW Solutions Chapter 12 - SELF TEST 9 Chapter 12 - SELF TEST 18.
1 Decision Analysis Here we study the situation where the probability of each state of nature is known.
Decision Analysis A. A. Elimam College of Business San Francisco State University.
MA - 1© 2014 Pearson Education, Inc. Decision-Making Tools PowerPoint presentation to accompany Heizer and Render Operations Management, Eleventh Edition.
Chapter 8 Decision Analysis n Problem Formulation n Decision Making without Probabilities n Decision Making with Probabilities n Risk Analysis and Sensitivity.
1 1 Slide Decision Theory Professor Ahmadi. 2 2 Slide Learning Objectives n Structuring the decision problem and decision trees n Types of decision making.
Contemporary Engineering Economics, 6 th edition Park Copyright © 2016 by Pearson Education, Inc. All Rights Reserved Decision-Tree Analysis Lecture No.
How to Market Yourself In a Difficult Job Market. Background (“Market Analysis”) The Job Tree Analyzing Your “Customer” Organizing Your “Selling Points”
MATH 2400 Ch. 15 Notes.
Global Trade For countries to grade goods and services, they must also trade their currencies. The process of converting one currency to another is known.
Copyright © 2009 Cengage Learning 22.1 Chapter 22 Decision Analysis.
Models for Strategic Marketing Decision Making. Market Entry Decisions To enter first or to wait Sources of First-Mover Advantages –Technological leadership.
Decision Trees. Introduction Decision trees enable one to look at decisions: with many alternatives and states of nature which must be made in sequence.
©2007 by The McGraw-Hill Companies, Inc. All rights reserved. Analyzing and Evaluating Inductive Arguments The aim of this tutorial is to help you learn.
By Edward Lim 8.7. What? Today, we continued our research on our chosen Cornerstone Piece, we got our learning journals up to date, we made sure all our.
To accompany Quantitative Analysis for Management, 7e by (Render/Stair 4-1 © 2000 by Prentice Hall, Inc., Upper Saddle River, N.J Quantitative Analysis.
QUANTITATIVE TECHNIQUES
Statistical Techniques
Decision Analysis.
1 1 © 2003 Thomson  /South-Western Slide Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
Decision Analysis EMBA 8150 Dr. Satish Nargundkar.
19-1 Consumer Choice  Prices are important in determining consumer behavior.  New products have to be priced correctly. The price could be set too high.
Chapter 19 Statistical Decision Theory ©. Framework for a Decision Problem action i.Decision maker has available K possible courses of action : a 1, a.
Chapter 8 Decision Analysis n Problem Formulation n Decision Making without Probabilities n Decision Making with Probabilities n Risk Analysis and Sensitivity.
1 1 Slide © 2005 Thomson/South-Western Chapter 13 Decision Analysis n Problem Formulation n Decision Making without Probabilities n Decision Making with.
Decision Making Under Uncertainty
Scatter Plots and Correlation Coefficients
Operations Management
John Loucks St. Edward’s University . SLIDES . BY.
Sample Presentation. Slide 1 Info Slide 2 Info.
Presentation Test. Second Slide Third Slide It worked.
Decision Analysis MBA 8040 Dr. Satish Nargundkar.
Chapter 13 Decision Analysis
Statistical Decision Theory
Presentation transcript:

1 Decision Analysis with Sample Information

2 I begin here with our familiar example. States of Nature Decision Alternatives s1s2 d187 d2145 d320-9 At this point we have said the P(s1) =.8 and P(s2) =.2. It may be in the interest of the decision maker to search for more information about the states of nature. Sample information would be used to potentially change our views about the probability of s1 and s2.

3 Let’s say here that the decision maker feels that searching for more information will yield 1 of 2 things: -A favorable report in that demand for condos seems high based on many individuals expressing a demand, -An unfavorable report in that the demand for condos appears low based on many individuals being asked about their future plans and the response being no demand often. On the next slide I have reproduced the basic decision tree we had for this problem before. But now we will have to consider the tree three times: once if we do the market research and the report is favorable, a second time if we do the market research and the report is unfavorable, and a third time if we do not do the market research (which we have already done).

d1 d2 d3 Strong weak strong weak Strong weak

5 If the report is favorable, say P(s1|favorable report) =.94 and P(s2|favorable report) =.06, Then the expected value of each decision alternative is d1.94(8) + (.06)(7) = 7.94 d2.94(14) + (.06)(5) = d3.94(20) + (.06)(-9) = The best would be d3 If the report is unfavorable, say P(s1|unfavorable report) =.35 and P(s2|unfavorable report) =.65, Then the expected value of each decision alternative is d1.35(8) + (.65)(7) = 7.35 d2.35(14) + (.65)(5) = 8.15 d3.35(20) + (.65)(-9) = 1.15 The best would be d2

6 If the additional information is not collected then we are back where we started and we have P(s1) =.8 and P(s2) =.2, and the expected value of each decision alternative is d1.8(8) +.2(7) = 7.80 d2.8(14) +.2(5) = d3.8(20) +.2(-9) = The best would be d3. Now, if the search for additional information happens d3 is chosen if the information is favorable and d2 is chosen if the information is unfavorable. Say P(favorable info) =.77 and P(unfavorable info) =.23. Then the expected value if additional information is searched for is.77(18.26) +.23(8.15) = Since this is higher than (the best if no information is search for) then the market research should happen. If information is favorable do d3, else do d2.

7 In our example was the expected value without sample information (EVwoSI), was the expected value with sample information (EVwSI). The expected value of sample information, EVSI, =Absolute value (EVwSI – EVwoSI) =1.73 in our example.

8 Efficiency of Sample Information You may recall we said the expected value of perfect information, EVPI, was what you could expect to gain if you had perfect information. EVSI is what is actually gained. E = (EVSI/EVPI)100 is the efficiency of sample information. Here we have (1.73/3.2)100 = 54.1%. Should we stop looking for more information about the states of nature? If the efficiency is high there is not much to gain. But if the efficiency is low you may not be able to do more because you have spent all you can hope to gain.

9 Let’s do a problem. We have state of nature alternativess1s2 d d If we do the market study and we get a favorable report the expected values are d1.57(100) +.43(300) = 186 (because P(s1|favorable report)=.57) d2.57(400) +.43(200) = 314 So you would go with d2. If we do the market study and we get an unfavorable report the expected values are d1.18(100) +.82(300) = 264 (because P(s1|unfavorable report)=.18) d2.18(400) +.82(200) = 236 So you would go with d1. If we do not do the market study the expected values are d1.4(100) +.6(300) = 220 (because P(s1)=.40) d2.4(400) +.6(200) = 280 So you would go with d2.

10 The expected value of the market study is.56(314) +.44(264) = 292. (because P(favorable report)=.56) The expected value without the study was the expected value of d2 of 280. So, do the market study and if the information is favorable do d2 and if it is unfavorable do d1.