Chapter Topics The Payoff Table and Decision Trees Opportunity Loss

Slides:



Advertisements
Similar presentations
© 2002 Prentice-Hall, Inc.Chap 17-1 Basic Business Statistics (8 th Edition) Chapter 17 Decision Making.
Advertisements

Decision Theory.
Chapter 3 Decision Analysis.
20- 1 Chapter Twenty McGraw-Hill/Irwin © 2005 The McGraw-Hill Companies, Inc., All Rights Reserved.
Chapter 14 Decision Analysis. Decision Making Many decision making occur under condition of uncertainty Decision situations –Probability cannot be assigned.
Chapter 3 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
Chapter 17 Decision Making 17.1 Payoff Table and Decision Tree 17.2 Criteria for Decision Making.
Chapter 3 Decision Analysis.
Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter Twenty An Introduction to Decision Making GOALS.
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.
DSC 3120 Generalized Modeling Techniques with Applications
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
Chapter 4 Decision Analysis.
Chap 19-1 Copyright ©2012 Pearson Education, Inc. publishing as Prentice Hall On Line Topic Decision Making Basic Business Statistics 12 th Edition.
1 1 Slide Decision Analysis n Structuring the Decision Problem n Decision Making Without Probabilities n Decision Making with Probabilities n Expected.
1 1 Slide Decision Analysis Professor Ahmadi. 2 2 Slide Decision Analysis Chapter Outline n Structuring the Decision Problem n Decision Making Without.
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap 18-1 Business Statistics: A Decision-Making Approach 6 th Edition Chapter.
CHAPTER 19: Decision Theory to accompany Introduction to Business Statistics fourth edition, by Ronald M. Weiers Presentation by Priscilla Chaffe-Stengel.
Business 260: Managerial Decision Analysis
Decision Making Under Uncertainty and Under Risk
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.
Decision Analysis Introduction Chapter 6. What kinds of problems ? Decision Alternatives (“what ifs”) are known States of Nature and their probabilities.
Chapter 14 Decision Making
Operations Management Decision-Making Tools Module A
CD-ROM Chap 14-1 A Course In Business Statistics, 4th © 2006 Prentice-Hall, Inc. A Course In Business Statistics 4 th Edition CD-ROM Chapter 14 Introduction.
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 16-1 Chapter 16 Decision Making Statistics for Managers Using Microsoft.
1 1 Slide © 2005 Thomson/South-Western EMGT 501 HW Solutions Chapter 12 - SELF TEST 9 Chapter 12 - SELF TEST 18.
Chapter 8 Decision Analysis n Problem Formulation n Decision Making without Probabilities n Decision Making with Probabilities n Risk Analysis and Sensitivity.
Module 5 Part 2: Decision Theory
PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J A-1 Operations.
Decision Theory Decision theory problems are characterized by the following: 1.A list of alternatives. 2.A list of possible future states of nature. 3.Payoffs.
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.
Operations Research II Course,, September Part 5: Decision Models Operations Research II Dr. Aref Rashad.
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 16-1 Chapter 16 Decision Making Statistics for Managers Using Microsoft.
Statistics for Managers Using Microsoft Excel, 5e © 2008 Prentice-Hall, Inc.Chap 17-1 Statistics for Managers Using Microsoft® Excel 5th Edition Chapter.
Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 17-1 Chapter 17 Decision Making Basic Business Statistics 10 th Edition.
Decision Analysis Mary Whiteside. Decision Analysis Definitions Actions – alternative choices for a course of action Actions – alternative choices for.
Copyright © 2009 Cengage Learning 22.1 Chapter 22 Decision Analysis.
Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc Chapter 23 Decision Analysis.
© 2008 Prentice Hall, Inc.A – 1 Decision-Making Environments  Decision making under uncertainty  Complete uncertainty as to which state of nature may.
Welcome Unit 4 Seminar MM305 Wednesday 8:00 PM ET Quantitative Analysis for Management Delfina Isaac.
Decision Theory McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
Models for Strategic Marketing Decision Making. Market Entry Decisions To enter first or to wait Sources of First-Mover Advantages –Technological leadership.
BUAD306 Chapter 5S – Decision Theory. Why DM is Important The act of selecting a preferred course of action among alternatives A KEY responsibility of.
QUANTITATIVE TECHNIQUES
Decision Analysis.
1 1 © 2003 Thomson  /South-Western Slide Slides Prepared by JOHN S. LOUCKS St. Edward’s University.
DECISION MODELS. Decision models The types of decision models: – Decision making under certainty The future state of nature is assumed known. – Decision.
Chap 18-1 Business Statistics: A Decision-Making Approach 6 th Edition Chapter 18 Introduction to Decision Analysis.
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.
QUANTITATIVE TECHNIQUES
DECISION THEORY & DECISION TREE
Slides 8a: Introduction
Systems Analysis Methods
Chapter 5S – Decision Theory
Chapter 19 Decision Making
Statistical Decision Theory
Decision Theory Analysis
Decision Analysis.
Chapter 17 Decision Making
Applied Statistical and Optimization Models
Presentation transcript:

Chapter Topics The Payoff Table and Decision Trees Opportunity Loss Criteria for Decision Making Expected Monetary Value Return to Risk Ratio

Features of Decision Making List Alternative Courses of Action (Possible Events or Outcomes) Determine ‘Payoffs’ (Associate a Payoff with Each Event or Outcome) Adopt Decision Criteria (Evaluate Criteria for Selecting the Best Course of Action)

List Possible Actions or Events Two Methods of Listing Payoff Table Decision Tree

Payoff Table Consider a food vendor determining whether to sell soft drinks or hot dogs. Course of Action (Aj) Sell Soft Drinks (A1) Sell Hot Dogs (A2) Event (Ei) Cool Weather (E1) x11 =$50 x12 = $100 Warm Weather (E2) x21 = 200 x22 = 125 xij = payoff (profit) for event i and action j

Decision Tree:Example Food Vendor Profit Tree Diagram x11 = $50 Cool Weather Warm Weather Soft Drinks x21 = 200 Hot Dogs x12 = 100 Cool Weather Warm Weather x22 =125

Opportunity Loss: Example Highest possible profit for an event Ei - Actual profit obtained for an action Aj Opportunity Loss (lij ) Event: Cool Weather Action: Soft Drinks Profit: $50 Alternative Action: Hot Dogs Profit: $100 Opportunity Loss = $100 - $50 = $50 Note: Opportunity Loss is always positive

Opportunity Loss: Table Alternative Course of Action Event Optimal Profit of Sell Soft Drinks Sell Hot Dogs Action Optimal Action Cool Hot 100 100 - 50 = 50 100 - 100 = 0 Weather Dogs Warm Soft 200 200 - 200 = 0 200 - 125 = 75 Weather Drinks

Decision Criteria Expected Monetary Value (EMV) The expected profit for taking an action Aj Expected Opportunity Loss (EOL) The expected loss for not taking action Aj Expected Value of Perfect Information (EVPI) The expected opportunity loss from the best decision

Decision Criteria -- EMV Expected Monetary Value (EMV) Sum (monetary payoffs of events) ´ (probabilities of the events) N å EMVj = Xij Pi i = 1 EMVj = expected monetary value of action j xi,j = payoff for action j and event i Pi = probability of event i occurring

Decision Criteria -- EMV Table Example: Food Vendor Pi Event Soft xijPi Hot xijPi Drinks Dogs .50 Cool $50 $50 ´.5 = $25 $100 $100´.50 = $50 .50 Warm $200 $200 ´.5 = 100 $125 $25´.50 = 62.50 EMV Soft Drink = $125 EMV Hot Dog = $112.50 Better alternative

Decision Criteria -- EOL Expected Opportunity Loss (EOL) Sum (opportunity losses of events) ´ (probabilities of events) N å EOLj = lij Pi i =1 EOLj = expected monetary value of action j li,j = payoff for action j and event i Pi = probability of event i occurring

Decision Criteria -- EOL Table Example: Food Vendor Pi Event Op Loss lijPi OP Loss lijPi Soft Drinks Hot Dogs .50 Cool $50 $50´.50 = $25 $0 $0´.50 = $0 .50 Warm 0 $0 ´.50 = $0 $75 $75 ´.50 = $37.50 EOL Soft Drinks = $25 EOL Hot Dogs = $37.50 Better Choice

Decision Criteria -- EVPI Expected Value of Perfect Information (EVPI) The expected opportunity loss from the best decision Represents the maximum amount you are willing to pay to obtain perfect information Expected Profit Under Certainty - Expected Monetary Value of the Best Alternative EVPI (should be a positive number)

EVPI Computation Expected Profit Under Certainty = .50($100) + .50($200) = $150 Expected Monetary Value of the Best Alternative = $125 EPVI = $25 The maximum you would be willing to spend to obtain perfect information.

Taking Account of Variability: FoodVendor s2 for Soft Drink = (50 -125)2 ´.5 + (200 -125)2 ´.5 = 5625 s for Soft Drink = 75 CVfor Soft Drinks = (75/125) ´ 100% = 60% s2 for Hot Dogs = 156.25 s for Hot dogs = 12.5 CVfor Hot dogs = 11.11%

Return to Risk Ratio Expresses the relationship between the return (payoff) and the risk (standard deviation). RRR = Return to Risk Ratio = RRRSoft Drinks = 125/75 = 1.67 RRRHot Dogs = 9 You might wish to choose Hot Dogs. Although Soft Drinks have the higher Expected Monetary Value, Hot Dogs have a much larger return to risk ratio and a much smaller CV. Note: RRR is the inverse of CV