Johan Oscar Ong, ST, MT

Size: px
Start display at page:

Download "Johan Oscar Ong, ST, MT"

Transcription

1 Decision Analysis Johan Oscar Ong, ST, MT

2 Analytical Decision Making Can Help Managers to: Gain deeper insight into the nature of business relationships Find better ways to assess values in such relationships; and See a way of reducing, or at least understanding, uncertainty that surrounds business plans and actions 2

3 Steps to Analytical DM Define problem and influencing factors Establish decision criteria Select decision-making tool (model) Identify and evaluate alternatives using decision-making tool (model) Select best alternative Implement decision Evaluate the outcome 3

4 Models Are less expensive and disruptive than experimenting with the real world system Allow operations managers to ask What if types of questions Are built for management problems and encourage management input Force a consistent and systematic approach to the analysis of problems Require managers to be specific about constraints and goals relating to a problem Help reduce the time needed in decision making 4

5 Limitations of the Models They may be expensive and time- consuming to develop and test Often misused and misunderstood (and feared) because of their mathematical and logical complexity Tend to downplay the role and value of nonquantifiable information Often have assumptions that oversimplify the variables of the real world 5

6 The Decision-Making Process Quantitative Analysis Problem Logic Historical Data Marketing Research Scientific Analysis Modeling Decision Qualitative Analysis Emotions Intuition Personal Experience and Motivation Rumors 6

7 Displaying a Decision Problem Decision trees Decision tables Outcomes States of Nature Alternatives Decision Problem 7

8 Types of Decision Models Decision making under uncertainty Decision making under risk Decision making under certainty 8

9 Fundamentals of Decision Theory Terms: Alternative: : course of action or choice State of nature: : an occurrence over which the decision maker has no control Symbols used in a decision tree: A decision node from which one of several alternatives may be selected A state of nature node out of which one state of nature will occur 9

10 Decision Table States of Nature Alternatives State 1 State 2 Alternative 1 Outcome 1 Outcome 2 Alternative 2 Outcome 3 Outcome 4 10

11 Getz Products Decision Tree A state of nature node A decision node Construct small plant 1 2 Favorable market Unfavorable market Favorable market Unfavorable market 11

12 Decision Making under Uncertainty Maximax-Choose the alternative that maximizes the maximum outcome for every alternative (Optimistic criterion) Maximin Maximin-Choose the alternative that maximizes the minimum outcome for every alternative (Pessimistic criterion) Equally likely - chose the alternative with the highest average outcome. 12

13 Example: States of Nature Alternatives Favorable Market Unfavorable Market Maximum in Row Minimum in Row Row Average Construct $200,000 -$180,000 $200,000 -$180,000 $10,000 large plant Construct small plant Do nothing $100,000 -$20,000 $100,000 -$20,000 $40,000 $0 $0 $0 $0 $0 Maximax Maximin Equally likely 13

14 Decision criteria The maximax choice is to construct a large plant. This is the maximum of the maximum number within each row or alternative. The maximin choice is to do nothing. This is the maximum mum of the minimum imum number within each row or alternative. The equally likely choice is to construct a small plant. This is the maximum of the average outcomes of each alternative. This approach assumes that all outcomes for any alternative are equally likely. 14

15 Decision Making under Risk Probabilistic decision situation States of nature have probabilities of occurrence Maximum Likelihood Criterion Maximize Expected Monitary Value (Bayes Decision Rule) 15

16 Maximum Likelihood Criteria Maximum Likelihood: : Identify most likely event, ignore others, and pick act with greatest payoff. Personal decisions are often made that way. Collectively, other events may be more likely. Ignores lots of information. 16

17 Bayes Decision Rule It is not a perfect criterion because it can lead to the less preferred choice. Consider the Far-Fetched Fetched Lottery decision: EVENTS Probability Would you gamble? Gamble ACTS Don t Gamble Head.5 +$10,000 $0 Tail.5 5,

18 The Far-Fetched Fetched Lottery Decision EVENTS Proba- bility Gamble Payoff Prob. ACTS Don t Gamble Payoff Prob Head.5 +$5,000 $0 Tail.5 2,500 0 Expected Payoff: $2,500 $0 Most people prefer not to gamble! That violates the Bayes decision rule. But the rule often indicates preferred choices even though it is not perfect. 18

19 Expected Monetary Value N: Number of states of nature k: Number of alternative decisions Xij: Value of Payoff for alternative i in state of nature j, i=1,2,...,k and j=1,2,...,n. Pj: Probability of state of naturej EMV ( A i ) = N j = 1 X ij P j 19

20 Example: States of Nature Alternatives Favorable Market P(0.5) Unfavorable Market P(0.5) Expected value Construct $200,000 -$180,000 $10,000 large plant Construct $100,000 -$20,000 $40,000 small plant Do nothing $0 $0 $0 Best choice 20

21 Decision Making under Certainty What if Getz knows the state of the nature with certainty? Then there is no risk for the state of the nature! A A marketing research company requests $65000 for this information 21

22 Questions: Should Getz hire the firm to make this study? How much does this information worth? What is the value of perfect information? 22

23 Expected Value With Perfect Information (EVPI) EVPI = Expected Payoff - Maximum expected payoff under Certainty with no information Let N: Number of states of nature and k: Number of actions, Expected Payoff under Ceratinty= N j= 1 (Max i { X ij }).P j Maximum expected payoff with no information=max {EMV i ; i=1,..,k} EVPI places an upper bound on what one would pay for additional information 23

24 Example: Expected Value of Perfect Information 24

25 Expected Value of Perfect Information Expected Value Under Certainty =($200,000* *0.50)= $100,000 Max( ax(emv)= Max{10,000, 40,000, 0}=$40,000 EVPI = Expected Value Under Certainty - Max( ax(emv) = $100,000 - $40,000 = $60,000 So Getz should not be willing to pay more than $60,000 25

26 Ex: Toy Manufacturer How to choose among 4 types of tippi-toes? toes? Demand for tippi-toes toes is uncertain: Light demand: 25,000 units (10%) Moderate demand: 100,000 units (70%) Heavy demand: 150,000 units (20%) 26

27 Payoff Table ACT (choice) Event (State of nature) Probability Gears and levers Spring Action Weights and pulleys Light 0.10 $25,000 -$10,000 -$125,000 Moderate , , ,000 Heavy , , ,000 27

28 Maximum Expected Payoff Criteria Gears and levers Expected $412,500 Payoff ACT (choice) Spring Action Weights and pulleys $412,500 $455,500 $417,000 Maximum expected payoff occurs at Spring Action! 28

29 Decision Trees Graphical display of decision process, i.e., alternatives, states of nature, probabilities, payoffs. Decision tables are convenient for problems with one set of alternatives and states of nature. With several sets of alternatives and states of nature (sequential decisions), decision trees are used! EMV criterion is the most commonly used criterion in decision tree analysis. 29

30 Steps of Decision Tree Analysis Define the problem Structure or draw the decision tree Assign probabilities to the states of nature Estimate payoffs for each possible combination of alternatives and states of nature Solve the problem by computing expected monetary values for each state- of-nature node 30

31 Decision Tree 1 State 1 State 2 Outcome 1 Outcome 2 Decision Node 2 State 1 State 2 State of Nature Node Outcome 3 Outcome 4 31

32 Ex1:Getz Products Decision Tree EMV for node 1 = $10,000 Construct small plant 1 2 Favorable market (0.5) Unfavorable market (0.5) Favorable market (0.5) Unfavorable market (0.5) EMV for node 2 = $40,000 Payoffs $200,000 -$180,000 $100,000-20,

33 A More Complex Decision Tree Let s say Getz Products has two sequential decisions to make: Conduct a survey for $10000? Build a large or small plant or not build? 33

34 Ex1:Getz Products Decision Tree 1 st decision point $49,200 2 nd decision point $106,400 $106,400 2 $63,600 3 Fav. Mkt (0.78) Unfav. Mkt (0.22) Fav. Mkt (0.78) Unfav. Mkt (0.22) $190,000 -$190,000 $90,000 -$30,000 $49,200 1 $40,000 $2,400 -$87,400 4 $2,400 5 $10,000 6 $40,000 7 Fav. Mkt (0.27) Unfav. Mkt (0.73) Fav. Mkt (0.27) Unfav. Mkt (0.73) Fav. Mkt (0.5) Unfav. Mkt (0.5) Fav. Mkt (0.5) Unfav. Mkt (0.5) 34 -$10,000 $190,000 -$190,000 $90,000 -$30,000 -$10,000 $200,000 -$180,000 $100,000 -$20,000 $0

35 Resulting Decision EMV of conducting the survey=$49,200 EMV of not conducting the survey=$40,000 So Getz should conduct the survey! If the survey results are favourable, build large plant. If the survey results are infavourable, build small plant. BIS 517-Aslı Sencer Erdem 35

36 Ex2: Ponderosa Record Company Decide whether or not to market the recordings of a rock group. Alternative1: test market 5000 units and if favorable, market units nationally Alternative2: Market units nationally Outcome is a complete success (all are sold) or failure 36

37 Ex2: Ponderosa-costs, prices Fixed payment to group: $5000 Production cost: $5000 and $0.75/cd Handling, distribution: $0.25/cd Price of a cd: $2/cd Cost of producing 5,000 cd s =5,000+5,000+( )5,000=$15,000 Cost of producing 45,000 cd s =0+5,000+( )45,000=$50,000 Cost of producing 50,000 cd s =5,000+5,000+( )50,000=$60,000 37

38 Ex2: Ponderosa-Event Probabilities Without testing P(success)=P(failure)=0.5 With testing P(success test result is favorable)=0.8 P(failure test result is favorable)=0.2 P(success test result is unfavorable)=0.2 P(failure test result is unfavorable)=0.8 38

39 Decision Tree for Ponderosa Record Company 39

40 Backward Approach 40

41 Sensitivity Analysis The optimal solution depends on many factors. Is the optimal policy robust? Question: -How does $1000 payoff change with respect to a change in success probability (0.8 currently)? earnings of success ($90,000 currently)? test marketing cost ($15,000 currently)? 41

42 Application Areas of Decision Theory Investments in research and development plant and equipment new buildings and structures Production and Inventory control Aggregate Planning Maintenance Scheduling, etc. 42

43 References Lapin L.L., Whisler W.D., Quantitative Decision Making, 7e, Heizer J., Render, B., Operations Management, 7e, Render, B., Stair R. M., Quantitative Analysis for Management, 8e, Anderson, D.R., Sweeney D.J, Williams T.A., Statistics for Business and Economics, 8e, Taha, H., Operations Research,

44

45

Chapter 3. Decision Analysis. Learning Objectives

Chapter 3. Decision Analysis. Learning Objectives Chapter 3 Decision Analysis To accompany Quantitative Analysis for Management, Eleventh Edition, by Render, Stair, and Hanna Power Point slides created by Brian Peterson Learning Objectives After completing

More information

The Course So Far. Decision Making in Deterministic Domains. Decision Making in Uncertain Domains. Next: Decision Making in Uncertain Domains

The Course So Far. Decision Making in Deterministic Domains. Decision Making in Uncertain Domains. Next: Decision Making in Uncertain Domains The Course So Far Decision Making in Deterministic Domains search planning Decision Making in Uncertain Domains Uncertainty: adversarial Minimax Next: Decision Making in Uncertain Domains Uncertainty:

More information

The Course So Far. Atomic agent: uninformed, informed, local Specific KR languages

The Course So Far. Atomic agent: uninformed, informed, local Specific KR languages The Course So Far Traditional AI: Deterministic single agent domains Atomic agent: uninformed, informed, local Specific KR languages Constraint Satisfaction Logic and Satisfiability STRIPS for Classical

More information

Introduction LEARNING OBJECTIVES. The Six Steps in Decision Making. Thompson Lumber Company. Thompson Lumber Company

Introduction LEARNING OBJECTIVES. The Six Steps in Decision Making. Thompson Lumber Company. Thompson Lumber Company Valua%on and pricing (November 5, 2013) Lecture 4 Decision making (part 1) Olivier J. de Jong, LL.M., MM., MBA, CFD, CFFA, AA www.olivierdejong.com LEARNING OBJECTIVES 1. List the steps of the decision-making

More information

Decision Analysis CHAPTER LEARNING OBJECTIVES CHAPTER OUTLINE. After completing this chapter, students will be able to:

Decision Analysis CHAPTER LEARNING OBJECTIVES CHAPTER OUTLINE. After completing this chapter, students will be able to: CHAPTER 3 Decision Analysis LEARNING OBJECTIVES After completing this chapter, students will be able to: 1. List the steps of the decision-making process. 2. Describe the types of decision-making environments.

More information

Module 15 July 28, 2014

Module 15 July 28, 2014 Module 15 July 28, 2014 General Approach to Decision Making Many Uses: Capacity Planning Product/Service Design Equipment Selection Location Planning Others Typically Used for Decisions Characterized by

More information

UNIT 5 DECISION MAKING

UNIT 5 DECISION MAKING UNIT 5 DECISION MAKING This unit: UNDER UNCERTAINTY Discusses the techniques to deal with uncertainties 1 INTRODUCTION Few decisions in construction industry are made with certainty. Need to look at: The

More information

Dr. Abdallah Abdallah Fall Term 2014

Dr. Abdallah Abdallah Fall Term 2014 Quantitative Analysis Dr. Abdallah Abdallah Fall Term 2014 1 Decision analysis Fundamentals of decision theory models Ch. 3 2 Decision theory Decision theory is an analytic and systemic way to tackle problems

More information

Decision Making. DKSharma

Decision Making. DKSharma Decision Making DKSharma Decision making Learning Objectives: To make the students understand the concepts of Decision making Decision making environment; Decision making under certainty; Decision making

More information

Causes of Poor Decisions

Causes of Poor Decisions Lecture 7: Decision Analysis Decision process Decision tree analysis The Decision Process Specify objectives and the criteria for making a choice Develop alternatives Analyze and compare alternatives Select

More information

IX. Decision Theory. A. Basic Definitions

IX. Decision Theory. A. Basic Definitions IX. Decision Theory Techniques used to find optimal solutions in situations where a decision maker is faced with several alternatives (Actions) and an uncertain or risk-filled future (Events or States

More information

Chapter 13 Decision Analysis

Chapter 13 Decision Analysis Problem Formulation Chapter 13 Decision Analysis Decision Making without Probabilities Decision Making with Probabilities Risk Analysis and Sensitivity Analysis Decision Analysis with Sample Information

More information

Decision Analysis Models

Decision Analysis Models Decision Analysis Models 1 Outline Decision Analysis Models Decision Making Under Ignorance and Risk Expected Value of Perfect Information Decision Trees Incorporating New Information Expected Value of

More information

PERT 12 Quantitative Tools (1)

PERT 12 Quantitative Tools (1) PERT 12 Quantitative Tools (1) Proses keputusan dalam operasi Fundamental Decisin Making, Tabel keputusan. Konsep Linear Programming Problem Formulasi Linear Programming Problem Penyelesaian Metode Grafis

More information

Chapter 18 Student Lecture Notes 18-1

Chapter 18 Student Lecture Notes 18-1 Chapter 18 Student Lecture Notes 18-1 Business Statistics: A Decision-Making Approach 6 th Edition Chapter 18 Introduction to Decision Analysis 5 Prentice-Hall, Inc. Chap 18-1 Chapter Goals After completing

More information

1.The 6 steps of the decision process are:

1.The 6 steps of the decision process are: 1.The 6 steps of the decision process are: a. Clearly define the problem Discussion and the factors that Questions influence it. b. Develop specific and measurable objectives. c. Develop a model. d. Evaluate

More information

Decision Making Models

Decision Making Models Decision Making Models Prof. Yongwon Seo (seoyw@cau.ac.kr) College of Business Administration, CAU Decision Theory Decision theory problems are characterized by the following: A list of alternatives. A

More information

Agenda. Lecture 2. Decision Analysis. Key Characteristics. Terminology. Structuring Decision Problems

Agenda. Lecture 2. Decision Analysis. Key Characteristics. Terminology. Structuring Decision Problems Agenda Lecture 2 Theory >Introduction to Making > Making Without Probabilities > Making With Probabilities >Expected Value of Perfect Information >Next Class 1 2 Analysis >Techniques used to make decisions

More information

Full file at CHAPTER 3 Decision Analysis

Full file at   CHAPTER 3 Decision Analysis CHAPTER 3 Decision Analysis TRUE/FALSE 3.1 Expected Monetary Value (EMV) is the average or expected monetary outcome of a decision if it can be repeated a large number of times. 3.2 Expected Monetary Value

More information

Decision Theory Using Probabilities, MV, EMV, EVPI and Other Techniques

Decision Theory Using Probabilities, MV, EMV, EVPI and Other Techniques 1 Decision Theory Using Probabilities, MV, EMV, EVPI and Other Techniques Thompson Lumber is looking at marketing a new product storage sheds. Mr. Thompson has identified three decision options (alternatives)

More information

Decision Making. D.K.Sharma

Decision Making. D.K.Sharma Decision Making D.K.Sharma 1 Decision making Learning Objectives: To make the students understand the concepts of Decision making Decision making environment; Decision making under certainty; Decision

More information

A B C D E F 1 PAYOFF TABLE 2. States of Nature

A B C D E F 1 PAYOFF TABLE 2. States of Nature Chapter Decision Analysis Problem Formulation Decision Making without Probabilities Decision Making with Probabilities Risk Analysis and Sensitivity Analysis Decision Analysis with Sample Information Computing

More information

Decision Analysis under Uncertainty. Christopher Grigoriou Executive MBA/HEC Lausanne

Decision Analysis under Uncertainty. Christopher Grigoriou Executive MBA/HEC Lausanne Decision Analysis under Uncertainty Christopher Grigoriou Executive MBA/HEC Lausanne 2007-2008 2008 Introduction Examples of decision making under uncertainty in the business world; => Trade-off between

More information

Textbook: pp Chapter 3: Decision Analysis

Textbook: pp Chapter 3: Decision Analysis 1 Textbook: pp. 81-128 Chapter 3: Decision Analysis 2 Learning Objectives After completing this chapter, students will be able to: List the steps of the decision-making process. Describe the types of decision-making

More information

Engineering Risk Benefit Analysis

Engineering Risk Benefit Analysis Engineering Risk Benefit Analysis 1.155, 2.943, 3.577, 6.938, 10.816, 13.621, 16.862, 22.82, ES.72, ES.721 A 1. The Multistage ecision Model George E. Apostolakis Massachusetts Institute of Technology

More information

Monash University School of Information Management and Systems IMS3001 Business Intelligence Systems Semester 1, 2004.

Monash University School of Information Management and Systems IMS3001 Business Intelligence Systems Semester 1, 2004. Exercise 7 1 : Decision Trees Monash University School of Information Management and Systems IMS3001 Business Intelligence Systems Semester 1, 2004 Tutorial Week 9 Purpose: This exercise is aimed at assisting

More information

Decision Analysis. Introduction. Job Counseling

Decision Analysis. Introduction. Job Counseling Decision Analysis Max, min, minimax, maximin, maximax, minimin All good cat names! 1 Introduction Models provide insight and understanding We make decisions Decision making is difficult because: future

More information

stake and attain maximum profitability. Therefore, it s judicious to employ the best practices in

stake and attain maximum profitability. Therefore, it s judicious to employ the best practices in 1 2 Success or failure of any undertaking mainly lies with the decisions made in every step of the undertaking. When it comes to business the main goal would be to maximize shareholders stake and attain

More information

Decision Making Supplement A

Decision Making Supplement A Decision Making Supplement A Break-Even Analysis Break-even analysis is used to compare processes by finding the volume at which two different processes have equal total costs. Break-even point is the

More information

19 Decision Making. Expected Monetary Value Expected Opportunity Loss Return-to-Risk Ratio Decision Making with Sample Information

19 Decision Making. Expected Monetary Value Expected Opportunity Loss Return-to-Risk Ratio Decision Making with Sample Information 19 Decision Making USING STATISTICS @ The Reliable Fund 19.1 Payoff Tables and Decision Trees 19.2 Criteria for Decision Making Maximax Payoff Maximin Payoff Expected Monetary Value Expected Opportunity

More information

DECISION ANALYSIS: INTRODUCTION. Métodos Cuantitativos M. En C. Eduardo Bustos Farias 1

DECISION ANALYSIS: INTRODUCTION. Métodos Cuantitativos M. En C. Eduardo Bustos Farias 1 DECISION ANALYSIS: INTRODUCTION Cuantitativos M. En C. Eduardo Bustos Farias 1 Agenda Decision analysis in general Structuring decision problems Decision making under uncertainty - without probability

More information

DECISION ANALYSIS. Decision often must be made in uncertain environments. Examples:

DECISION ANALYSIS. Decision often must be made in uncertain environments. Examples: DECISION ANALYSIS Introduction Decision often must be made in uncertain environments. Examples: Manufacturer introducing a new product in the marketplace. Government contractor bidding on a new contract.

More information

MGS 3100 Business Analysis. Chapter 8 Decision Analysis II. Construct tdecision i Tree. Example: Newsboy. Decision Tree

MGS 3100 Business Analysis. Chapter 8 Decision Analysis II. Construct tdecision i Tree. Example: Newsboy. Decision Tree MGS 3100 Business Analysis Chapter 8 Decision Analysis II Decision Tree An Alternative e (Graphical) Way to Represent and Solve Decision Problems Under Risk Particularly l Useful lfor Sequential Decisions

More information

DECISION ANALYSIS. (Hillier & Lieberman Introduction to Operations Research, 8 th edition)

DECISION ANALYSIS. (Hillier & Lieberman Introduction to Operations Research, 8 th edition) DECISION ANALYSIS (Hillier & Lieberman Introduction to Operations Research, 8 th edition) Introduction Decision often must be made in uncertain environments Examples: Manufacturer introducing a new product

More information

MBF1413 Quantitative Methods

MBF1413 Quantitative Methods MBF1413 Quantitative Methods Prepared by Dr Khairul Anuar 4: Decision Analysis Part 1 www.notes638.wordpress.com 1. Problem Formulation a. Influence Diagrams b. Payoffs c. Decision Trees Content 2. Decision

More information

Chapter 2 supplement. Decision Analysis

Chapter 2 supplement. Decision Analysis Chapter 2 supplement At the operational level hundreds of decisions are made in order to achieve local outcomes that contribute to the achievement of the company's overall strategic goal. These local outcomes

More information

SCHOOL OF BUSINESS, ECONOMICS AND MANAGEMENT. BF360 Operations Research

SCHOOL OF BUSINESS, ECONOMICS AND MANAGEMENT. BF360 Operations Research SCHOOL OF BUSINESS, ECONOMICS AND MANAGEMENT BF360 Operations Research Unit 5 Moses Mwale e-mail: moses.mwale@ictar.ac.zm BF360 Operations Research Contents Unit 5: Decision Analysis 3 5.1 Components

More information

Decision Analysis REVISED TEACHING SUGGESTIONS ALTERNATIVE EXAMPLES

Decision Analysis REVISED TEACHING SUGGESTIONS ALTERNATIVE EXAMPLES M03_REND6289_0_IM_C03.QXD 5/7/08 3:48 PM Page 7 3 C H A P T E R Decision Analysis TEACHING SUGGESTIONS Teaching Suggestion 3.: Using the Steps of the Decision-Making Process. The six steps used in decision

More information

INSE 6230 Total Quality Project Management

INSE 6230 Total Quality Project Management INSE 6230 Total Quality Project Management Lecture 6 Project Risk Management Project risk management is the art and science of identifying, analyzing, and responding to risk throughout the life of a project

More information

Resource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Decision Analysis

Resource Allocation and Decision Analysis (ECON 8010) Spring 2014 Foundations of Decision Analysis Resource Allocation and Decision Analysis (ECON 800) Spring 04 Foundations of Decision Analysis Reading: Decision Analysis (ECON 800 Coursepak, Page 5) Definitions and Concepts: Decision Analysis a logical

More information

Decision Making. BUS 735: Business Decision Making and Research. Learn how to conduct regression analysis with a dummy independent variable.

Decision Making. BUS 735: Business Decision Making and Research. Learn how to conduct regression analysis with a dummy independent variable. Making BUS 735: Business Making and Research 1 Goals of this section Specific goals: Learn how to conduct regression analysis with a dummy independent variable. Learning objectives: LO5: Be able to use

More information

Decision Analysis. Chapter Topics

Decision Analysis. Chapter Topics Decision Analysis Chapter Topics Components of Decision Making Decision Making without Probabilities Decision Making with Probabilities Decision Analysis with Additional Information Utility Decision Analysis

More information

Decision Analysis. Chapter Copyright 2010 Pearson Education, Inc. Publishing as Prentice Hall

Decision Analysis. Chapter Copyright 2010 Pearson Education, Inc. Publishing as Prentice Hall Decision Analysis Chapter 12 12-1 Chapter Topics Components of Decision Making Decision Making without Probabilities Decision Making with Probabilities Decision Analysis with Additional Information Utility

More information

Chapter 12. Decision Analysis

Chapter 12. Decision Analysis Page 1 of 80 Chapter 12. Decision Analysis [Page 514] [Page 515] In the previous chapters dealing with linear programming, models were formulated and solved in order to aid the manager in making a decision.

More information

Next Year s Demand -Alternatives- Low High Do nothing Expand Subcontract 40 70

Next Year s Demand -Alternatives- Low High Do nothing Expand Subcontract 40 70 Lesson 04 Decision Making Solutions Solved Problem #1: see text book Solved Problem #2: see textbook Solved Problem #3: see textbook Solved Problem #6: (costs) see textbook #1: A small building contractor

More information

Decision Analysis. Chapter 12. Chapter Topics. Decision Analysis Components of Decision Making. Decision Analysis Overview

Decision Analysis. Chapter 12. Chapter Topics. Decision Analysis Components of Decision Making. Decision Analysis Overview Chapter Topics Components of Decision Making with Additional Information Chapter 12 Utility 12-1 12-2 Overview Components of Decision Making A state of nature is an actual event that may occur in the future.

More information

Handling Uncertainty. Ender Ozcan given by Peter Blanchfield

Handling Uncertainty. Ender Ozcan given by Peter Blanchfield Handling Uncertainty Ender Ozcan given by Peter Blanchfield Objectives Be able to construct a payoff table to represent a decision problem. Be able to apply the maximin and maximax criteria to the table.

More information

Decision making under uncertainty

Decision making under uncertainty Decision making under uncertainty 1 Outline 1. Components of decision making 2. Criteria for decision making 3. Utility theory 4. Decision trees 5. Posterior probabilities using Bayes rule 6. The Monty

More information

Decision Making. BUS 735: Business Decision Making and Research. exercises. Assess what we have learned. 2 Decision Making Without Probabilities

Decision Making. BUS 735: Business Decision Making and Research. exercises. Assess what we have learned. 2 Decision Making Without Probabilities Making BUS 735: Business Making and Research 1 1.1 Goals and Agenda Goals and Agenda Learning Objective Learn how to make decisions with uncertainty, without using probabilities. Practice what we learn.

More information

Subject : Computer Science. Paper: Machine Learning. Module: Decision Theory and Bayesian Decision Theory. Module No: CS/ML/10.

Subject : Computer Science. Paper: Machine Learning. Module: Decision Theory and Bayesian Decision Theory. Module No: CS/ML/10. e-pg Pathshala Subject : Computer Science Paper: Machine Learning Module: Decision Theory and Bayesian Decision Theory Module No: CS/ML/0 Quadrant I e-text Welcome to the e-pg Pathshala Lecture Series

More information

DECISION MAKING. Decision making under conditions of uncertainty

DECISION MAKING. Decision making under conditions of uncertainty DECISION MAKING Decision making under conditions of uncertainty Set of States of nature: S 1,..., S j,..., S n Set of decision alternatives: d 1,...,d i,...,d m The outcome of the decision C ij depends

More information

TIm 206 Lecture notes Decision Analysis

TIm 206 Lecture notes Decision Analysis TIm 206 Lecture notes Decision Analysis Instructor: Kevin Ross 2005 Scribes: Geoff Ryder, Chris George, Lewis N 2010 Scribe: Aaron Michelony 1 Decision Analysis: A Framework for Rational Decision- Making

More information

Making Hard Decision. ENCE 627 Decision Analysis for Engineering. Identify the decision situation and understand objectives. Identify alternatives

Making Hard Decision. ENCE 627 Decision Analysis for Engineering. Identify the decision situation and understand objectives. Identify alternatives CHAPTER Duxbury Thomson Learning Making Hard Decision Third Edition RISK ATTITUDES A. J. Clark School of Engineering Department of Civil and Environmental Engineering 13 FALL 2003 By Dr. Ibrahim. Assakkaf

More information

36106 Managerial Decision Modeling Decision Analysis in Excel

36106 Managerial Decision Modeling Decision Analysis in Excel 36106 Managerial Decision Modeling Decision Analysis in Excel Kipp Martin University of Chicago Booth School of Business October 19, 2017 Reading and Excel Files Reading: Powell and Baker: Sections 13.1,

More information

UNIT 10 DECISION MAKING PROCESS

UNIT 10 DECISION MAKING PROCESS UIT 0 DECISIO MKIG PROCESS Structure 0. Introduction Objectives 0. Decision Making Under Risk Expected Monetary Value (EMV) Criterion Expected Opportunity Loss (EOL) Criterion Expected Profit with Perfect

More information

Learning Objectives = = where X i is the i t h outcome of a decision, p i is the probability of the i t h

Learning Objectives = = where X i is the i t h outcome of a decision, p i is the probability of the i t h Learning Objectives After reading Chapter 15 and working the problems for Chapter 15 in the textbook and in this Workbook, you should be able to: Distinguish between decision making under uncertainty and

More information

Subject O Basic of Operation Research (D-01) Date O 20/04/2011 Time O 11.00 to 02.00 Q.1 Define Operation Research and state its relation with decision making. (14) What are the opportunities and short

More information

DECISION ANALYSIS WITH SAMPLE INFORMATION

DECISION ANALYSIS WITH SAMPLE INFORMATION DECISION ANALYSIS WITH SAMPLE INFORMATION In the previous section, we saw how probability information about the states of nature affects the expected value calculations and therefore the decision recommendation.

More information

Chapter 4: Decision Analysis Suggested Solutions

Chapter 4: Decision Analysis Suggested Solutions Chapter 4: Decision Analysis Suggested Solutions Fall 2010 Que 1a. 250 25 75 b. Decision Maximum Minimum Profit Profit 250 25 75 Optimistic approach: select Conservative approach: select Regret or opportunity

More information

ESD.71 Engineering Systems Analysis for Design

ESD.71 Engineering Systems Analysis for Design ESD.71 Engineering Systems Analysis for Design Assignment 4 Solution November 18, 2003 15.1 Money Bags Call Bag A the bag with $640 and Bag B the one with $280. Also, denote the probabilities: P (A) =

More information

- Economic Climate Country Decline Stable Improve South Korea Philippines Mexico

- Economic Climate Country Decline Stable Improve South Korea Philippines Mexico 1) Micro-comp is a Toronto based manufacturer of personal computers. It is planning to build a new manufacturing and distribution facility in South Korea, Philippines, or Mexico. The profit (in $ millions)

More information

Objective of Decision Analysis. Determine an optimal decision under uncertain future events

Objective of Decision Analysis. Determine an optimal decision under uncertain future events Decision Analysis Objective of Decision Analysis Determine an optimal decision under uncertain future events Formulation of Decision Problem Clear statement of the problem Identify: The decision alternatives

More information

Resource Allocation and Decision Analysis (ECON 8010) Spring 2014 Fundamentals of Managerial and Strategic Decision-Making

Resource Allocation and Decision Analysis (ECON 8010) Spring 2014 Fundamentals of Managerial and Strategic Decision-Making Resource Allocation and Decision Analysis ECON 800) Spring 0 Fundamentals of Managerial and Strategic Decision-Making Reading: Relevant Costs and Revenues ECON 800 Coursepak, Page ) Definitions and Concepts:

More information

Chapter 17 Student Lecture Notes 17-1

Chapter 17 Student Lecture Notes 17-1 Chapter 17 Student Lecture Notes 17-1 Basic Business Statistics (9 th Edition) Chapter 17 Decision Making 2004 Prentice-Hall, Inc. Chap 17-1 Chapter Topics The Payoff Table and Decision Trees Opportunity

More information

Decision Analysis. Carlos A. Santos Silva June 5 th, 2009

Decision Analysis. Carlos A. Santos Silva June 5 th, 2009 Decision Analysis Carlos A. Santos Silva June 5 th, 2009 What is decision analysis? Often, there is more than one possible solution: Decision depends on the criteria Decision often must be made in uncertain

More information

Project Risk Analysis and Management Exercises (Part II, Chapters 6, 7)

Project Risk Analysis and Management Exercises (Part II, Chapters 6, 7) Project Risk Analysis and Management Exercises (Part II, Chapters 6, 7) Chapter II.6 Exercise 1 For the decision tree in Figure 1, assume Chance Events E and F are independent. a) Draw the appropriate

More information

Making Choices. Making Choices CHAPTER FALL ENCE 627 Decision Analysis for Engineering. Making Hard Decision. Third Edition

Making Choices. Making Choices CHAPTER FALL ENCE 627 Decision Analysis for Engineering. Making Hard Decision. Third Edition CHAPTER Duxbury Thomson Learning Making Hard Decision Making Choices Third Edition A. J. Clark School of Engineering Department of Civil and Environmental Engineering 4b FALL 23 By Dr. Ibrahim. Assakkaf

More information

GLS UNIVERSITY S FACULTY OF COMMERCE B. COM. SECOND YEAR SEMESTER IV STATISTICS FOR BUSINESS AND MANAGEMENT OBJECTIVE QUESTIONS

GLS UNIVERSITY S FACULTY OF COMMERCE B. COM. SECOND YEAR SEMESTER IV STATISTICS FOR BUSINESS AND MANAGEMENT OBJECTIVE QUESTIONS Q.1 Choose the correct options: GLS UNIVERSITY S FACULTY OF COMMERCE B. COM. SECOND YEAR SEMESTER IV STATISTICS FOR BUSINESS AND MANAGEMENT OBJECTIVE QUESTIONS 2017-18 Unit: 1 Differentiation and Applications

More information

G5212: Game Theory. Mark Dean. Spring 2017

G5212: Game Theory. Mark Dean. Spring 2017 G5212: Game Theory Mark Dean Spring 2017 Modelling Dynamics Up until now, our games have lacked any sort of dynamic aspect We have assumed that all players make decisions at the same time Or at least no

More information

Managerial Economics

Managerial Economics Managerial Economics Unit 9: Risk Analysis Rudolf Winter-Ebmer Johannes Kepler University Linz Winter Term 2015 Managerial Economics: Unit 9 - Risk Analysis 1 / 49 Objectives Explain how managers should

More information

Economics 111 Exam 1 Spring 2008 Prof Montgomery. Answer all questions. Explanations can be brief. 100 points possible.

Economics 111 Exam 1 Spring 2008 Prof Montgomery. Answer all questions. Explanations can be brief. 100 points possible. Economics 111 Exam 1 Spring 2008 Prof Montgomery Answer all questions. Explanations can be brief. 100 points possible. 1) [36 points] Suppose that, within the state of Wisconsin, market demand for cigarettes

More information

ECON 459 Game Theory. Lecture Notes Auctions. Luca Anderlini Spring 2017

ECON 459 Game Theory. Lecture Notes Auctions. Luca Anderlini Spring 2017 ECON 459 Game Theory Lecture Notes Auctions Luca Anderlini Spring 2017 These notes have been used and commented on before. If you can still spot any errors or have any suggestions for improvement, please

More information

MgtOp 470 Business Modeling with Spreadsheets Washington State University Sample Final Exam

MgtOp 470 Business Modeling with Spreadsheets Washington State University Sample Final Exam MgtOp 470 Business Modeling with Spreadsheets Washington State University Sample Final Exam Section 1 Multiple Choice 1. An information desk at a rest stop receives requests for assistance (from one server).

More information

Energy and public Policies

Energy and public Policies Energy and public Policies Decision making under uncertainty Contents of class #1 Page 1 1. Decision Criteria a. Dominated decisions b. Maxmin Criterion c. Maximax Criterion d. Minimax Regret Criterion

More information

April 28, Decision Analysis 2. Utility Theory The Value of Information

April 28, Decision Analysis 2. Utility Theory The Value of Information 15.053 April 28, 2005 Decision Analysis 2 Utility Theory The Value of Information 1 Lotteries and Utility L1 $50,000 $ 0 Lottery 1: a 50% chance at $50,000 and a 50% chance of nothing. L2 $20,000 Lottery

More information

TUTORIAL KIT OMEGA SEMESTER PROGRAMME: BANKING AND FINANCE

TUTORIAL KIT OMEGA SEMESTER PROGRAMME: BANKING AND FINANCE TUTORIAL KIT OMEGA SEMESTER PROGRAMME: BANKING AND FINANCE COURSE: BFN 425 QUANTITATIVE TECHNIQUE FOR FINANCIAL DECISIONS i DISCLAIMER The contents of this document are intended for practice and leaning

More information

Decision Theory. Refail N. Kasimbeyli

Decision Theory. Refail N. Kasimbeyli Decision Theory Refail N. Kasimbeyli Chapter 3 3 Utility Theory 3.1 Single-attribute utility 3.2 Interpreting utility functions 3.3 Utility functions for non-monetary attributes 3.4 The axioms of utility

More information

8. Uncertainty. Reading: BGVW, Chapter 7

8. Uncertainty. Reading: BGVW, Chapter 7 8. Uncertainty Reading: BGVW, Chapter 7 1. Introduction Uncertainties abound future: incomes/prices/populations analysis: dose-response/valuation/climate/effects of regulation on environmental quality/longevity

More information

Notes 10: Risk and Uncertainty

Notes 10: Risk and Uncertainty Economics 335 April 19, 1999 A. Introduction Notes 10: Risk and Uncertainty 1. Basic Types of Uncertainty in Agriculture a. production b. prices 2. Examples of Uncertainty in Agriculture a. crop yields

More information

Thomas Saaty

Thomas Saaty An Illustrated Guide to the ANALYTIC HIERARCHY PROCESS Oliver Meixner & Rainer Haas Institute of Marketing & Innovation University of Natural Resources and Life Sciences, Vienna http://www.wiso.boku.ac.at/mi/

More information

Comparison of Decision-making under Uncertainty Investment Strategies with the Money Market

Comparison of Decision-making under Uncertainty Investment Strategies with the Money Market IBIMA Publishing Journal of Financial Studies and Research http://www.ibimapublishing.com/journals/jfsr/jfsr.html Vol. 2011 (2011), Article ID 373376, 16 pages DOI: 10.5171/2011.373376 Comparison of Decision-making

More information

Introduction to Decision Analysis

Introduction to Decision Analysis Session # Page Decisions Under Certainty State of nature is certain (one state) Select decision that yields the highest return Examples: Product Mix Diet Problem Distribution Scheduling Decisions Under

More information

P1: PBU/OVY P2: PBU/OVY QC: PBU/OVY T1: PBU GTBL GTBL032-Black-v13 January 22, :43

P1: PBU/OVY P2: PBU/OVY QC: PBU/OVY T1: PBU GTBL GTBL032-Black-v13 January 22, :43 CHAPTER19 Decision Analysis LEARNING OBJECTIVES This chapter describes how to use decision analysis to improve management decisions, thereby enabling you to: 1. Learn about decision making under certainty,

More information

Optimization Methods in Management Science

Optimization Methods in Management Science Problem Set Rules: Optimization Methods in Management Science MIT 15.053, Spring 2013 Problem Set 6, Due: Thursday April 11th, 2013 1. Each student should hand in an individual problem set. 2. Discussing

More information

Lecture 12: Introduction to reasoning under uncertainty. Actions and Consequences

Lecture 12: Introduction to reasoning under uncertainty. Actions and Consequences Lecture 12: Introduction to reasoning under uncertainty Preferences Utility functions Maximizing expected utility Value of information Bandit problems and the exploration-exploitation trade-off COMP-424,

More information

A Taxonomy of Decision Models

A Taxonomy of Decision Models Decision Trees and Influence Diagrams Prof. Carlos Bana e Costa Lecture topics: Decision trees and influence diagrams Value of information and control A case study: Drilling for oil References: Clemen,

More information

Decision Analysis CHAPTER 19

Decision Analysis CHAPTER 19 CHAPTER 19 Decision Analysis LEARNING OBJECTIVES This chapter describes how to use decision analysis to improve management decisions, thereby enabling you to: 1. Learn about decision making under certainty,

More information

Decision Making Under Risk Probability Historical Data (relative frequency) (e.g Insurance) Cause and Effect Models (e.g.

Decision Making Under Risk Probability Historical Data (relative frequency) (e.g Insurance) Cause and Effect Models (e.g. Decision Making Under Risk Probability Historical Data (relative frequency) (e.g Insurance) Cause and Effect Models (e.g. casinos, weather forecasting) Subjective Probability Often, the decision maker

More information

Operation Research II

Operation Research II Operation Research II Johan Oscar Ong, ST, MT Grading Requirements: Min 80% Present in Class Having Good Attitude Score/Grade : Quiz and Assignment : 30% Mid test (UTS) : 35% Final Test (UAS) : 35% No

More information

INTERNATIONAL UNIVERSITY OF JAPAN Public Management and Policy Analysis Program Graduate School of International Relations

INTERNATIONAL UNIVERSITY OF JAPAN Public Management and Policy Analysis Program Graduate School of International Relations Hun Myoung Park (5/2/2018) Decision Analysis: 1 INTERNATIONAL UNIVERSITY OF JAPAN Public Management and Policy Analysis Program Graduate School of International Relations DCC5350/ADC5005 (2 Credits) Public

More information

Known unknowns and unknown unknowns: uncertainty from the decision-makers perspective. Neil Hawkins Oxford Outcomes

Known unknowns and unknown unknowns: uncertainty from the decision-makers perspective. Neil Hawkins Oxford Outcomes Known unknowns and unknown unknowns: uncertainty from the decision-makers perspective Neil Hawkins Oxford Outcomes Outline Uncertainty Decision making under uncertainty Role of sensitivity analysis Fundamental

More information

Decision Analysis CHAPTER 19 LEARNING OBJECTIVES

Decision Analysis CHAPTER 19 LEARNING OBJECTIVES CHAPTER 19 Decision Analysis LEARNING OBJECTIVES This chapter describes how to use decision analysis to improve management decisions, thereby enabling you to: 1. Make decisions under certainty by constructing

More information

Mohammad Hossein Manshaei 1394

Mohammad Hossein Manshaei 1394 Mohammad Hossein Manshaei manshaei@gmail.com 1394 Let s play sequentially! 1. Sequential vs Simultaneous Moves. Extensive Forms (Trees) 3. Analyzing Dynamic Games: Backward Induction 4. Moral Hazard 5.

More information

ECON 312: MICROECONOMICS II Lecture 11: W/C 25 th April 2016 Uncertainty and Risk Dr Ebo Turkson

ECON 312: MICROECONOMICS II Lecture 11: W/C 25 th April 2016 Uncertainty and Risk Dr Ebo Turkson ECON 312: MICROECONOMICS II Lecture 11: W/C 25 th April 2016 Uncertainty and Risk Dr Ebo Turkson Chapter 17 Uncertainty Topics Degree of Risk. Decision Making Under Uncertainty. Avoiding Risk. Investing

More information

At the operational level, hundreds of decisions are made in order to achieve local outcomes

At the operational level, hundreds of decisions are made in order to achieve local outcomes BMAppendixA.indd Page 592 14/03/14 9:46 PM user APPENDIXA Operational Decision-Making Tools: Decision Analysis LEARNING OBJECTIVES < Decision Analysis (With and Without Probabilities) At the operational

More information

Review of Expected Operations

Review of Expected Operations Economic Risk and Decision Analysis for Oil and Gas Industry CE81.98 School of Engineering and Technology Asian Institute of Technology January Semester Presented by Dr. Thitisak Boonpramote Department

More information

ECONS 424 STRATEGY AND GAME THEORY HANDOUT ON PERFECT BAYESIAN EQUILIBRIUM- III Semi-Separating equilibrium

ECONS 424 STRATEGY AND GAME THEORY HANDOUT ON PERFECT BAYESIAN EQUILIBRIUM- III Semi-Separating equilibrium ECONS 424 STRATEGY AND GAME THEORY HANDOUT ON PERFECT BAYESIAN EQUILIBRIUM- III Semi-Separating equilibrium Let us consider the following sequential game with incomplete information. Two players are playing

More information

Binomial Option Pricing

Binomial Option Pricing Binomial Option Pricing The wonderful Cox Ross Rubinstein model Nico van der Wijst 1 D. van der Wijst Finance for science and technology students 1 Introduction 2 3 4 2 D. van der Wijst Finance for science

More information

In general, the value of any asset is the present value of the expected cash flows on

In general, the value of any asset is the present value of the expected cash flows on ch05_p087_110.qxp 11/30/11 2:00 PM Page 87 CHAPTER 5 Option Pricing Theory and Models In general, the value of any asset is the present value of the expected cash flows on that asset. This section will

More information

Comparative Study between Linear and Graphical Methods in Solving Optimization Problems

Comparative Study between Linear and Graphical Methods in Solving Optimization Problems Comparative Study between Linear and Graphical Methods in Solving Optimization Problems Mona M Abd El-Kareem Abstract The main target of this paper is to establish a comparative study between the performance

More information