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Decision-Making Techniques The Decision-Making Techniques programme is written by David Targett, Professor of Information Management, Imperial College Management School, London, formerly ICL Professor of Information Systems at the School of Management, University of Bath, and before that Senior Lecturer in Decision Sciences at the London Business School. Professor Targett has many years of experience teaching executives to add numeracy to their list of management skills and become balanced decision makers. His style is based on demystifying complex techniques and demonstrating clearly their practical relevance as well as their shortcomings. He is the author of the Quantitative Methods programme in the Edinburgh Business School MBA series, and his books, including Coping with numbers and The Economist Guide to Business Numeracy have been sold throughout the world. One of his many articles, a study on the provision of management the Pergamon Prize in 1986.



He was part of the team that designed London Business School's highly successful parttime MBA programme of which he was the Director from 1985 to 1988. During this time he extended the international focus of the teaching by leading pioneering study groups to Hong Kong, Singapore and the United States of America. He has taught on all major management programmes at Imperial College, the University of Bath and the London Business School. He has developed and run management education courses involving scores of major companies including:

Marks & Spencer Shell



~Decision-Mal<ing Techniques A Distance Learning Programme

David Targett



~Decision-Making Techniques A Distance Learning Programme

David Targett

United Kingdom Edinburgh


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Contents Module 1


Decision Analysis 1.1 Introduction 1.2 Prerequisite Ideas 1.3 Applications 1.4 1.5 1.6 1.7

1/1 1/2 1/3 1/5 1/7 1/12 1/15 1/17

Carrying out Decision Analysis The Value of Additional Information The Validity of the EMV Criterion Concluding Remarks

Module 2

Advanced Decision Analysis Introduction 2.1 Continuous Probability Distributions 2.2 2.3 Assessment of Subjective Probabilities 2.4 Revising Probabilities 2.5 Concluding Remarks

2/1 2/1 2/2 2/7 2/13 2/19

Module 3

Linear Programming 3.1 Introduction 3.2 Formulating an LP Problem 3.3 Applications of LP 3.4 Graphical Solution 3.5 Computer Solution

3/1 3/2 3/2 3/5 3/6 3/11 3/12 3/13 3/13 3/15 3/17 3/18


3.6 3.7 3.8 3.9 3.10 3.11 Module 4

Infeasibility Unboundedness Redundancy Slack Minimisation Problems Concluding Remarks

Extending the Ideas of Linear Programming Introduction 4.1 4.2 Deriving Additional Information from LP 4.3 Decision Problems with LP Associations 4.4 Integer Programming 4.5 4.6 4.7

Non-Linear Programming Programming under Uncertainty Concluding Remarks

4/1 4/1 4/2 4/9 4/13 4/13 4/15 4/16

~ Module 5


Simulation Introduction 5.1 5.2 Applying Simulation to a Distribution 5.3 Types of Simulation 5.4 Monte Carlo Technique



Business School


5/1 5/1 5/3 5/5 5/6


Flowcharts How the Simulation Works Interpreting the Output Risk Analysis Concluding Remarks

5/8 5/10 5113 5/15 5/17

Network Planning Techniques for Large Projects


6.1 6.2 6.3 6.4 6.5 6.6 6.7


Introduction Applications Drawing the Network Analysing the Network Handling Uncertainty Time-Cost Trade-Offs Concluding Remarks

Decisions and Information

6/3 6/4 6/8

6/14 6/17 6/22


7.1 7.2 7.3 7.4 7.5

Introduction Technical Issues Applications Past and Present Benefits

7.6 7.7 7.8

Implications for the Individual The Future Concluding Remarks

7/1 7/2 7/2 7/4

7/6 7/10 7/13 7/16 7/18

Module 1

Decision Analysis Contents 1.1



1.2 1.2.1 1.2.2 1.2.3

Prerequisite Ideas Decision Trees Payoffs Expected Monetary Value (EMV)

1/3 1/3 1/3 1/4

1.3 1.3.1 1.3.2 1.3.3

Applications Oil Explorations Technological Projects Medical Research

1/5 1/5 1/6 1/6

1.4 1.4.1 1.4.2

Carrying out Decision Analysis Stage 1: Draw the Tree Stage 2: Insert Payoffs

1n 1n

1.4.3 1.4.4

Stage 3: Insert Probabilities Stage 4: Roll-back Procedure

1/9 1/10


Stage 5: Summarise the Optimal Path and Draw up the Risk Profile


1.5 1.5.1 1.5.2

The Value of Additional Information Expected Value of Sample Information Expected Value of Perfect Information

1/12 1/13 1/13


The Validity of the EMV Criterion



Concluding Remarks



Review Questions


Case Study 1.1


Case Study 1.2


Case Study 1.3


Appendix to Module 1


Decision analysis is a technique which is able to determine the optimal action to take in certain types of decision problem. This module describes how to carry out a decision analysis and the areas in which it can be applied. It also explains some of the additional management information that can be derived. Some simple ideas concerning probability theory will be used and the reader who has never met probabilities before should take a little while to read the appendix to this module. By the end of the module the reader should know where decision analysis can be used and have the necessary skills for applying it to a range of problems.

Decision Making Techniques