Decision Analysis
ASW/QMB-Ch.04 3/8/01 10:35 AM Page 96
Chapter 4
DECISION ANALYSIS
CONTENTS 4.1 PROBLEM FORMULATION Influence Diagrams Payoff Tables Decision Trees DECISION MAKING WITHOUT PROBABILITIES Optimistic Approach Conservative Approach Minimax Regret Approach DECISION MAKING WITH PROBABILITIES Expected Value of Perfect Information RISK ANALYSIS AND SENSITIVITY ANALYSIS Risk Analysis Sensitivity Analysis DECISION ANALYSIS WITH SAMPLE INFORMATION An Influence Diagram A Decision Tree Decision Strategy Risk Profile Expected Value of Sample Information Efficiency of Sample Information COMPUTING BRANCH PROBABILITIES
4.2
4.3 4.4
4.5
4.6
Decision analysis can be used to determine an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain or risk-filled pattern of future events. For example, a global manufacturer might be interested in determining the best location for a new plant. Suppose that the manufacturer has identified five decision alternatives corresponding to five plant locations in different countries. Making the plant location decision is complicated by factors such as the world economy, demand in various regions of the world, labor availability, raw material costs, transportation costs, and so on. In such a problem, several scenarios could be developed to describe how the various factors combine to form the possible uncertain future events. Then probabilities can be assigned to the events. Using profit or cost as a measure of the consequence for each decision alternative and each future event combination, the best plant location can be selected. Even when a careful decision analysis has been conducted, the uncertain future events make the final consequence uncertain. In some cases, the selected decision alternative may provide good or excellent results. In other cases, a relatively unlikely future event may occur causing the selected decision alternative to provide only fair or even poor results. The
Chapter 4
DECISION ANALYSIS
CONTENTS 4.1 PROBLEM FORMULATION Influence Diagrams Payoff Tables Decision Trees DECISION MAKING WITHOUT PROBABILITIES Optimistic Approach Conservative Approach Minimax Regret Approach DECISION MAKING WITH PROBABILITIES Expected Value of Perfect Information RISK ANALYSIS AND SENSITIVITY ANALYSIS Risk Analysis Sensitivity Analysis DECISION ANALYSIS WITH SAMPLE INFORMATION An Influence Diagram A Decision Tree Decision Strategy Risk Profile Expected Value of Sample Information Efficiency of Sample Information COMPUTING BRANCH PROBABILITIES
4.2
4.3 4.4
4.5
4.6
Decision analysis can be used to determine an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain or risk-filled pattern of future events. For example, a global manufacturer might be interested in determining the best location for a new plant. Suppose that the manufacturer has identified five decision alternatives corresponding to five plant locations in different countries. Making the plant location decision is complicated by factors such as the world economy, demand in various regions of the world, labor availability, raw material costs, transportation costs, and so on. In such a problem, several scenarios could be developed to describe how the various factors combine to form the possible uncertain future events. Then probabilities can be assigned to the events. Using profit or cost as a measure of the consequence for each decision alternative and each future event combination, the best plant location can be selected. Even when a careful decision analysis has been conducted, the uncertain future events make the final consequence uncertain. In some cases, the selected decision alternative may provide good or excellent results. In other cases, a relatively unlikely future event may occur causing the selected decision alternative to provide only fair or even poor results. The