Decision Tree Model
Instructor’s Manual
Chapter 1
1
Chapter 1
I
1.1
Chapter Outline
A Decision Tree Model and Its Analysis • The following concepts are introduced through the use of a simple decision tree example (the Bill Sampras ' summer job decision): Decision tree Decision node Event node Mutually exclusive and collectively exhaustive set of events Branches and final values Expected Monetary Value (EMV) Optimal decision strategy • Introduction of the folding back or backward induction procedure for solving a decision tree. • Discussion on sensitivity analysis in a decision tree. Summary of the General Method of Decision Analysis. Another Decision Tree Model and Its Analysis • Detailed formulation, discussion, and solution of the Bio-Imagining example, which is a problem with more alternatives and event nodes than the Bill Sampras example. • Discussion on sensitivity analysis and analysis of other alternatives faced by Bio-Imaging and Medtech (a related company). The Need for a Systematic Theory of Probability • Discussion on the "Suds-Away" dishwashing detergent example, which introduces the need for probability theory to properly assign probabilities to branches at event nodes (conditional probabilities). • The solution to this example is postponed to Chapter 2. Further Issues and Concluding Remarks On Decision Analysis • Discussion on the following subjects: • Risk analysis when using the EMV procedure. • Non-quantifiable consequences not necessarily considered by a decision tree analysis. • Benefits of using decision analysis: Clarity of decision problem Insight into decision process Importance of key data Suggestion of other ways to look at the problem
1.2 1.3
1.4
1.5
Manual to accompany Data, Models & Decisions: The Fundamentals of Management Science by Bertsimas and Freund. Copyright 2000, South-Western College Publishing. Prepared by Manuel Nunez, Chapman University.
Instructor’s Manual
Chapter 1
2
II
1.
Teaching Tips
Generally,
Chapter 1
1
Chapter 1
I
1.1
Chapter Outline
A Decision Tree Model and Its Analysis • The following concepts are introduced through the use of a simple decision tree example (the Bill Sampras ' summer job decision): Decision tree Decision node Event node Mutually exclusive and collectively exhaustive set of events Branches and final values Expected Monetary Value (EMV) Optimal decision strategy • Introduction of the folding back or backward induction procedure for solving a decision tree. • Discussion on sensitivity analysis in a decision tree. Summary of the General Method of Decision Analysis. Another Decision Tree Model and Its Analysis • Detailed formulation, discussion, and solution of the Bio-Imagining example, which is a problem with more alternatives and event nodes than the Bill Sampras example. • Discussion on sensitivity analysis and analysis of other alternatives faced by Bio-Imaging and Medtech (a related company). The Need for a Systematic Theory of Probability • Discussion on the "Suds-Away" dishwashing detergent example, which introduces the need for probability theory to properly assign probabilities to branches at event nodes (conditional probabilities). • The solution to this example is postponed to Chapter 2. Further Issues and Concluding Remarks On Decision Analysis • Discussion on the following subjects: • Risk analysis when using the EMV procedure. • Non-quantifiable consequences not necessarily considered by a decision tree analysis. • Benefits of using decision analysis: Clarity of decision problem Insight into decision process Importance of key data Suggestion of other ways to look at the problem
1.2 1.3
1.4
1.5
Manual to accompany Data, Models & Decisions: The Fundamentals of Management Science by Bertsimas and Freund. Copyright 2000, South-Western College Publishing. Prepared by Manuel Nunez, Chapman University.
Instructor’s Manual
Chapter 1
2
II
1.
Teaching Tips
Generally,