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This page contains computer files and source code (Excel, Netica, DPL, YAAHP, Python scripts, Jupyter notebooks, etc) that are covered in the lectures.
Chapter 2: Probabilistic Thinking
Excel Files
DPL Models
Chapter 4: Basic Decision Analysis
Python Code and Jupyter Notebooks
- 4.2.9 Party Problem: Fitting an equation to Kim's utility function
- 4.7 Party Problem: Stochastic Dominance Analysis
Excel Files
- 4.8.1 Kim's Party Problem with Treeplan (Complete analysis with Base model, Sensitivity Analysis, Value of Perfect and Imperfect Information Analysis)
- 4.8.1 Kim's Party Problem with Dynamic Sensitivity Analysis with Treeplan
- 4.8.1 Jane's Party Problem with Treeplan (Complete analysis with Base model, Sensitivity Analysis, Value of Perfect and Imperfect Information Analysis)
- 4.8.2 Kim Party Problem with Precision Tree (Base Model, Risk Profiles, Sensitivity Analysis)
Chapters 5: Influence Diagrams
DPL Models
Chapter 6: Advanced Decision Analysis - Risk Preferences
Python Code and Jupyter Notebooks
- 6.1.3 Compute Buying Price using root function
- 6.3.3 Compute Risk Tolerance using 50-50 CE method with root function
- 6.5.1 Compute NPV of multi-period cash flows using numpy_financial package
Excel Files
- 6.1.3 Compute Buying and Selling Price using Excel Goal Seek
- 6.3.3 Finding Risk Tolerance using 50-50 CE method with Excel Goal Seek
- 6.5.1 NPV of cash flows
- 6.5.2 Utility of NPV with Treeplan
DPL Models
- 6.1.2 Compute Selling Price using DPL Rainbow Diagram
- 6.1.3 Compute Buying Price using DPL Rainbow Diagram
- 6.5.2 Utility of NPV in DPL version 1 (Using arrays of cash flows and NPV function in DPL)
- 6.5.2 Utility of NPV in DPL version 2 (Using local discounted get/pay cashflows along tree branches)
Chapter 7: Probability Distribution Assessment
Python Code and Jupyter Notebooks
Chapter 8: Solving Complex Problems - The DA Process/Cycle
Python Code and Jupyter Notebooks
DPL Models
- 8.3.1 Exxoff Case Study - DPL file with Models and Outputs
- 8.3.1 Exxoff Case Study - DPL file with Models only (lean and clean)
- 8.4.4 LIM Case Study - DPL file with Models and Outputs
- 8.4.4 LIM Case Study - DPL file with Models only (lean and clean)
- Exercise: Biotecno Problem - DPL file with Models and Outputs
- Exercise: Biotecno Problem - DPL file with Models only (lean and clean)
Chapter 9: MCDM I - Analytic Hierrchy Process
Python Code and Jupyter Notebooks
- 9.2.6 Compute AHP matrix using Linear Algerba method
- 9.2.6 Compute AHP matrix using np.linalg.eig_function
- 9.2.7 Compute AHP matrix using Row Geometric Means Approximation method
- 9.2.7 Compute AHP matrix using Column Normalization Approximation method
- 9.2.8 Compute AHP matrix using Power Iterations method
Excel Files
- 9.2.x_Compute AHP Matrix using User Defined Functions (All Methods)
- 9.2.6 Compute AHP Matrix using Linear Algebra method templates
- 9.2.7 Compute AHP Matrix using Row Geometric Means Approximation method templates
- 9.2.7 Compute AHP Matrix using Columns Normalization Approximation method templates
- 9.4.2 Job Selection Problem with Sensitivity Analysis using UDF AHPmat_algebra
- 9.4.2 Job Selection Problem with Sensitivity Analysis using UDF AHPmat_power
- 9.5 Job Selection Problem with SubcCriteria Sensitivity Analysis using UDF AHPmat_algebra
- 9.5 Job Selection Problem with SubcCriteria Sensitivity Analysis using UDF AHPmat_power
- 9.6.2 AHP Rating Method with Sensitivity: Employees Evaluation with UDF AHPmat_power
- 9.6.2 AHP Rating Method with Sensitivity: Employees Evaluation with UDF AHPmat_algebra
YAAHP Models
Chapter 10: MCDM II - Multiple Attribute Decision Analysis
Python Code and Jupyter Notebooks
Excel Files
- 10.3 Drug Counseling Center Relocation Problem using UDF AHPmat_power
- 10.3 Drug Counseling Center Relocation Problem using UDF AHPmat_algebra
DPL Models
- 10.1.3 DPL file for MAUT Example 1 (Adveristing Campaign Decision)
- 10.1.3 DPL file for MAUT Example 2 (Lisa's Holiday Problem)