Algorithmic Decision Making with Python Resources: From Multicriteria Performance Records to Decision Algorithms via Bipolar-Valued Outranking Digraph

Part I: Introduction to the DIGRAPH3 Python Resources.- 1. Working with the DIGRAPH3 Python Resources.- 2. Working with Bipolar-Valued Digraphs.- 3. Working with Outranking Digraphs.- Part II: Evaluation Models and Decision Algorithms.- 4. Building a Best Choice Recommendation.- 5. How to Create a New Multiple-Criteria Performance Tableau.- 6. Generating Random Performance Tableaux.- 7. Who Wins the Election?.- 8. Ranking with Multiple Incommensurable Criteria.- 9. Rating by Sorting into Relative Performance Quantiles.- 10. Rating-by-Ranking with Learned Performance Quantile Norms.- 11. HPC Ranking of Big Performance Tableaux.- Part III: Evaluation and Decision Case Studies.- 12. Alice's Best Choice: A Selection Case Study.- 13. The Best Academic Computer Science Depts: A Ranking Case Study.- 14. The Best Students, Where Do They Study? A Rating Case Study.- 15. Exercises.- Part IV: Advanced Topics.- 16. On Measuring the Fitness of a Multiple-Criteria Ranking.- 17. On Computing Digraph Kernels.- 18. On Confident Outrankings with Uncertain Criteria Significance Weights.- 19. Robustness Analysis of Outranking Digraphs.- 20. Tempering Plurality Tyranny Effects in Social Choice.- Part V: Working with Undirected Graphs.- 21. Bipolar-Valued Undirected Graphs.- 22. On Tree Graphs and Graph Forests.- 23. About Split, Comparability, Interval, and Permutation Graphs.