Algorithmic decision making with Python resources : from multicriteria performance records to decision algorithms via bipolar-valued outranking digraphs / Raymond Bisdorff.
Bisdorff, Raymond, author.
2022
HD30.23
Available Online

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Title Algorithmic decision making with Python resources : from multicriteria performance records to decision algorithms via bipolar-valued outranking digraphs / Raymond Bisdorff.
Author Bisdorff, Raymond, author.
ISBN 9783030909284 (electronic bk.)
303090928X (electronic bk.)
9783030909277
3030909271
DOI https://doi.org/10.1007/978-3-030-90928-4
Published Cham : Springer, 2022.
Language English
Description 1 online resource.
Item Number 10.1007/978-3-030-90928-4 doi
Call Number HD30.23
Dewey Decimal Classification 658.403
Summary This book describes Python3 programming resources for implementing decision aiding algorithms in the context of a bipolar-valued outranking approach. These computing resources, made available under the name Digraph3, are useful in the field of Algorithmic Decision Theory and more specifically in outranking-based Multiple-Criteria Decision Aiding (MCDA). The first part of the book presents a set of tutorials introducing the Digraph3 collection of Python3 modules and its main objects, such as bipolar-valued digraphs and outranking digraphs. In eight methodological chapters, the second part illustrates multiple-criteria evaluation models and decision algorithms. These chapters are largely problem-oriented and demonstrate how to edit a new multiple-criteria performance tableau, how to build a best choice recommendation, how to compute the winner of an election and how to make rankings or ratings using incommensurable criteria. The books third part presents three real-world decision case studies, while the fourth part addresses more advanced topics, such as computing ordinal correlations between bipolar-valued outranking digraphs, computing kernels in bipolar-valued digraphs, testing for confidence or stability of outranking statements when facing uncertain or solely ordinal criteria significance weights, and tempering plurality tyranny effects in social choice problems. The fifth and last part is more specifically focused on working with undirected graphs, tree graphs and forests. The closing chapter explores comparability, split, interval and permutation graphs. The book is primarily intended for graduate students in management sciences, computational statistics and operations research. The chapters presenting algorithms for ranking multicriteria performance records will be of computational interest for designers of web recommender systems. Similarly, the relative and absolute quantile-rating algorithms, discussed and illustrated in several chapters, will be of practical interest to public and private performance auditors.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed April 8, 2022).
Series International series in operations research & management science ; v. 324.
Available in Other Form Print version: 3030909271
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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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. Alices 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.

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