國立高雄大學圖資館 |

Algorithmic decision making with Python resourcesfrom multicriteria performance records to decision algorithms via bipolar-valued outranking digraphs /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Algorithmic decision making with Python resourcesby Raymond Bisdorff.
其他題名:	from multicriteria performance records to decision algorithms via bipolar-valued outranking digraphs /
作者:	Bisdorff, Raymond.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xxxvii, 346 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Decision makingData processing.
電子資源:	https://doi.org/10.1007/978-3-030-90928-4
ISBN:	9783030909284$q(electronic bk.)

Algorithmic decision making with Python resourcesfrom multicriteria performance records to decision algorithms via bipolar-valued outranking digraphs /
Bisdorff, Raymond.

Algorithmic decision making with Python resourcesfrom multicriteria performance records to decision algorithms via bipolar-valued outranking digraphs /[electronic resource] :by Raymond Bisdorff. - Cham :Springer International Publishing :2022. - xxxvii, 346 p. :ill., digital ;24 cm. - International series in operations research & management science,v. 3242214-7934 ;. - International series in operations research & management science ;v. 140..

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.

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 book's 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.

ISBN: 9783030909284$q(electronic bk.)

Standard No.: 10.1007/978-3-030-90928-4doiSubjects--Topical Terms:

235779
Decision making
--Data processing.

LC Class. No.: HD30.23 / .B57 2022

Dewey Class. No.: 658.403

Algorithmic decision making with Python resourcesfrom multicriteria performance records to decision algorithms via bipolar-valued outranking digraphs /
LDR:04705nmm a2200361 a 4500 001 622243
003 DE-He213
005 20220330045943.0
006 m d
007 cr nn 008maaau
008 221209s2022 sz s 0 eng d
020 $a 9783030909284$q(electronic bk.)
020 $a 9783030909277$q(paper)
024 7 $a 10.1007/978-3-030-90928-4 $2 doi
035 $a 978-3-030-90928-4
040 $a GP $c GP
041 0 $a eng
050 4 $a HD30.23 $b .B57 2022
072 7 $a KJT $2 bicssc
072 7 $a KJMD $2 bicssc
072 7 $a BUS049000 $2 bisacsh
072 7 $a KJT $2 thema
072 7 $a KJMD $2 thema
082 0 4 $a 658.403 $2 23
090 $a HD30.23 $b .B621 2022
100 1 $a Bisdorff, Raymond. $3 726340
245 1 0 $a Algorithmic decision making with Python resources $h [electronic resource] : $b from multicriteria performance records to decision algorithms via bipolar-valued outranking digraphs / $c by Raymond Bisdorff.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xxxvii, 346 p. : $b ill., digital ; $c 24 cm.
490 1 $a International series in operations research & management science, $x 2214-7934 ; $v v. 324
505 0 $a 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.
520 $a 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 book's 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.
650 0 $a Decision making $x Data processing. $3 235779
650 0 $a Python (Computer program language) $3 215247
650 1 4 $a Operations Research and Decision Theory. $3 914898
650 2 4 $a Graph Theory. $3 522732
650 2 4 $a Mathematical Applications in Computer Science. $3 530811
650 2 4 $a Statistics and Computing. $3 922173
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a International series in operations research & management science ; $v v. 140. $3 490892
856 4 0 $u https://doi.org/10.1007/978-3-030-90928-4
950 $a Business and Management (SpringerNature-41169)