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001445606 019__ $$a1308795770$$a1309025600$$a1309056363
001445606 020__ $$a9783030909284$$q(electronic bk.)
001445606 020__ $$a303090928X$$q(electronic bk.)
001445606 020__ $$z9783030909277
001445606 020__ $$z3030909271
001445606 0247_ $$a10.1007/978-3-030-90928-4$$2doi
001445606 035__ $$aSP(OCoLC)1308393033
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001445606 049__ $$aISEA
001445606 050_4 $$aHD30.23
001445606 08204 $$a658.403$$223
001445606 1001_ $$aBisdorff, Raymond,$$eauthor.
001445606 24510 $$aAlgorithmic decision making with Python resources :$$bfrom multicriteria performance records to decision algorithms via bipolar-valued outranking digraphs /$$cRaymond Bisdorff.
001445606 264_1 $$aCham :$$bSpringer,$$c2022.
001445606 300__ $$a1 online resource.
001445606 4901_ $$aInternational series in operations research & management science ;$$vvolume 324
001445606 5050_ $$aPart 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.
001445606 506__ $$aAccess limited to authorized users.
001445606 520__ $$aThis 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.
001445606 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 8, 2022).
001445606 650_0 $$aDecision making$$xData processing.
001445606 650_0 $$aPython (Computer program language)
001445606 650_6 $$aPrise de décision$$xInformatique.
001445606 650_6 $$aPython (Langage de programmation)
001445606 655_0 $$aElectronic books.
001445606 77608 $$iPrint version: $$z9783030909277$$z3030909271$$w(OCoLC)1295107710
001445606 830_0 $$aInternational series in operations research & management science ;$$vv. 324.
001445606 852__ $$bebk
001445606 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-90928-4$$zOnline Access$$91397441.1
001445606 909CO $$ooai:library.usi.edu:1445606$$pGLOBAL_SET
001445606 980__ $$aBIB
001445606 980__ $$aEBOOK
001445606 982__ $$aEbook
001445606 983__ $$aOnline
001445606 994__ $$a92$$bISE