01305nam 2200409 450 991046692980332120200407165404.090-272-6344-2(CKB)4100000007008726(MiAaPQ)EBC5549061(EXLCZ)99410000000700872620181114d2018 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierCognitive rhetoric the cognitive poetics of political discourse /Sam BrowseAmsterdam ;Philadelphia :John Benjamins Publishing Company,2018.1 online resource (249 pages)Linguistic Approaches to Literature ;Volume 3190-272-0154-4 Linguistic approaches to literature ;Volume 31.1569-3112Discourse analysisPolitical aspectsRhetoricPolitical aspectsElectronic books.Discourse analysisPolitical aspects.RhetoricPolitical aspects.401.41Browse Sam860010MiAaPQMiAaPQMiAaPQBOOK9910466929803321Cognitive rhetoric1919009UNINA05904nam 22006615 450 991055849670332120251204105528.09783030909284(electronic bk.)978303090927710.1007/978-3-030-90928-4(MiAaPQ)EBC6942562(Au-PeEL)EBL6942562(CKB)21441195500041(PPN)261518380(BIP)83681689(BIP)81838784(DE-He213)978-3-030-90928-4(EXLCZ)992144119550004120220329d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAlgorithmic Decision Making with Python Resources From Multicriteria Performance Records to Decision Algorithms via Bipolar-Valued Outranking Digraphs /by Raymond Bisdorff1st ed. 2022.Cham :Springer International Publishing :Imprint: Springer,2022.1 online resource (366 pages)International Series in Operations Research & Management Science,2214-7934 ;324Print version: Bisdorff, Raymond Algorithmic Decision Making with Python Resources Cham : Springer International Publishing AG,c2022 9783030909277 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 threereal-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.International Series in Operations Research & Management Science,2214-7934 ;324Operations researchGraph theoryComputer scienceMathematicsMathematical statisticsData processingOperations Research and Decision TheoryGraph TheoryMathematical Applications in Computer ScienceStatistics and ComputingOperations research.Graph theory.Computer scienceMathematics.Mathematical statisticsData processing.Operations Research and Decision Theory.Graph Theory.Mathematical Applications in Computer Science.Statistics and Computing.005.133658.4033Bisdorff Raymond1220512MiAaPQMiAaPQMiAaPQ9910558496703321Algorithmic Decision Making with Python Resources2824666UNINA