Algorithmic Decision Making with Python Resources

Raymond Bisdorff ()

in International Series in Operations Research and Management Science from Springer, currently edited by Camille C. Price, Joe Zhu and Frederick S. Hillier

Date: 2022
ISBN: 978-3-030-90928-4
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Chapters in this book:

Ch Chapter 1 Working with the Digraph3 Python Resources

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Ch Chapter 10 Rating-by-Ranking with Learned Performance Quantile Norms

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Ch Chapter 11 HPC Ranking of Big Performance Tableaux

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Ch Chapter 12 Alice’s Best Choice: A Selection Case Study

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Ch Chapter 13 The Best Academic Computer Science Depts: A Ranking Case Study

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Ch Chapter 14 The Best Students, Where Do They Study? A Rating Case Study

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Ch Chapter 15 Exercises

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Ch Chapter 16 On Measuring the Fitness of a Multiple-Criteria Ranking

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Ch Chapter 17 On Computing Digraph Kernels

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Ch Chapter 18 On Confident Outrankings with Uncertain Criteria Significance Weights

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Ch Chapter 19 Robustness Analysis of Outranking Digraphs

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Ch Chapter 2 Working with Bipolar-Valued Digraphs

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Ch Chapter 20 Tempering Plurality Tyranny Effects in Social Choice

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Ch Chapter 21 Bipolar-Valued Undirected Graphs

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Ch Chapter 22 On Tree Graphs and Graph Forests

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Ch Chapter 23 About Split, Comparability, Interval, and Permutation Graphs

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Ch Chapter 3 Working with Outranking Digraphs

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Ch Chapter 4 Building a Best Choice Recommendation

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Ch Chapter 5 How to Create a New Multiple-Criteria Performance Tableau

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Ch Chapter 6 Generating Random Performance Tableaux

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Ch Chapter 7 Who Wins the Election?

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Ch Chapter 8 Ranking with Multiple Incommensurable Criteria

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Ch Chapter 9 Rating by Sorting into Relative Performance Quantiles

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DOI: 10.1007/978-3-030-90928-4

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