Algorithmic Decision Making With Python Resources : From Multicriteria Perfor...

Description: Algorithmic Decision Making With Python Resources : From Multicriteria Performance Records to Decision Algorithms Via Bipolar-valued Outranking Digraphs, Paperback by Bisdorff, Raymond, ISBN 3030909301, ISBN-13 9783030909307, Brand New, Free shipping in the US 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 th 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. Th’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. Th 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.  

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Book Title: Algorithmic Decision Making with Python Resources : From Multicriteria Performance Records to Decision Algorithms Via Bipolar-Valued Outranking Digraphs

Number of Pages: Xxxvii, 346 Pages

Language: English

Publisher: Springer International Publishing A&G

Topic: Operations Research, Economics / General, Applied, Discrete Mathematics

Publication Year: 2023

Illustrator: Yes

Genre: Mathematics, Business & Economics

Item Weight: 20.7 Oz

Author: Raymond Bisdorff

Item Length: 9.3 in

Book Series: International Series in Operations Research and Management Science Ser.

Item Width: 6.1 in

Format: Trade Paperback