Algorithmic Decision Making with Python Resources

Name: Algorithmic Decision Making with Python Resources
Brand: Springer, Berlin
SKU: 9783030909307
Price: 331.49 PLN
Availability: InStock

From Multicriteria Performance Records to Decision Algorithms via Bipolar-Valued Outranking Digraphs

Autor: Raymond Bisdorff

Język:

Oprawa: Miękka

Wydawca: Springer, Berlin

Dostępność: Dostępna u dostawcy

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This book describes Python3 programming resources for implementing decision aiding algorithms in the...

Informacje o książce

Autor

Raymond Bisdorff

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2023

strony

346

EAN

9783030909307

Enbook ID

43083462

Wydawca

Springer, Berlin

Waga

592

Wymiary

155 x 235

Pełny opis

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.

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Algorithmic Decision Making with Python Resources

From Multicriteria Performance Records to Decision Algorithms via Bipolar-Valued Outranking Digraphs

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