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Algorithmic Decision Making with Python Resources - From Multicriteria Performance Records to Decision Algorithms via Bipolar-Valued Outranking Digraphs (Hardcover, 1st ed. 2022)
Loot Price: R3,072
Discovery Miles 30 720
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Algorithmic Decision Making with Python Resources - From Multicriteria Performance Records to Decision Algorithms via Bipolar-Valued Outranking Digraphs (Hardcover, 1st ed. 2022)
Series: International Series in Operations Research & Management Science, 324
Expected to ship within 12 - 17 working days
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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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