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This book showcases a large variety of multiple criteria decision
applications (MCDAs), presenting them in a coherent framework
provided by the methodology chapters and the comments accompanying
each case study. The chapters describing MCDAs invite the reader to
experiment with MCDA methods and perhaps develop new variants using
data from these case studies or other cases they encounter,
equipping them with a broader perception of real-world problems and
how to overcome them with the help of MCDAs.
This book showcases a large variety of multiple criteria decision
applications (MCDAs), presenting them in a coherent framework
provided by the methodology chapters and the comments accompanying
each case study. The chapters describing MCDAs invite the reader to
experiment with MCDA methods and perhaps develop new variants using
data from these case studies or other cases they encounter,
equipping them with a broader perception of real-world problems and
how to overcome them with the help of MCDAs.
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. Â
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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