Reference : Algorithmic Decision Making with Python Resources: From multicriteria performance rec...
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Business & economic sciences : Quantitative methods in economics & management
Algorithmic Decision Making with Python Resources: From multicriteria performance records to decision algorithms via bipolar-valued outranking digraphs
Bisdorff, Raymond mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
In press
International Series in Operations Research & Management Science ISOR 324
xxxv, 342
[en] Algorithmic Decision Theory ; Outranking approach ; multiple-criteria performance records ; Multicriteria decision aiding ; best choice recommendations ; ranking with incommensurable criteria ; relative and absolute quantile ratings
[en] 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. The second part illustrates in eight methodological chapters multiple-criteria evaluation models and decision algorithms. These chapters are mostly problem oriented and show how 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 rank or rate with incommensurable criteria. The book's third part presents three real 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 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. Chapters presenting algorithms for ranking multicriteria performance records will be of computational interest for designers of web recommender systems. Similarly, the relative and absolute quantiles-rating algorithms, discussed and illustrated in several chapters, will be of practical interest for public or private performance auditors.
Researchers ; Professionals ; Students

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