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

This book provides Python3 programming resources for implementing decision-aiding algorithms in the context of a bipolar-valued outranking approach. It offers tutorials, illustrations of multiple-criteria evaluation models, and real-world decision case studies. It is primarily intended for graduate students in management sciences, computational statistics, and operations research, as well as designers of web recommender systems and performance auditors.

Format: Hardback
Length: 346 pages
Publication date: 30 March 2022
Publisher: Springer Nature Switzerland AG

This comprehensive book delves into Python3 programming resources for implementing decision-aiding algorithms within the framework of a bipolar-valued outranking approach. The book, aptly named Digraph3, offers a valuable toolkit for practitioners in Algorithmic Decision Theory, particularly in the realm of outranking-based Multiple-Criteria Decision Aiding (MCDA).

The first part of the book serves as an introductory guide, presenting a series of tutorials that introduce the Digraph3 collection of Python3 modules and its key objects, including bipolar-valued digraphs and outranking digraphs. These tutorials provide a solid foundation for understanding the core concepts and functionalities of the library.

The second part of the book takes a more methodological approach, offering eight chapters that illustrate various multiple-criteria evaluation models and decision algorithms. Each chapter is designed with a problem-oriented focus, demonstrating step-by-step techniques for editing a new multiple-criteria performance tableau, building best choice recommendations, computing election winners, and making rankings or ratings using incommensurable criteria.

The third part of the book presents three real-world decision case studies, providing practical examples of how the concepts and techniques discussed earlier can be applied in practical scenarios. These case studies showcase the versatility and effectiveness of the Digraph3 library in addressing complex decision-making problems.

The fourth part of the book delves into 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. These topics offer valuable insights for researchers and practitioners seeking to explore the intricacies of decision-making processes.

The fifth and final part of the book is dedicated to working with undirected graphs, tree graphs, and forests. This section provides valuable insights into the manipulation and analysis of these graph structures, which are commonly encountered in various applications. The closing chapter explores comparability, split, interval, and permutation graphs, further expanding the range of applications that can benefit from the Digraph3 library.

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 particularly appeal to those interested in developing efficient and effective decision-making systems.

In conclusion, this book is a valuable resource for anyone seeking to leverage Python3 programming for implementing decision-aiding algorithms within the context of a bipolar-valued outranking approach. With its comprehensive coverage, practical examples, and advanced topics, it provides a solid foundation for researchers and practitioners in the field of Algorithmic Decision Theory and MCDA.

Weight: 733g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030909277
Edition number: 1st ed. 2022