Documentation of the Digraph3 resources

Author:Raymond Bisdorff, Emeritus Professor, University of Luxembourg FSTC - CSC/ILIAS
Version:Revision: Python 3.6
Copyright:R. Bisdorff 2013-2018


This documentation, also available on the Read The Docs site:, describes the Python3 resources for implementing decision aid algorithms in the context of a bipolarly-valued outranking approach ([1], [2]). These computing resources are useful in the field of Algorithmic Decision Theory ( and more specifically in outranking based Multiple Criteria Decision Aid (MCDA).


Parts of the documentation:

The documentation contains, first, a set of tutorials introducing the main objects like digraphs, outranking digraphs and performance tableaux. There is also a tutorial provided on undirected graphs. Some tutorials are problem oriented and show how to compute the winner of an election, how to build a best choice recommendation, or how to linearly rank with multiple incommensurable ranking criteria.

The second part concerns the reference manual of the proposed Python3 modules, classes and methods. The main generic root classes in this collection are the digraphs.Digraph class, the perfTabs.PerformanceTableau class and the outrankingDigraphs.OutrankingDigraph class. The technical documentation also provides links to the complete source code of all modules, classes and methods.


  1. Bisdorff, L.C. Dias, P. Meyer, V. Mousseau and M. Pirlot (Eds.) (2015). Evaluation and decision models with multiple criteria: Case studies. Springer-Verlag Berlin Heidelberg, International Handbooks on Information Systems, ISBN 978-3-662-46815-9, 643 pages (downloadable content extract PDF file 401.4 kB).
  1. Bisdorff (2013) “On Polarizing Outranking Relations with Large Performance Differences” Journal of Multi-Criteria Decision Analysis (Wiley) 20:3-12 (Preprint PDF file 403.5kB)

For further scientific documentation of the Digraph3 resources, see