.. meta::
   :description: Documentation of the Digraph3 collection of python3 modules for algorithmic decision theory
   :keywords: Algorithmic Decision Theory, Outranking Digraphs, MIS and kernels, Multiple Criteria Decision Aid, Bipolar-valued Epistemic Logic

Algorithmic Decision Theory Lectures
====================================
:Author: Raymond Bisdorff, Emeritus Professor of Applied Mathematics and Computer Science, University of Luxembourg
:Copyright: R. Bisdorff |location_link3| 2013-2023

Introduction
------------

From 2007 to 2011 the Algorithmic Decision Theory COST Action IC0602, coordinated by Alexis Tsoukiàs, gathered researchers coming from different fields such as *Decision Theory*, *Discrete Mathematics*, *Theoretical Computer Science* and *Artificial Intelligence* in order to improve decision support in the presence of **massive data bases**, **combinatorial structures**, **partial** and/or **uncertain information** and **distributed**, possibly **interoperating decision makers**.

A positive result a.o. of this COST action was the organisation from 2012 to 2020 of a Semester Course on |location_link4| at the *University of Luxembourg* in the context of its **Master in Information and Computer Science**.

Below are gathered 2x2 reduced copies of the presentation slides for 12 Lectures from the Summer Semester 2020.

Lectures
--------

L1. `General introduction to Algorithmic Decision Theory <_static/1-adtIntro-2x2.pdf>`_
    1. Historical notes and acknowledgements

    2. Generic conceptual framework for studying decision aiding *processes* 

    3. Selecting, ranking, rating and clustering problems

L2. `Who wins the election ? Choosing from multiple opinions <_static/2-voting-2x2.pdf>`_ 
    1. On plurality tyranny in uni-nominal elections and other difficulties with simple voting rules

    2. How to aggregate voter's preferences ?

    3. Voting and complexity issues

L3. `On social consensus rankings <_static/3-rankingRules-2x2.pdf>`_
    1. On ranking from different opinions

    2. A typology of ranking rules

    3. Classification of ranking rules

L4. `Evaluation models for measuring and aggregating performances <_static/4-grading-2x2.pdf>`_
    1. Grading students

    2. Rules for aggregating grades

    3. How to aggregate *ordinal* grades ?

L5. `Solving social compromise decision problems with CBA <_static/5-CBA-2x2.pdf>`_
    1. What is Cost-Benefit Analysis (CBA) ?

    2. Principles and critical perspective

    3. Applications in public transport problems

L6. `Choosing with multiple commensurable criteria: the Multiple Attribute Value Theory <_static/6-mavt-2x2.pdf>`_
    1. Measuring the performances of potential decision alternatives

    2. Agregating Costs and Benefits

    3. Theoretical foundations and critical perspective

L7. `Choosing with multiple non-commensurable criteria: The Rubis outranking approach <_static/7a-outranking-2x2.pdf>`_
    1. Comparing alternatives with potentially conflicting criteria

    2. Theoretical foundation of the outranking approach

    3. The Rubis best-choice recommender system

L8. `Generating random outranking digraphs <_static/8-randPerfTabs-2x2.pdf>`_
    1. Random performance generators

    2. Random standard performance tableau

    3. Special models: Cost-Benefit, 3-Objectives or academic performance tableaux

L9. `On rating with multiple performance criteria <_static/9-quantilesSortingRating-2x2.pdf>`_
    1. How to rate with multiple incommensurable criteria ?

    2. On rating-by-sorting with relative quantiles

    3. Absolute rating-by-ranking with learned quantile norms

L10. `On ranking from bipolar-valued pairwise outranking situations <_static/10-rankingOutrankings-2x2.pdf>`_
    1. Ranking with outranking digraphs

    2. Ranking-by-scoring rules

    3. Ranking-by-choosing rules

L11. `On ranking by first and last choosing <_static/11-rankingByChoosing-2x2.pdf>`_
    1. Partial weak ranking by first and last choosing
    
    2. Useful properties of the Rubis best-choice procedure

    3. A bipolar ranking-by-choosing algorithm

L12. `Ranking big multiple incommensurable criteria performance tableaux <_static/12-bigPerfTabRanking-2x2.pdf>`_
    1. Pre-ranking a *q*-tiled performance tableau

    2. On sparse outranking digraphs

    3. HPC-ranking of big performance tableaux
 

.. |location_link1| raw:: html

   <a href="https://rbisdorff.github.io/" target="_blank">https://rbisdorff.github.io/</a>

.. |location_link3| raw:: html

   <a href="_static/digraph3_copyright.html" target="_blank">&copy;</a>

.. |location_link4| raw:: html

   <a href="http://hdl.handle.net/10993/37933" target="_blank">Algorithmic Decision Theory</a>
