4. Algorithmic Decision Theory Lectures


Raymond Bisdorff, Emeritus Professor of Applied Mathematics and Computer Science

  1. Bisdorff © 2013-2023

4.1. 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 Algorithmic Decision Theory 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.

4.2. Lectures

L1. General introduction to Algorithmic Decision Theory
  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
  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
  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
  1. Grading students

  2. Rules for aggregating grades

  3. How to aggregate ordinal grades ?

L5. Solving social compromise decision problems with CBA
  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
  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
  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
  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
  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
  1. Ranking with outranking digraphs

  2. Ranking-by-scoring rules

  3. Ranking-by-choosing rules

L11. On ranking by first and last choosing
  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
  1. Pre-ranking a q-tiled performance tableau

  2. On sparse outranking digraphs

  3. HPC-ranking of big performance tableaux