4. Algorithmic Decision Theory Lectures

Author

Raymond Bisdorff, Emeritus Professor of Applied Mathematics and Computer Science

Copyright
  1. Bisdorff © 2013-2021

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

  1. General introduction to Algorithmic Decision Theory

    Historical notes and aknowledgments followed by the presentation of a generic conceptual framework for studying decision aiding processes: formulating a decision problem, choosing the evaluation models and building decision recommendations.

  2. Who wins the election ? Choosing from multiple opinions

    On majority tyranny in uninominal elections and other difficulties with simple voting rules. How to aggregate voter’s preferences? Voting and complexity issues.

  3. On social consensus rankings

    On ranking from different opinions. A typology of ranking rules.

  4. Evaluation models for measuring and aggregating performances

    Grading students. Rules for aggregating grades. How to aggregate ordinal grades?

  5. Solving social compromise decision problems with CBA

    Critical perspective on the Cost-Benefit Analysis (CBA) decision approach, its principles and applications in public transport problems.

  6. Choosing with multiple commensurable criteria: the Multiple Attribute Value Theory

    Measuring the performances of potential decision alternatives. Comparing Costs and Benefits. Theoretical foundations of MAVT and critical perspective.

  7. Best multiple criteria compromise choice: The Rubis outranking approach

    Comparing alternatives with potentially conflicting criteria. Theoretical foundation of the outranking approach. The Rubis best-choice recommender system.

  8. Generating random outranking digraphs

    Random performance generators. Random performance tableaux. Special random tableaux: random Cost-Benefit, random 3-Objectives or random academic performance tableaux.

  9. On rating with multiple criteria

    How to rate with multiple incommensurable criteria? On rating-by-sorting with relative quantiles. Absolute rating-by-ranking with learned quantile norms.

  10. On ranking from bipolar-valued pairwise outranking situations

    Ranking with outranking digraphs. Ranking-by-scoring and ranking-by-choosing rules.

  11. On ranking by first and last choosing

    Useful properties of the Rubis best-choice procedure. A bipolar ranking-by-choosing algorithm.

  12. Ranking big multiple incommensurable criteria performance tableaux

    Pre-ranking a q-tiled performance tableau. On sparse outranking digraphs. HPC-ranking a big performance tableau.