4. Algorithmic Decision Theory Lectures
Raymond Bisdorff, Emeritus Professor of Applied Mathematics and Computer Science
Bisdorff © 2013-2022
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.
Historical notes and acknowledgements 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.
On majority tyranny in uni-nominal elections and other difficulties with simple voting rules. How to aggregate voter’s preferences? Voting and complexity issues.
On ranking from different opinions. A typology of ranking rules.
Grading students. Rules for aggregating grades. How to aggregate ordinal grades?
Critical perspective on the Cost-Benefit Analysis (CBA) decision approach, its principles and applications in public transport problems.
Measuring the performances of potential decision alternatives. Comparing Costs and Benefits. Theoretical foundations of MAVT and critical perspective.
Comparing alternatives with potentially conflicting criteria. Theoretical foundation of the outranking approach. The Rubis best-choice recommender system.
Random performance generators. Random performance tableaux. Special random tableaux: random Cost-Benefit, random 3-Objectives or random academic performance tableaux.
How to rate with multiple incommensurable criteria? On rating-by-sorting with relative quantiles. Absolute rating-by-ranking with learned quantile norms.
Ranking with outranking digraphs. Ranking-by-scoring and ranking-by-choosing rules.
Useful properties of the Rubis best-choice procedure. A bipolar ranking-by-choosing algorithm.
Pre-ranking a q-tiled performance tableau. On sparse outranking digraphs. HPC-ranking a big performance tableau.