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Master in Information and Computer Sciences

Algorithmic Decision Theory
Module:Module 2.1, Semester 2
Objective: The objective of this course is to introduce students to ADT, a new interdisciplinary field at the intersection of decision theory, discrete mathematics, theoretical computer science and artificial intelligence. ADT proposes new ideas, approaches and tools for supporting decision making processes in presence of massive databases, combinatorial structures, partial and/or uncertain information, and distributed, possibly inter-operating, decision makers. Such problems arise in several real-world decision making problems such as humanitarian logistics, epidemiology, risk assessment and management, e-government, electronic commerce, and the implementation of recommender systems
Course learning outcomes: * Recognise and formulate problems that relate to Algorithmic Decision Theory (ADT)
* Identify the operational complexity issues arising in ADT
* Adapt some of the classical Operational Research and Decision Aid solving strategies to the ADT context
* Implement an ADT solver for selected case studies

Description: Varying with the main focus and the interest of the students, the content of the lectures may concern:
1) General introduction to algorithmic decision theory
2) Who wins the election ? An introduction to social choice theory
3) How to rank the candidates ? Main ranking rules
4) Building and aggregating performance measures
5) The American way: Multiple attributes value theory MAVT
6) The European way: Multiple criteria based decision aid MCDA
7) Best choice recommendation algorithms
8) Sorting and clustering algorithms
9) Inverse Decision Analysis
Organization:The course is organized as a series of theoretical lectures with hands-on exercises intermixed
Language: English
Lecturer: BISDORFF Raymond Joseph
Rating: The students may choose either to elaborate a project: 100% or to write a final exam: 100%
Note:The following textbooks will be used in this course:
- Evaluation and decision models: A critical perspective, D. Bouyssou, Th. Marchant et al. Kluwer 2000
- Aiding decisions with multiple criteria, D. Bouyssou, E. Jacquet-Lagrèze et al., Kluwer 2002
- Evaluation and decision models with multiple criteria: Stepping stones for the analyst, D. Bouyssou, Th. Marchant et al. Springer 2006