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The 16th International Conference on
Modeling Decisions for Artificial Intelligence

Milan, Italy September 4 - 6, 2019
Submission deadline:
DEADLINE: USB Proc. June 22nd, 2019


Prof. Jérôme Lang
LAMSADE, University Université Paris-Dauphine
Incomplete Knowledge in Computational Social Choice

Abstract: Social choice theory studies the aggregation of individual preferences towards a collective choice. Computational social choice emerged in the late 1980s, and mostly uses computational paradigms and techniques to provide a better analysis of social choice mechanisms (especially in the fields of voting and of fair division of resources), and to construct new ones. Among the subfields of artificial intelligence that are concerned by this interaction, knowledge representation plays an important role (other subfields being machine learning, reasoning with uncertainty, search, and constraint programming). The reasons for which it plays an important role include: representing preferences and reasoning about them; computing collective decisions with incomplete knowledge of agents' preferences; the role of knowledge in strategic behavior; and using logic for automated theorem proving in social choice.

Prof. Salvatore Greco
Department of Economics and Business, University of Catania.
As simple as possible but not simpler in Multiple Criteria Decision Analysis: the robust stochastic level dependent Choquet integral approach

Abstract: The level dependent Choquet integral has been proposed to take into account multiple criteria decision making problems in which the importance of criteria, the sign and the magnitude of their interactions may depend on the level of the alternatives' evaluations. This integral is based on a level dependent capacity, which is a family of single capacities associated to each level of evaluation for the considered criteria. Since, in general, there is not only one but many level dependent capacities compatible with the preference expressed by the Decision Maker, we propose to take into account all of them by using the Robust Ordinal Regression (ROR) and the Stochastic Multicriteria Acceptability Analysis (SMAA). On one hand, ROR defines a necessary preference relation (if an alternative a is at least as good as an alternative b for all compatible level dependent capacities), and a possible preference relation (if a is at least as good as b for at least one compatible level dependent capacity). On the other hand, considering a random sampling of compatible level dependent capacities, SMAA gives the probability that each alternative reaches a certain position in the ranking of the alternatives as well as the probability that an alternative is preferred to another. A real decision problem related to ranking of universities is provided to illustrate the proposed methodology.
Abstract and references here
Very short cv here

MDAI 2019

MDAI - Modeling Decisions

Vicenç Torra, Last modified: 20 : 20 June 18 2019.