Prof. Jérôme Lang
LAMSADE, University Université Paris-Dauphine
Incomplete Knowledge in Computational Social Choice
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 Aiding:
the robust stochastic level dependent Choquet integral approach
The level dependent Choquet integral has been proposed to take into account 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 information provided 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
information in terms of probability that each alternative reaches a certain position in the ranking
of the alternatives as well as in terms of probability that an alternative is preferred to another.
A real decision problem is provided to illustrate the proposed methodology.
Abstract and references here
Very short cv here