The 16th International Conference on Modeling Decisions for Artificial Intelligence Milan, Italy September 4 - 6, 2019 http://www.mdai.cat/mdai2019 |
Submission deadline:
DEADLINE: USB Proc. June 22nd, 2019 |
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.
Abstract:
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 alternativesâ€™ 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.
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