Practical extension of MACBETH methodology to deal with sophisticated preferences’ models
Résumé
When solving complex and critical decision prob-
lems involving several aspects, collecting decision-maker (DM)
preferences remains a delicate issue. Indirect parameter iden-
tification procedures are more adequate in such situations
than procedures consisting to ask directly the DM to provide
preferences. In the case of methods based on additive models
such as MACBETH, disaggregation procedures are implemented
to handle this problem. In case of sophisticated non-additive
model, e.g. Choquet integral based fuzzy measure, most of the
existing indirect procedures assume a 2-additive fuzzy measure
and the DM is asked to change her/his preferences if they do
not match this model. This paper proposes an approach to
determine preferences model that achieves a trade-off between
model sophistication and the fastidious MACBETH questioning
procedure when considering interactions between more than two
attributes. First, it proposes a practical approach to solve a
real case of multiple criteria decision problem while considering
sophistical model of preferences. On one hand, particular binary
alternatives are selected for a first questioning procedure to de-
termine the fuzzy measure parameters. In order to make the later
procedure as simple as possible to handle, some simplifications
are introduced to reduce both the amount of information and
the cognitive effort requested from the DM. Our proposition
consists in fixing several non-linear optimization problems until
reaching the first k for which the k-additive fuzzy measure match
the DM preferences. On the other hand, starting from a decision
matrix and the answers to a second questioning procedure, value
functions are determined. The second contribution is a theoretical
one and consists in proving the existence of a k-additive fuzzy
measure matching the DM preferences. Finally, the approach is
applied to a real world case that concerns the comparison of some
electrical and thermal vehicles regarding their environmental
impacts.
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