Behavioral analysis of aggregation in multicriteria decision aid

By Jean-Luc Marichal. 2000

62 citations recorded by the source corpus.

The most often used operator to aggregate criteria in decision making problems is the classical weighted arithmetic mean. In many problems however, the criteria considered interact, and a substitute to the weighted arithmetic mean has to be adopted. It was shown that, under rather natural conditions, the discrete Choquet integral is an adequate aggregation operator that extends the weighted arithmetic mean by taking into consideration of the interaction among criteria. However, since this operator is constructed from coefficients (weights) whose meaning is not always very clear for the decision maker, it is useful to define from these coefficients some indices that offer a better understanding of the behavioral properties of the aggregation. We present and discuss the following indices: the global importance of criteria, the interaction among criteria, the influence of the criteria on the aggregation, the tolerance of the decision maker, and the dispersion of the weights on the criteria.

Subfields: Artificial Intelligence, Control and Systems Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/9e17cda8c38afd310a81bfc490c8c20f601fa8d1

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In Artificial Intelligence

In Control and Systems Engineering

In Management Science and Operations Research

Citations in the corpus

Aggregation Functions: A Guide for Practitioners (2007, background): “The first studies of this kind of aggregation functions date back to 1980-s [78, 83, 190, 227], but their applications to the solution of different decision problems – such as preference modeling [102, 103] or multicriteria decision making [162]– have renewed the interest in them (see recent publica”
Axiomatic characterizations of probabilistic and cardinal-probabilistic interaction indices (2006, methodology): “In this context, it is used to appraise the overall interaction among criteria [8, 9, 15, 20, 21, 22, 24], thereby giving more insight into the decision problem.”
A review of methods for capacity identification in Choquet integral based multi-attribute utility theory: Applications of the Kappalab R package (2008, unknown): “For a better comprehension of the interaction phenomena modeled by the underlying capacity, several numerical indices can be computed [20, 21].”
Aggregation of interacting criteria by means of the discrete Choquet integral (2002, background): “Other indices, such as tolerance and dispersion, were proposed and studied by the author in [15, 16].”