Incremental Elicitation of Rank-Dependent Aggregation
Functions based on Bayesian Linear Regression
Abstract
We introduce a new model-based incremental
choice procedure for multicriteria decision support,
that interleaves the analysis of the set of alternatives and the elicitation of weighting coefficients
that specify the role of criteria in rank-dependent
models such as ordered weighted averages (OWA)
and Choquet integrals. Starting from a prior distribution on the set of weighting parameters, we propose an adaptive elicitation approach based on the
minimization of the expected regret to iteratively
generate preference queries. The answers of the
Decision Maker are used to revise the current distribution until a solution can be recommended with
sufficient confidence. We present numerical tests
showing the interest of the proposed approach