Decision-Making with Multiple Interacting Criteria: An Indirect Elicitation of Preference Parameters Using Evolutionary Algorithms

Abstract

Decision-making problems often require characterization of alternatives through multiple criteria. In contexts where some of these criteria interact, the decision maker (DM) must consider the interaction effects during the aggregation of criteria scores. The well-known ELECTRE (ELimination Et Choix Traduisant la REalité) methods were recently improved to deal with interacting criteria fulfilling several relevant properties, addressing the main types of interaction, and retaining most of the fundamental characteristics of the classical methods. An important criticism to such a family of methods is that defining its parameter values is often difficult and can involve significant challenges and high cognitive effort for the DM; this is exacerbated in the improved version whose parameters are even less intuitive. Here, we describe an evolutionary-based method in which parameter values are inferred by exploiting easy-to-make decisions made or accepted by the DM, thereby reducing his/her cognitive effort. A genetic algorithm is proposed to solve a regression-inspired nonlinear optimization problem. To the best of our knowledge, this is the first paper addressing the indirect elicitation of the ELECTRE model’s parameters with interacting criteria. The proposal is assessed through both in-sample and out-of-sample experiments. Statistical tests indicate robustness of the proposal in terms of the number of criteria and their possible interactions. Results show almost perfect effectiveness to reproduce the DM’s preferences in all situations.