The robustness concern in preference disaggregation approaches for decision aiding: An Overview

Abstract

In multiple criteria decision aid, preference disaggregation techniques are used to facilitate the construction of decision models, through regression-based approaches that enable the elicitation of preferential information from a representative set of decision examples provided by a decision-maker. The robustness of such approaches and their results is an important feature for their successful implementation in practice. In this chapter we discuss the robustness concern in this context, overview the main methodologies that have been recently developed to obtain robust recommendations from disaggregation techniques, and analyze the connections with statistical learning theory, which is also involved with inferring models from data.