Cautious OWA and evidential reasoning for decision making under uncertainty

To make a decision under certainty, multicriteria decision methods aims to choose, rank or sort alternatives on the basis of quantitative or qualitative criteria and preferences expressed by the decision-makers. However, decision is often done under uncertainty: choosing alternatives can have different consequences depending on the external context (or state of the word). In this paper, a new methodology called Cautious Ordered Weighted Averaging with Evidential Reasoning (COWA-ER) is proposed for decision making under uncertainty to take into account imperfect evaluations of the alternatives and unknown beliefs about groups of the possible states of the world (scenarii). COWA-ER mixes cautiously the principle of Yager´s Ordered Weighted Averaging (OWA) approach with the efficient fusion of belief functions proposed in Dezert-Smarandache Theory (DSmT).