A Prospect Theory-Based Evidential Reasoning Approach for Multi-expert Multi-criteria Decision-Making with Uncertainty Considering the Psychological Cognition of Experts

To model and deal with complex decision-making problems, many methods have been proposed in the past decades. However, how to deal with probabilistic uncertainty is still an open question. In this regard, this paper introduces a prospect theory-based evidential reasoning (ER) approach to process such uncertainty in multi-expert multi-criteria decision-making. Firstly, we introduce a novel way to model the uncertainty by assigning the uncertain belief to the envelope of evidence, which makes the expression consistent with experts' perceptions. The ER approach is used to aggregate individual decision-making matrices with probabilistic uncertainty to obtain a collective matrix. Then, the ER approach is applied again on the obtained collective matrix to aggregate the evaluations under different criteria to generate the comprehensive evaluation of each alternative. Given that the decision-maker may have different risk preferences to gain and loss from those of the experts, we introduce a prospect function of evaluation grades to rank alternatives. In this way, we can obtain the ranking of alternatives which considers both the evaluations of experts and the risk preference of the decision-maker. The advantages of this method are highlighted by a case study concerning the evaluation of sustainable development ability of enterprises.