Multicriteria decision making inspired by human cognitive processes

In this study, we present an approach to multi-criteria decision-making modeling inspired by human cognitive processes. The proposed model exploits the ideas of fuzzy sets, balanced fuzzy sets and their connectives, namely t-norms, t-conorms and derived connectives. Balanced connectives are compared and contrasted with unipolar fuzzy connectives. Our objective is to capture complex aspects of decision-making processes. We consider imperfect bipolar information that engages positive and negative aspects and leads to decisions. Furthermore, we investigate behavioral biases. This allows us to provide a detailed insight into decision-making processes realized by human beings and their biases: impact of arguments ordering on the decision, risk aversion, and impact of input arguments fading with time. We investigate and describe the suitability of selected triangular and balanced connectives for decision-making modeling inspired by cognitive processes. We compare different pairs of standard t-norms, t-conorms and balanced counterparts, and discuss their abilities to model various, also irrational, aspects of human behavior. The developed two-step procedure is applied to decision-making modeling presented as a case study and compared with a method known in the literature.