Triangular Neutrosophic Cognitive Map for Multistage Sequential Decision-Making Problems

Nowadays, fuzzy cognitive maps (FCMs) are one of the most efficient artificial intelligence techniques for modeling large and complex systems. However, traditional FCMs have the limitation of not representing the indeterminacy situations presented in many decision-making problems. To overcome this limitation, neutrosophic cognitive maps (NCMs) were proposed as a new extension of traditional FCMs. Nevertheless, the way that NCMs reported in the bibliography handle the indeterminacy is still insufficient since they cannot quantify the degree of indeterminacy. Moreover, there are decision-making problems in which decisions should be considered as a sequence of decisions hardly interconnected in sequential order. This situation is presented in scenarios such as projects evaluation characterized by the existence of multiple interconnected processes (diagnosis, decision, and prediction). The lack of a suitable FCMs topology for modeling this kind of decision-making problems constitutes another challenging issue of FCMs. This paper presents a new neutrosophic cognitive map based on triangular neutrosophic numbers for multistage sequential decision-making problems (MS-TrNCM). In the proposed model, all the map's connections are represented by triangular neutrosophic numbers, making it possible for decision makers to express their preferences considering the truth, indeterminacy, and falsity degrees. Furthermore, a new topology for representing multistage sequential processes is introduced. The suggested MS-TrNCM is applied to make diagnoses, decisions, and predictions during the evaluation of 1011 projects records from project evaluation database uci-gp-eval-201903051137 provided by the University of Informatics Sciences. In validation process, the superiority of the proposed MS-TrNCM over the NCMs and traditional FCMs has been demonstrated.

Automated summary

Fuzzy cognitive maps are widely used in modeling large and complex systems, but they have limitations in representing uncertainties. Neutrosophic cognitive maps are proposed as an extension to address this issue, but they still lack the ability to quantify uncertainty. In addition, some decision-making problems involve interrelated decisions that are difficult to prioritize in sequential order.