Since the fuzzy set (FS) theory was proposed by Zadeh in 1965, FS has been widely applied in the field of intelligent computing and decision support (ICDS). However, most previous research is based on the membership degree function to solve the core problems in the field of ICDS, such as information representation, distance measure and logical relation. As it is difficult for membership degree to encapsulate people's diversified cognition, FS is unable to deal with complex ICDS problems with multiple cognitive features, thus restricting its further development. As such, Atanassov defined the concept of intuitionistic fuzzy set (IFS) to characterize human cognition according to membership degree and non-membership degree. Yager further developed the concept of q-rung orthopair fuzzy set (q-ROFS) of which the characteristics are the sum of q (q>1) power of membership degree and non-membership degree is bounded by 1. The IFS and q-ROFS effectively improve the representation ability of cognitive information and partly overcome the problems of complex information measurement and aggregation which have been difficult to address in previous fuzzy ICDS methods.

With the rapid development of the Internet Plus, multi-dimensional cognitive information of human beings can be recorded by online platforms, such as comments, likes, forwarding, emoticons, etc., which provides support for people to make complex decisions in the environment of big data. However, the core parameters of IFS and q-ROFS are limited to membership and non-membership degrees, so it is still a great challenge for them to comprehensively summarize this multidimensional heterogeneous cognitive information, thus creating some roadblocks to developments in intelligent computing and decision support based on the IFS and q-ROFS. Therefore, the question of how to combine this multidimensional cognitive information with the existing fuzzy set or how to develop the novel fuzzy set concept to increase its ability of cognitive information representation, intelligent computing and decision support is important.

The aim of this Special Issue is to collate original research and review articles on innovative methods and deep application of multidimensional cognitive information-driven fuzzy intelligent computing and decision support.

Potential topics include but are not limited to the following:

• New definitions of multidimensional cognitive information-driven fuzzy sets
• Cognitive information measurement under novel fuzzy set environment
• Multidimensional cognitive information aggregation
• Fuzzy topological relations in novel fuzzy set
• Advanced methods on fuzzy ICDS
• Fuzzy ICDS for multidimensional data expression
• Fuzzy ICDS for searching group-consistency
• Fuzzy ICDS for classification and prediction
• Fuzzy ICDS for multi-criteria optimization
• Applications of multidimensional cognitive information-driven fuzzy ICDS