LAUSR

Modeling uncertainties with multipolar information is an important tool in computational intelligence to address complexities in real‐world circumstances. An m‐polar fuzzy set (mPFS) is the strong model to express multipolarity with m $m$ membership grades (MGs) in the unit closed interval [ 0 , 1 ] $[0,1]$ . A q‐rung orthopair fuzzy set (qROFS) is the strong model to express vague and uncertain information with MGs and nonmembership grades (NMGs). The notion of q‐rung orthopair m‐polar fuzzy set is a new hybrid extension of both mPFS and qROFS. An ROmPFS is a generalized concept that has the ability to deal with multipolarity with m $m$ ordered pairs of MGs and NMGs. Motivated by these robust concepts, in this article, various aggregation operators (AOs) for the aggregation of q‐rung orthopair m‐polar fuzzy numbers are proposed, including q‐rung orthopair m‐polar fuzzy weighted averaging operator, symmetric q‐rung orthopair m‐polar fuzzy weighted averaging operator, q‐rung orthopair m‐polar fuzzy weighted geometric operator, symmetric q‐rung orthopair m‐polar fuzzy weighted geometric operator, and q $q$ ‐rung orthopair m‐polar fuzzy Maclaurin symmetric mean operator. On the basis of proposed AOs, a robust multicriteria decision‐making approach is proposed. An application of proposed AOs is presented to address economic crises during COVID‐19. Furthermore, the comparison analysis is designed to discuss the validity and rationality of proposed AOs.