**8. Conclusions**

Traditional aggregation operators, which usually deal with uni-polar information, fail to aggregate m-polar fuzzy soft information taking their values from [0, <sup>1</sup>]*<sup>m</sup>*. This study proposes a new aggregation method for processing the MAGDM problems with m-polar fuzzy soft information in which both attributes and experts have different weights. For this purpose, firstly the concept of m-polar fuzzy soft sets is introduced. Then, the new aggregation operator M-pFSMWM in the domain of m-polar fuzzy soft sets is defined. The advantage of proposed M-pFSMWM operator is to be sensitive for different partial agreemen<sup>t</sup> scenarios at a consensus degree *α*. Further, the m-polar fuzzy soft induced ordered weighted average (M-pFSIOWA) operator and the m-polar fuzzy soft induced ordered weighted geometric (M-pFSIOWG) operator, which are the extensions of IOWA and IOWG operators, respectively, are developed. Some desirable properties of M-pFSMWM, M-pFSIOWA, and M-pFSIOWG operators, such as idempotency, monotonicity, and commutativity are also studied. The characteristics of the proposed M-pFSMWM operator shows it is more adaptable for a wider range of MAGDM problems in comparison with M-pFSIOWA and M-pFSIOWG operators. In addition, a procedure for ranking m-polar fuzzy soft data based on a new score value function is proposed. Then, two algorithms are designed to MAGDM problems based on M-pFSMWM, M-pFSIOWA, and M-pFSIOWG operators. Finally, to show the efficiency of proposed methods, some numerical examples are discussed.

**Author Contributions:** Conceptualization, A.Z.K.; Investigation, A.Z.K.; Methodology, A.Z.K. and A.K.; Validation, A.Z.K. and A.K.; Writing—original draft, A.Z.K.; and Writing—review and editing, A.Z.K. and A.K.

**Acknowledgments:** This research was supported by the Ministry of Education Malaysia (KPM) under Grant FRGS/1/2018/STG06/UPM/01/3.

**Conflicts of Interest:** The authors declare no conflict of interest.
