The concept of Pythagorean fuzzy sets (PFSs) was initially developed by Yager in 2013, which provides a novel way to model\nuncertainty and vaguenesswith high precision and accuracy compared to intuitionistic fuzzy sets (IFSs).The conceptwas concretely\ndesigned to represent uncertainty and vagueness in mathematical way and to furnish a formalized tool for tackling imprecision to\nreal problems. In the present paper, we have used both probabilistic and nonprobabilistic types to calculate fuzzy entropy of PFSs.\nFirstly, a probabilistic-type entropy measure for PFSs is proposed and then axiomatic definitions and properties are established.\nSecondly, we utilize a nonprobabilistic-type with distances to construct newentropymeasures for PFSs. Then a minâ??max operation\nto calculate entropy measures for PFSs is suggested. Some examples are also used to demonstrate suitability and reliability of the\nproposed methods, especially for choosing the best one/ones in structured linguistic variables. Furthermore, a newmethod based on\nthe chosen entropies is presented for Pythagorean fuzzy multicriterion decision making to compute criteria weights with ranking\nof alternatives. A comparison analysis with the most recent and relevant Pythagorean fuzzy entropy is conducted to reveal the\nadvantages of our developed methods. Finally, this method is applied for ranking China-Pakistan Economic Corridor (CPEC)\nprojects.These examples with applications demonstrate practical effectiveness of the proposed entropy measures.
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