Bustince Sola, Humberto
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Bustince Sola
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Humberto
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Estadística, Informática y Matemáticas
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ISC. Institute of Smart Cities
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Publication Open Access Hesitant cognitive uncertain information in aggregation and decision making(University of Sistan and Baluchestan, 2024) Jin, LeSheng; Yager, Ronald R.; Ma, Chao; Langari, Reza; Jana, Chiranjibe; Mesiar, Radko; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta MatematikaThe concepts of cognitive interval information and cognitive uncertain information, which are two recently proposed types of uncertain information, have been extended in this work to the typical hesitant fuzzy environment. We introduce the notions of typical hesitant monopolar cognitive interval information and typical hesitant cognitive uncertain information. To facilitate their analysis, we define uncertainty degree functions and score functions for these concepts using extended aggregation operators. Furthermore, we reanalyze some decision models discussed in earlier literature using these newly proposed concepts to demonstrate their advantages and potential applications.Publication Open Access Unsymmetrical basic uncertain information with some decision-making methods(IOS Press, 2022) Jin, LeSheng; Yager, Ronald R.; Chen, Zhen-Song; Mesiar, Radko; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta MatematikaMotivated by a specific decision-making situation, this work proposes the concept and definition of unsymmetrical basic uncertain information which is a further generalization of basic uncertain information and can model uncertainties in some new decision-making situations. We show that unsymmetrical basic uncertain information in some sense can model linguistic hedges such as 'at least' and 'at most'. Formative weighted arithmetic means and induced aggregations are defined for the proposed concept. Rules-based decision making and semi-copula based integral for this concept with some numerical examples are also presented.Publication Open Access Comprehensive rules-based and preferences induced weights allocation in group decision-making with BUI(Springer, 2022) Li, GePeng; Yager, Ronald R.; Zhang, XinXing; Mesiar, Radko; Jin, LeSheng; Bustince Sola, Humberto; Estadística, Informática y Matemáticas; Estatistika, Informatika eta MatematikaDecision-makers' subjective preferences can be well modeled using preference aggregation operators and related induced weights allocation mechanisms. However, when several different types of preferences occur in some decision environment with more complex uncertainties, repeated uses of preferences induced weights allocation sometimes become unsuitable or less reasonable. In this work, we discuss a common decision environment where several invited experts will offer their respective evaluation values for a certain object. There are three types of preferences which will significantly affect the weights allocations from experts. Instead of unsuitably performing preference induced weights allocation three times independently and then merging the results together using convex combination as some literatures recently did, in this work, we propose some organic and comprehensive rules-based screen method to first rule out some unqualified experts and then take preference induced weights allocation for the refined group of experts. A numerical example in business management and decision-making is presented to show the cognitive reasonability and practical feasibility. © 2022, The Author(s).