Hesitant cognitive uncertain information in aggregation and decision making

Jin, LeSheng; Yager, Ronald R.; Ma, Chao; Langari, Reza; Jana, Chiranjibe; Mesiar, Radko; Bustince, Humberto

doi:10.22111/ijfs.2024.48546.8564

Document Type : Research Paper

Authors

¹ Business School, Nanjing Normal University, Nanjing, China

² Iona Cillege

³ No.296, Longzhong Road, Xiangcheng District

⁴ Department of Mechanical Engineering Texas A&amp;M University College Station, TX 77843-3123 USA

⁵ vidyasagar university

⁶ Faculty of Civil Engineering, Department of Mathematics, Slovak University of Technology (STU), Bratislava, Slovakia

⁷ Department of Statistics, Computer Science and Mathematics, Public University of Navarre

https://doi.org/10.22111/ijfs.2024.48546.8564

Abstract

The 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.

Keywords

Main Subjects

Fuzzy Math

References

[1] K. T. Atanassov, Intuitionistic fuzzy sets, Physica, Heidelberg (1999). https://doi.org/10.1007/
978-3-7908-1870-3_1

[2] B. Bedregal, R. Reiser, H. Bustince, C. Lopez-Molina, V. Torra, Aggregation functions for typical hesitant fuzzy
elements and the action of automorphisms, Information Sciences, 255 (2014), 82-99. https://doi.org/10.1016/
j.ins.2013.08.024

[3] B. Bedregal, R. H. N. Santiago, H. Bustince, D. Paternain, R. Reiser, Typical hesitant fuzzy negations, International
Journal of Intelligent Systems, 29 (2014), 525-543. https://doi.org/10.1002/int.21655

[4] M. Boczek, A. Hovana, O. Hutn´ık, M. Kaluszka, H¨older-Minkowski type inequality for generalized Sugeno integral,
Fuzzy Sets and Systems, 396 (2020), 51-71. https://doi.org/10.1016/j.fss.2020.01.005

[5] M. Boczek, A. Hovana, O. Hutn´ık, M. Kaluszka, New monotone measure-based integrals inspired by scientific impact
problem, European Journal of Operational Research, 290(1) (2021), 346-357. https://doi.org/10.1016/j.ejor.
2020.07.057

[6] T. Calvo, G. Mayor, R. Mesiar, Aggregation operators: New trends and applications, Springer Science and Business
Media, 2002.

[7] J. P. Chang, Z. S. Chen, Z. J. Wang, L. Jin, W. Pedrycz, L. Mart´ınez, M. J. Skibniewski, Assessing spatial synergy
between integrated urban rail transit system and urban form: A BULI-based MCLSGA model with the wisdom of
crowds, IEEE Transactions on Fuzzy Systems, 31(2) (2023), 434-448. https://doi.org/10.1109/TFUZZ.2022.
3185680

[8] Z. S. Chen, X. Zhang, W. Pedrycz, X. J. Wang, M. J. Skibniewski, Bid evaluation in civil construction under
uncertainty: A two-stage LSP-ELECTRE III-based approach, Engineering Applications of Artificial Intelligence, 94
(2020), 103835. https://doi.org/10.1016/j.engappai.2020.103835

[9] Z. S. Chen, X. Zhang, R. M. Rodr´ıguez, W. Pedrycz, L. Mart´ınez, Expertise-based bid evaluation for constructioncontractor selection with generalized comparative linguistic ELECTRE III, Automation in Construction, 125 (2021), 103578. https://doi.org/10.1016/j.autcon.2021.103578

[10] Z. S. Chen, X. Zhang, R. M. Rodr´ıguez, W. Pedrycz, L. Mart´ınez, M. J. Skibniewski, Expertise-structure and riskappetite-integrated two-tiered collective opinion generation framework for large-scale group decision making, IEEE
Transactions on Fuzzy Systems, 30(12) (2022), 5496-5510. https://doi.org/10.1109/TFUZZ.2022.3179594

[11] Z. S. Chen, M. D. Zhou, K. S. Chin, A. Darko, X. J. Wang, W. Pedrycz, Optimized decision support for BIM
maturity assessment, Automation in Construction, 149 (2023), 104808. https://doi.org/10.1016/j.autcon.
2023.104808

[12] W. L. Gau, D. J. Buehrer, Vague sets, IEEE Transactions on Systems, Man, and Cybernetics, 23(2) (1993),
610-614. https://doi.org/10.1109/21.229476

[13] M. Grabisch, J. L. Marichal, R. Mesiar, E. Pap, Aggregation functions, Cambridge University Press, 2009.

[14] I. Grattan-Guinness, Fuzzy membership mapped onto intervals and many-valued quantities, Mathematical Logic
Quarterly, 22 (1976), 149-160. https://doi.org/10.1002/malq.19760220120

[15] L. Jin, Z. S. Chen, R. R. Yager, R. Langari, Cognitive uncertain information with some properties and characteristics,
Journal of Intelligent and Fuzzy Systems, 44(6) (2023), 9411-9418. https://doi.org/10.3233/JIFS-223119

[16] L. Jin, Z. S. Chen, R. R. Yager, R. Langari, Interval type interval and cognitive uncertain information in information
fusion and decision making, International Journal of Computational Intelligence Systems, 16 (2023), 60. https:
//doi.org/10.1007/s44196-023-00227-4

[17] L. Jin, A. Koles´arov´a, R. Mesiar, Semicopula based integrals, Fuzzy Sets and Systems, 412 (2021), 106-119.
https://doi.org/10.1016/j.fss.2021.01.004

[18] L. Jin, R. Mesiar, S. Borkotokey, M. Kalina, Certainty aggregation and the certainty fuzzy measures, International
Journal of Intelligent Systems, 33 (2018), 759-770. https://doi.org/10.1002/int.21961

[19] L. Jin, R. Mesiar, R. Yager, Ordered weighted averaging aggregation on convex poset, IEEE Transactions on Fuzzy
Systems, 27(3) (2019), 612-617. https://doi.org/10.1109/TFUZZ.2019.2893371

[20] L. Jin, R. Mesiar, R. Yager, Parameterized preference aggregation operators with improved adjustability, International
Journal of General Systems, 49(8) (2020), 843-855. https://doi.org/10.1080/03081079.2020.1786822

[21] L. S. Jin, Y. Q. Xu, Z. S. Chen, R. Mesiar, R. R. Yager, Relative basic uncertain information in preference and
uncertain involved information fusion, International Journal of Computational Intelligence Systems, 15 (2022), 12.
https://doi.org/10.1007/s44196-022-00066-9

[22] L. Jin, R. R. Yager, Z. S. Chen, J. ˘Spirkov´a, R. Mesiar, Interval and BUI type basic uncertain information in
multi-sources evaluation and rules based decision making, International Journal of General Systems, 52(4) (2023),
443-454. https://doi.org/10.1080/03081079.2022.2142217

[23] G. Li, R. R. Yager, X. Zhang, R. Mesiar, H. Bustince, L. Jin, Comprehensive rules-based and preferences induced
weights allocation in group decision-making with BUI, International Journal of Computational Intelligence Systems,
15 (2022), 54. https://doi.org/10.1007/s44196-022-00116-2

[24] R. Mesiar, S. Borkotokey, L. Jin, M. Kalina, Aggregation under uncertainty, IEEE Transactions on Fuzzy Systems,
26(4) (2018), 2475-2478. https://doi.org/10.1109/TFUZZ.2017.2756828

[25] R. Mesiar, A. Koles´arov´a, S. Borkotokey, L. Jin, M¨obius product-based constructions of aggregation functions, Fuzzy
Sets and Systems, 448 (2022), 17-34. https://doi.org/10.1016/j.fss.2022.01.002

[26] G. Qian, E. Zhang, Z. Chen, R. Mesiar, R. R. Yager, L. Jin, Consistent construction of evaluation threshold
values and rules for heterogeneous linguistic input information, International Journal of Computational Intelligence
Systems, 14 (2021), 153. https://doi.org/10.1007/s44196-021-00003-2

[27] V. Torra, Hesitant fuzzy sets, International Journal of Intelligent Systems, 25(6) (2010), 529-539. https://doi.
org/10.1002/int.20418

[28] R. R. Yager, On ordered weighted averaging aggregation operators in multicriteria decisionmaking, IEEE Transactions
on Systems, Man, and Cybernetics, 18(1) (1988), 183-190. https://doi.org/10.1109/21.87068

[29] R. R. Yager, Quantifier guided aggregation using OWA operators, International Journal of Intelligent Systems, 11
(1996), 49-73. https://doi.org/10.1002/(SICI)1098-111X(199601)11:1<49::AID-INT3>3.0.CO;2-Z

[30] R. R. Yager, Induced aggregation operators, Fuzzy Sets and Systems, 137(1) (2003), 59-69. https://doi.org/10.
1016/S0165-0114(02)00432-3

[31] L. A. Zadeh, A note on Z-numbers, Information Sciences, 181(14) (2011), 2923-2932. https://doi.org/10.1016/
j.ins.2011.02.022

Iranian Journal of Fuzzy Systems

Hesitant cognitive uncertain information in aggregation and decision making

References

References

Volume 21, Issue 5
September and October 2024
Pages 1-9

Hesitant cognitive uncertain information in aggregation and decision making

References

References

Volume 21, Issue 5September and October 2024Pages 1-9

Volume 21, Issue 5
September and October 2024
Pages 1-9