0599856 20250320143118.120241025235959.9 85206645259 001338864500001 10.1016/j.fss.2024.109153 16 s. P cav_un_epca*02566420165-0114498Elsevier Aggregation Basic uncertain information Information fusion Probabilistic uncertainty Probability merging Uncertainty merging cav_un_auth*0361232 Jin L. CN 20 K cav_un_auth*0045803 Yang Y. CN cav_un_auth*0474976 Cheng Z. S. CN 20 cav_un_auth*0474977 Deveci M. TR 20 cav_un_auth*0101163 Mesiar Radko UTIA-B Ekonometrie Department of Econometrics E E Department of Econometrics 20 Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2024/E/mesiar-0599856.pdf https://www.sciencedirect.com/science/article/pii/S0165011424002999?via%3Dihub Basic uncertain information is a recently introduced and significant type of uncertainty that proves particularly valuable in decision-making environments with inherent uncertainties. In this study, we propose the concept of uncertainty cognition merging, which effectively combines basic uncertain information granules with probability measures to generate new probability measures within the same probability space. Additionally, we present a degenerated method that merges basic uncertain information granules with unit intervals to create new subintervals. We introduce four distinct uncertainty cognition merging methods and thoroughly compare and analyze their respective properties, limitations, and advantages. To demonstrate the practical application potential of our proposals, we provide numerical examples alongside further mathematical results. 2026 BA WOS 10000 10100 10101 5 RVO:67985556 https://hdl.handle.net/11104/0357456 S 109153 A MATHEMATICS.APPLIED|STATISTICS&PROBABILITY|COMPUTERSCIENCE.THEORY&METHODS 2.6 0.9 19.7 0.0062 0.613 14846 191 0.754 37.34 2.7 2335 Q1 2.200 229 Q1 82 1.43 Q1 91.4 2025 85206645259 SCOPUS PUBMED 001338864500001 WOS cav_un_epca*0256642 Fuzzy Sets and Systems 498 1 2025 0165-0114 1872-6801 Elsevier