Affiliations: [a] School of Government Audit, Nanjing Audit University, Nanjing, China
| [b] Business School, Nanjing Normal University, Nanjing, China
| [c] Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Campus Arrosadia sn, Pamplona, Spain
| [d] Machine Intelligence Institute, Iona College, New Rochelle, NY, USA
| [e] Faculty of Civil Engineering, Slovak University of Technology, Slovakia, Palacky Univ Olomouc, Fac Sci, Dept Algebra & Geometry, Olomouc, Czech Republic
| [f] Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Campus Arrosadia sn, Pamplona, Spain
Correspondence:
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Corresponding author. LeSheng Jin, Business School, Nanjing Normal University, Nanjing, China. E-mail: jls1980@163.com.
Abstract: In decision making, very often the data collected are with different extents of uncertainty. The recently introduced concept, Basic Uncertain Information (BUI), serves as one ideal information representation to well model involved uncertainties with different extents. This study discusses some methods of BUI aggregation by proposing some uncertainty transformations for them. Based on some previously obtained results, we at first define IOWA operator with poset valued input vector and inducing vector. The work then defines the concept of uncertain system, on which we can further introduce the multi-layer uncertainty transformation for BUI. Subsequently, we formally introduce MUT_IOWA aggregation procedure, which has good potential to more and wider application areas. A numerical example is also offered along with some simple usage of it in decision making.
