Abstract
Uncertainty in information provides the possibility of having and applying preferences, and therefore, it necessitates the study of preferences-involved aggregation theory and techniques under uncertainty information environments. Hence, this paper discusses several rules-based decision-making methods in uncertain decision scenarios with or without fusion processes. Some special averaging aggregation methods and transformations for basic uncertain information vector are proposed. [0, 1]-valued, [\(-1\), 1]-valued, and vector-valued bipolar preferences with some of their relations are discussed. The discussions and analyses of aggregation mainly revolve around Sugeno integral. Finally, multiple aggregation schemes with vector-valued preferences with numerical examples are proposed, and some few related comparisons are provided.
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Acknowledgements
The authors would like to thank the Editor-in-Chief, the Associate Editor, and the anonymous referees for their prompt, thorough, and insightful comments on this paper.
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This work is partly supported by the National Natural Science Foundation of China (Grant Nos. 72171182 and 71801175), grant APVV-18-0052, VEGA 1/0006/19, and the grant Palacky University Olomouc IGAPrF2021.
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Jin, L., Yager, R.R., Chen, ZS. et al. Averaging aggregation under uncertainty and bipolar preference environments. Soft Comput 27, 8153–8159 (2023). https://doi.org/10.1007/s00500-023-08152-6
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DOI: https://doi.org/10.1007/s00500-023-08152-6