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Some bipolar-preferences-involved aggregation methods for a sequence of OWA weight vectors


The ordered weighted averaging (OWA) operator and its associated weight vectors have been both theoretically and practically verified to be powerful and effective in modeling the optimism/pessimism preference of decision makers. When several different OWA weight vectors are offered, it is necessary to develop certain techniques to aggregate them into one OWA weight vector. This study firstly details several motivating examples to show the necessity and usefulness of merging those OWA weight vectors. Then, by applying the general method for aggregating OWA operators proposed in a recent literature, we specifically elaborate the use of OWA aggregation to merge OWA weight vectors themselves. Furthermore, we generalize the normal preference degree in the unit interval into a preference sequence and introduce subsequently the preference aggregation for OWA weight vectors with given preference sequences. Detailed steps in related aggregation procedures and corresponding numerical examples are also provided in the current study.

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This work was supported by the Scientific Research Start-up Foundation Project (Grant No. 184080H202B165), the Science and Technology Assistance Agency (Grant No. APVV-18-0052), the project of Grant Agency of the Czech Republic (GAČR) (Grant No. 18-06915S), the National Natural Science Foundation of China (Grant No. 71801175) and the Fundamental Research Funds for the Central Universities (Grant No. 2042018kf0006).

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Correspondence to Zhen-Song Chen.

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Jin, L., Yager, R.R., Chen, ZS. et al. Some bipolar-preferences-involved aggregation methods for a sequence of OWA weight vectors. Soft Comput 25, 895–902 (2021).

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  • Aggregation functions
  • Decision making
  • Evaluation
  • OWA operators
  • Preference-involved aggregation