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A comprehensive study on effect of multi-subgroup background in group decision-making

  • Soft computing in decision making and in modeling in economics
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Abstract

In group decision-making problems, experts often form multiple subgroups depending upon their backgrounds or attitudes. To explore the effect of different types of subgroups’ formation on the final decision and to make the decision process more conforming to human custom, we propose a comprehensive study on the effect of “multi-subgroup background” on a basis of weights optimization and Granular Computing techniques. Firstly, a comparison of different types of clustering strategies is realized to find the most suitable method for clustering experts. Since Analytical Hierarchy Process is selected as the fundamental model, it becomes a reciprocal matrices’ or eigenvectors’ clustering task. Secondly, allocation strategies of information granularity to each expert along with weight to each subgroup are carefully designed and optimized. Information granularity is viewed as a design asset to provide experts with some flexibility to adjust their evaluation results, whereas weights reflect the importance degrees of different subgroups and further help to increase the consensus. A granulation-degranulation process is developed to evaluate the performance of a set of weights and granularities under a multi-criteria objective’s guidance. Thirdly, a proper evolutionary optimization method like particle swarm optimization is redesigned to iteratively generate the best set of information granularities and weights under an equality constraint condition. A series of numerical studies on synthetic data and real-world data are executed to verify the effectiveness of our method. The results show same trend present across experts.

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Data Availability

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Funding

This work was supported by the National Natural Science Foundation of China (NSFC) 61773352 and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. School of Computer and Cyber Sciences, Communication University of China, Beijing, China

    Mingli Song & Lijie Han

  2. State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China

    Mingli Song

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6R 2G7, Canada

    Witold Pedrycz

  4. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Witold Pedrycz

  5. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Witold Pedrycz

Authors
  1. Mingli Song
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  2. Lijie Han
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by MS, LH and WP. The first draft of the manuscript was written by MS, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Mingli Song.

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Song, M., Han, L. & Pedrycz, W. A comprehensive study on effect of multi-subgroup background in group decision-making. Soft Comput 26, 13543–13566 (2022). https://doi.org/10.1007/s00500-022-07498-7

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