Movatterモバイル変換

[0]ホーム
URL:

Skip to main content
Springer Nature Link
Log in

Large Group Decision-Making Method Based on Social Network Analysis: Integrating Evaluation Information and Trust Relationships

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

In the context of large group decision-making (LGDM), the opinions of individuals can influence each other due to their trust relationships. So, trust relationships should be deemed as just as important as evaluation information, and they should be considered jointly throughout the LGDM. This study first transforms the trust relationships between decision-makers into an information type, labeled as compromise information, whose form is the same as the evaluation information. The compromise information is utilized to incorporate trust relationships into various stages of the decision-making process, including clustering, weight determination, consensus reaching, and alternative selection. In the expert clustering and weight determination processes, more criteria and factors are considered by considering the compromise information. In the consensus reaching process, an optimization model is built to adjust the evaluation information of clusters to simultaneously guarantee a substantial increase in the global consensus level and minimize the adjustment cost. The compromise information also serves as a reference to limit the range of the adjusted information. An objective method to determine the consensus threshold is proposed. The proposed method is validated through an application example and comparisons, demonstrating its rationality and effectiveness. Simulation results indicate that the proposed consensus reaching method converges regardless of the number of experts, alternatives, and criteria. The proposed method integrates evaluation information and trust relationships into the LGDM process, thereby improving the rationality and scientificity of the decision results.

This is a preview of subscription content,log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

The datasets generated during and/or analyzed during the current study are available from the first author on reasonable request.

References

  1. Wu J, Dai L, Chiclana F, Fujita H, Herrera-Viedma E. A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust. Inf Fusion. 2018;41:232–42.

    Article  Google Scholar 

  2. Du Z, Yu S, Luo H, Lin X. Consensus convergence in large-group social network environment: coordination between trust relationship and opinion similarity. Knowl Based Syst. 2021;217: 106828.

    Article  Google Scholar 

  3. Liao H, Wu Z, Tang M, Wan Z. An interactive consensus reaching model with updated weights of clusters in large-scale group decision making. Eng Appl Artif Intell. 2022;107: 104532.

    Article  Google Scholar 

  4. Xu X, Du Z, Chen X. Consensus model for multi-criteria large-group emergency decision making considering non-cooperative behaviors and minority opinions. Decis Support Syst. 2015;79:150–60.

    Article  Google Scholar 

  5. Xu X, Zhang Q, Chen X. Consensus-based non-cooperative behaviors management in large-group emergency decision-making considering experts’ trust relations and preference risks. Knowl Based Syst. 2020;190: 105108.

    Article  Google Scholar 

  6. Zhang H, Zhu W, Chen X, Wu Y, Liang H, Li C, Dong Y. Managing flexible linguistic expression and ordinal classification-based consensus in large-scale multi-attribute group decision making. Ann Oper Res. 2022: 1-54.

  7. Zhong X, Xu X. Clustering-based method for large group decision making with hesitant fuzzy linguistic information: integrating correlation and consensus. Appl Soft Comput. 2020;87: 105973.

    Article  Google Scholar 

  8. Labella Á, Liu Y, Rodríguez RM, Martínez L. Analyzing the performance of classical consensus models in large scale group decision making: a comparative study. Appl Soft Comput. 2018;67:677–90.

    Article  Google Scholar 

  9. Du Z, Luo H, Lin X, Yu S. A trust-similarity analysis-based clustering method for large-scale group decision-making under a social network. Inf Fusion. 2020;63:13–29.

    Article  Google Scholar 

  10. Wu T, Zhang K, Liu X, Cao C. A two-stage social trust network partition model for large-scale group decision-making problems. Knowl Based Syst. 2019;163:632–43.

    Article  Google Scholar 

  11. Zhang C, Zhao M, Zhao L, Yuan Q. A consensus model for large-scale group decision-making based on the trust relationship considering leadership behaviors and non-cooperative behaviors. Group Decis Negot. 2021;30(3):553–86.

    Article  Google Scholar 

  12. Zhong X, Xu X, Yin X. A multi-stage hybrid consensus reaching model for multi-attribute large group decision-making: Integrating cardinal consensus and ordinal consensus. Comput Ind Eng. 2021;158: 107443.

    Article  Google Scholar 

  13. You X, Hou F, Lou Z. Consensus building in multi-criteria group decision-making with single-valued neutrosophic sets. Cognit Comput. 2021;13(6):1496–514.

    Article  Google Scholar 

  14. Lee VK, Harris LT. How social cognition can inform social decision making. Front Neurosci. 2013;7: 259.

  15. Chu J, Wang Y, Liu X, Liu Y. Social network community analysis based large-scale group decision making approach with incomplete fuzzy preference relations. Inf Fusion. 2020;60:98–120.

    Article  Google Scholar 

  16. Liu B, Zhou Q, Ding R, Palomares I, Herrera F. Large-scale group decision making model based on social network analysis: trust relationship-based conflict detection and elimination. Eur J Oper Res. 2019;275(2):737–54.

    Article MathSciNet  Google Scholar 

  17. Lu Y, Xu Y, Herrera-Viedma E, Han Y. Consensus of large-scale group decision making in social network: the minimum cost model based on robust optimization. Inf Sci. 2021;547:910–30.

    Article MathSciNet  Google Scholar 

  18. Li X, Liao H. A group decision making method to manage internal and external experts with an application to anti-lung cancer drug selection. Expert Syst Appl. 2021;183: 115379.

    Article  Google Scholar 

  19. Tian Z, Nie R, Wang J. Social network analysis-based consensus-supporting framework for large-scale group decision-making with incomplete interval type-2 fuzzy information. Inf Sci. 2019;502:446–71.

    Article  Google Scholar 

  20. Wu T, Liu X, Liu F. An interval type-2 fuzzy TOPSIS model for large scale group decision making problems with social network information. Inf Sci. 2018;432:392–410.

    Article MathSciNet  Google Scholar 

  21. Yan B, Wang Y, Xia W, Hu X. Novel consensus-reaching model in the social network environment for large-group emergency decision-making: an approach to managing non-cooperative behaviors. Artif Intell Rev. 2023: 1-37.

  22. Sun X, Zhu J. Large-scale group classification decision making method and its application with trust–interest dual factors in social network. Appl Soft Comput. 2023;133: 109890.

    Article  Google Scholar 

  23. Wu T, Liu X, Liu F. The solution for fuzzy large-scale group decision making problems combining internal preference information and external social network structures. Soft Comput. 2019;23(18):9025–43.

    Article  Google Scholar 

  24. Zhou M, Zhou Y, Liu X, Wu J, Fujita H, Herrera-Viedma E. An adaptive two-stage consensus reaching process based on heterogeneous judgments and social relations for large-scale group decision making. Inf Sci. 2023;644: 119280.

    Article  Google Scholar 

  25. Zhang C, Huang M, Gu J, Ye Y, Zeng S. A large-group dynamic decision-making method for assessing storm surge emergency plans under hybrid information. Expert Syst Appl. 2023;227: 120263.

    Article  Google Scholar 

  26. Liu P, Zhang K, Wang P, Wang F. A clustering- and maximum consensus-based model for social network large-scale group decision making with linguistic distribution. Inf Sci. 2022;602:269–97.

    Article  Google Scholar 

  27. Tan X, Zhu J, Cabrerizo FJ, Herrera-Viedma E. A cyclic dynamic trust-based consensus model for large-scale group decision making with probabilistic linguistic information. Appl Soft Comput. 2021;100: 106937.

    Article  Google Scholar 

  28. Gai T, Cao M, Cao Q, Wu J, Yu G, Zhou M. A joint feedback strategy for consensus in large-scale group decision making under social network. Comput Ind Eng. 2020;147: 106626.

    Article  Google Scholar 

  29. Li S, Wei C. A two-stage dynamic influence model-achieving decision-making consensus within large scale groups operating with incomplete information. Knowl Based Syst. 2020;189: 105132.

    Article  Google Scholar 

  30. Lu Y, Xu Y, Huang J, Wei J, Herrera-Viedma E. Social network clustering and consensus-based distrust behaviors management for large-scale group decision-making with incomplete hesitant fuzzy preference relations. Appl Soft Comput. 2022;117: 108373.

    Article  Google Scholar 

  31. Gai T, Cao M, Chiclana F, Zhang Z, Dong Y, Herrera-Viedma E, Wu J. Consensus-trust driven bidirectional feedback mechanism for improving consensus in social network large-group decision making. Group Decis Negot. 2023;32(1):45–74.

    Article  Google Scholar 

  32. Wasserman S, Faust K. Social network analysis: methods and applications Cambridge university press, 1994.

  33. Pérez LG, Mata F, Chiclana F. Social network decision making with linguistic trustworthiness-based induced OWA operators. Int J Intell Syst. 2014;29(12):1117–37.

    Article  Google Scholar 

  34. Ren R, Tang M, Liao H. Managing minority opinions in micro-grid planning by a social network analysis-based large scale group decision making method with hesitant fuzzy linguistic information. Knowl Based Syst. 2020;189: 105060.

    Article  Google Scholar 

  35. Liu X, Xu Y, Herrera F. Consensus model for large-scale group decision making based on fuzzy preference relation with self-confidence: detecting and managing overconfidence behaviors. Inf Fusion. 2019;52:245–56.

    Article  Google Scholar 

  36. Xu X, Du Z, Chen X, Cai C. Confidence consensus-based model for large-scale group decision making: a novel approach to managing non-cooperative behaviors. Inf Sci. 2019;477:410–27.

    Article  Google Scholar 

  37. Xu X, Zhong X, Chen X, Zhou Y. A dynamical consensus method based on exit–delegation mechanism for large group emergency decision making. Knowl Based Syst. 2015;86:237–49.

    Article  Google Scholar 

  38. Ma Z, Zhu J, Ponnambalam K, Zhang S. A clustering method for large-scale group decision-making with multi-stage hesitant fuzzy linguistic terms. Inf Fusion. 2019;50:231–50.

    Article  Google Scholar 

  39. Zhong X, Xu X, Pan B. A non-threshold consensus model based on the minimum cost and maximum consensus-increasing for multi-attribute large group decision-making. Inf Fusion. 2022;77:90–106.

    Article  Google Scholar 

  40. Tang M, Zhou X, Liao H, Xu J, Fujita H, Herrera F. Ordinal consensus measure with objective threshold for heterogeneous large-scale group decision making. Knowl Based Syst. 2019;180:62–74.

    Article  Google Scholar 

  41. Wu Z, Xu J. A consensus model for large-scale group decision making with hesitant fuzzy information and changeable clusters. Inf Fusion. 2018;41:217–31.

    Article  Google Scholar 

  42. Ureña R, Chiclana F, Melançon G, Herrera-Viedma E. A social network based approach for consensus achievement in multiperson decision making. Inf Fusion. 2019;47:72–87.

    Article  Google Scholar 

  43. Zheng Y, Xu Z, He Y, Tian Y. A hesitant fuzzy linguistic bi-objective clustering method for large-scale group decision-making. Expert Syst Appl. 2021;168: 114355.

    Article  Google Scholar 

  44. Kim YS. Multi-objective clustering with data - and human-driven metrics. J Comput Inf Syst. 2011;51(4):64–73.

    Google Scholar 

  45. Meng F, An Q, Tan C, Chen X. An approach for group decision making with interval fuzzy preference relations based on additive consistency and consensus analysis. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2017;47(8):2069–82.

    Article  Google Scholar 

  46. Zhang H, Dong Y, Chen X. The 2-rank consensus reaching model in the multigranular linguistic multiple-attribute group decision-making. IEEE Trans Syst Man Cybern Syst. 2018;48(12):2080–94.

    Article  Google Scholar 

  47. Liang Q, Liao X, Liu J. A social ties-based approach for group decision-making problems with incomplete additive preference relations. Knowl Based Syst. 2017;119:68–86.

    Article  Google Scholar 

  48. Wang P, Xu X, Huang S. An improved consensus-based model for large group decision making problems considering experts with linguistic weighted information. Group Decis Negot. 2019;28(3):619–40.

    Article  Google Scholar 

  49. Jain V, Sangaiah AK, Sakhuja S, Thoduka N, Aggarwal R. Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry. Neural Comput Appl. 2018;29(7):555–64.

    Article  Google Scholar 

  50. Song Y, Li G. A large-scale group decision-making with incomplete multi-granular probabilistic linguistic term sets and its application in sustainable supplier selection. J Oper Res Soc. 2018;70(5):827–41.

    Article  Google Scholar 

  51. Yu S, Du Z, Xu X. Hierarchical punishment-driven consensus model for probabilistic linguistic large-group decision making with application to global supplier selection. Group Decis Negot. 2021;30(6):1343–72.

    Article  Google Scholar 

  52. Wang C, Yang C, Cheng H. A fuzzy multicriteria decision-making (MCDM) model for sustainable supplier evaluation and selection based on triple bottom line approaches in the garment industry. Processes (Basel). 2019;7(7):400.

    Article  Google Scholar 

  53. Awasthi A, Govindan K, Gold S. Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach. Int J Prod Econ. 2018;195:106–17.

    Article  Google Scholar 

  54. Dong Y, Xu Y, Li H, Feng B. The OWA-based consensus operator under linguistic representation models using position indexes. Eur J Oper Res. 2010;203(2):455–63.

    Article  Google Scholar 

  55. Labella Á, Liu H, Rodríguez RM, Martínez L. A cost consensus metric for consensus reaching processes based on a comprehensive minimum cost model. Eur J Oper Res. 2020;281(2):316–31.

    Article MathSciNet  Google Scholar 

Download references

Funding

This study was funded by the Major Project for National Natural Science Foundation of China (72293574, 72091515), the Project for National Natural Science Foundation of China (71971217, 72073041), the Hunan Provincial Innovation Foundation for Postgraduate (CX20200143), and the Independent Exploration of Innovation Project for Postgraduate of Central South University (2020zzts014).

Author information

Authors and Affiliations

  1. School of Economics and Management, Fuzhou University, 350108, Fuzhou, Fujian, China

    Xiangyu Zhong

  2. School of Business, Central South University, No. 448, Lushan South Road, Yuelu District, 410083, Changsha, Hunan, China

    Xiangyu Zhong & Xuanhua Xu

  3. NUS Business School and The Logistics Institute-Asia Pacific, National University of Singapore, Singapore, Singapore

    Mark Goh

  4. School of Accounting, Hunan University of Finance and Economics, 410083, Changsha, China

    Bin Pan

Authors
  1. Xiangyu Zhong

    You can also search for this author inPubMed Google Scholar

  2. Xuanhua Xu

    You can also search for this author inPubMed Google Scholar

  3. Mark Goh

    You can also search for this author inPubMed Google Scholar

  4. Bin Pan

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toXuanhua Xu.

Ethics declarations

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of Interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhong, X., Xu, X., Goh, M.et al. Large Group Decision-Making Method Based on Social Network Analysis: Integrating Evaluation Information and Trust Relationships.Cogn Comput16, 86–106 (2024). https://doi.org/10.1007/s12559-023-10184-x

Download citation

Keywords

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Advertisement

[8]ページ先頭
©2009-2025 Movatter.jp