Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (2): 158-164.doi: 10.11896/jsjkx.240600044

• Database & Big Data & Data Science • Previous Articles     Next Articles

Game-theoretic Rough Group Consensus Decision-making Model Based on Individual-Whole SpanAdjustments and Its Applications

HOU Hanzhong1, ZHANG Chao1,2, LI Deyu1,2   

  1. 1 School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China
    2 Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education,Shanxi University,Taiyuan 030006,China
  • Received:2024-06-05 Revised:2024-08-22 Online:2025-02-15 Published:2025-02-17
  • About author:HOU Hanzhong,born in 2001,master,is a member of CCF(No.U3251G).His main research interests include data mining,granular computing and intelli-gent decision.
    ZHANG Chao,born in 1989,Ph.D.His main research interests include data mining,granular computing and intelli-gent decision.
  • Supported by:
    National Natural Science Foundation of China(62272284,62072294,62473241),Special Fund for Science and Technology Innovation Teams of Shanxi(202204051001015),Training Program for Young Scientific Researchers of Higher Education Institutions in Shanxi,Cultivate Scientific Research Excellence Programs of Higher Education Institutions in Shanxi(2019SK036),Wenying Young Scholars of Shanxi University and Central Government Guides Local Science and Technology Innovation(YDZJSX2024D015).

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Abstract: Group consensus decision-making refers to the process in which a group of individuals adjust their opinions through collective negotiation to ensure that the problem is solved on the premise of reaching consensus.Exploring the group consensus model through the example of air quality assessments,this study first uses intuitionistic fuzzy numbers to represent individual evaluations and proposes a novel mapping function to convert real numbers into intuitionistic fuzzy numbers.Next,a method to adjust the relative span between individual and overall evaluations is proposed to achieve consensus,which helps quickly identify and adjust the differences between individual and overall evaluations.Then,based on the achieved consensus,a game-theoretic rough set model is employed to determine the threshold by balancing accuracy and generality.This approach improves performance by reducing the size of the boundary region,thereby increasing the accuracy of the decision results.Finally,the feasibility and effectiveness of the proposed method are validated through an air quality evaluation example.In conclusion,the proposed model not only enriches the related theoretical framework and effectively reduces the risk of group consensus decision-making,but also provides a feasible path for solving complex decision-making problems.

Key words: Granular computing, Three-way decision, Group consensus decision-making, Intuitionistic fuzzy number, Game-theoretic rough set

CLC Number: 

  • TP181
[1]DING J J,ZHANG C,LI D Y,et al.Hyperauomation for airquality evaluations:A perspective of evidential three-way decision-making[J].Cognitive Computation,2023,16(5):2437-2453.
[2]XUE Z N,JING M M,LI Y X,et al.Variable precision multi-granulation covering rough intuitionistic fuzzy sets[J].Granular Computing,2023,8(3):577-596.
[3]SINGH K,SINGH S.On a dual preximity measure based on intuitionistic fuzzy sets[J].Neural Computing & Applications,2023,35(8):6293-6311.
[4]KHAMIS A,AHMAD A G.A note on direct product of complex intuitionistic fuzzy subfield[J].Journal of Intelligent & Fuzzy Systems,2023,45(2):2111-2132.
[5]DEVECI K.Ranking intuitionistic fuzzy sets with hypervolume-based approach:An application for multi-criteria assessment of energy alterna- tives[J].Applied Soft Computing,2024,150,111038.
[6]GONG Z T,WANG F D.Operation properties and(a,β)-equalities of complex intuitionistic fuzzy sets[J].Soft Computing,2023,27(8):4369-4391.
[7]LIN Z Y,CHANG J Y,JENG J T.An efficient intuitionisticfuzzy sets base stations deployment strategy in internet of things systems[J].International Journal of Fuzzy Systems,2023,25(5):1882-1894.
[8]GUO L,ZHAN J M,KOU G.Consensus reaching process using personalized modification rules in large-scale group decision-making[J].Information Fusion,2024,103:102-138.
[9]ZHANG Z,LI Z L.A consensus-reaching model for group decision making that considers personalized individual semantics and consistency[J].Journal of Industrial Engineering and Enginee-ring Management,2023,37(6):157-168.
[10]LI C C,DONG Y C,PEDRYCZ W,et al.Integrating continual personalized individual semantics learning in consensus reaching in linguistic group decision making[J].IEEE Transac tions on Systems,Man,and Cybernetics:Systems,2022,52(3):1525-1536.
[11]LIANG X,GUO J,LIU P D.A consensus model considers ma-naging manipulative and overconfident behaviours in large-scale group decision-making[J].Information Sciences,2024,654:119848.
[12]LU Y L,XU Y J,LI M Q.Large-scale group decision-making method based on negative behavior management and improved minimum cost consensus model in social network environment [J].Control and Decision,2024,39(1):327-335.
[13]YUAN Y X,CHENG D,ZHOU Z L,et al.A minimum adjust-ment cost consensus framework considering harmony degrees and trust propagation for social network group decision making[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2023,53(3):1453-1465.
[14]QIN J D,LIANG Y Y.Modeling the minimum cost consensus problem with risk preferences[J].Journal of The Operational Research Society,2023,74(1):417-429.
[15]WU Z Q,ZHU K,QU S J.Distributionally robust optimization model for a minimum cost consensus with asymmetric adjustment costs based on the Wasserstein metric[J].Mathematics,2022,10(22):4312.
[16]LIU P D,LI Y,ZHANG X H,et al.A multiattri -bute group decision-making method with probabilistic linguistic information based on an adaptive consensus reaching model and evidential reasoning[J].IEEE Transactions on Cybernetics,2023,53(3):1905-1919.
[17]SHEN Y F,MA X L,ZHAN J M.A two-stage adaptive consensus reaching model by virtue of three-way clustering for large-scale group decision making[J].Information Sciences,2023,649:119658.
[18]ZHANG J J,ZHANG C,CHEN W Z,et al.Three-way spherical fuzzy multi-attribute group decision-making based on multigranulation probab- ilistic rough sets under bounded rationality[J].Fuzzy System and Mathematics,2022,36(6):12-25.
[19]HERBERT J P,YAO J T.Game-theoretic rough sets[J].Fundamenta Informaticae,2011,108(3/4):267 -286.
[20]AZAM N,YAO J T.Game-theoretic rough sets for recommender systems[J].Knowledge-based Systems,2014,72:96-107.
[21]AZAM N,YAO J T.Analyzing uncertainties of probabilisticrough set regions with game-theoretic rough sets[J].International Journal of Approximate Reasoning,2014,55(1):142-155.
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