计算机科学 ›› 2025, Vol. 52 ›› Issue (2): 158-164.doi: 10.11896/jsjkx.240600044

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于个体-整体跨度调整的博弈粗糙群共识决策模型及其应用

侯涵中1, 张超1,2, 李德玉1,2   

  1. 1 山西大学计算机与信息技术学院 太原 030006
    2 计算智能与中文信息处理教育部重点实验室(山西大学) 太原 030006
  • 收稿日期:2024-06-05 修回日期:2024-08-22 出版日期:2025-02-15 发布日期:2025-02-17
  • 通讯作者: 张超(czhang@sxu.edu.cn)
  • 作者简介:(hhz830016@163.com)
  • 基金资助:
    国家自然科学基金(62272284,62072294,62473241);山西省科技创新青年人才团队项目(202204051001015);山西省高等学校青年科研人员培育计划;山西省高等学校优秀成果培育项目(2019SK036);山西大学文瀛青年学者;中央引导地方科技发展资金项目(YDZJSX2024D015).

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).

摘要: 群共识决策指在面对多个备选方案时,一组个体通过集体协商,调整不同个体的意见,以确保在达成共识的前提下解决问题的过程。以空气质量评估为例探索群共识模型。首先,采用直觉模糊数来对个体评价进行表示,同时提出新型映射函数来将实数转化为直觉模糊数。其次,提出调整个体与整体相对跨度的方法来达成共识,有助于快速锁定个体和整体的差异,从而对个体评价进行调整。然后,在达成共识的基础上,采用博弈粗糙集模型,通过权衡准确性与通用性来确定阈值。在提升性能的基础上,减少边界区域的大小,从而增加决策结果的准确性。最后,通过空气质量评价的实例,验证所提方法的可行性和有效性。综上所述,该模型的提出不仅丰富了相关理论体系,有效降低了群共识决策的风险,更为复杂决策问题的解决提供了一种可行的路径。

关键词: 粒计算, 三支决策, 群共识决策, 直觉模糊数, 博弈粗糙集

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

中图分类号: 

  • 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.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!