多粒度语言环境下基于持续学习和行为建模的个性化语义共识决策模型

doi:10.16381/j.cnki.issn1003-207x.2023.0230

摘要/Abstract

摘要：

多粒度语言表征与词义复杂性问题是词计算与语言决策领域关注的焦点，本文研究多粒度语言环境下个性化语义持续学习与行为建模，用于支持群体共识决策。首先，结合个体语义容易受到决策环境和其他决策者偏好的影响而动态改变的特点，构建了共识决策问题中基于前景理论的行为模型和持续学习的个性化语义模型。然后，采用基于多粒度语言偏好关系的共识测量方法，并设计共识反馈调整规则，用于支持多粒度语言环境下群体共识过程。最后，通过数值与仿真分析来展示模型的运算过程，并讨论了动态个性化语义、损失规避等决策行为对群体共识演化的影响。研究结果通过融入持续学习与行为建模，进一步完善个性化语义与共识决策模型。

关键词: 多粒度语言决策, 持续学习, 前景理论, 个性化语义, 共识

Abstract:

Along with the rapid development of Internet technology and the rise of new social media， the pattern of group decision making has been changed profoundly， and it often occurs in a linguistic context. The multi-granular linguistic representations and semantics complexity have been the focuses in computing with words and linguistic decision-making. The personalized individual semantics （PISs） model with continuous learning and behavior modeling is proposed to support group consensus decision-making under multi-granular linguistic environment. Firstly， it is considered that the PISs of decision makers will be affected by the decision-making environment and decision-makers' preferences， so it will be changing dynamically during the consensus reaching process. Thus， a PISs model of behavior modeling and continuous learning based on prospect theory has been proposed. Then， the consensus measurement and feedback recommendation based on PISs under multi-granular linguistic context are developed to support the consensus process. Finally， numerical and simulation analysis are presented to illustrate the use of the proposed model， and to show the influences of the decision-making behaviors （e.g.， dynamic PISs， loss aversion） on the evolution of group consensus. The research results in this paper show that the PISs and consensus decision-making model can be improved by integrating continuous learning and behavior modeling.

Key words: multi-granular linguistic decision making, continuous learning, prospect theory, personalized individual semantics, consensus

中图分类号:

C934

李聪聪, 梁海明, 董玉成. 多粒度语言环境下基于持续学习和行为建模的个性化语义共识决策模型[J]. 中国管理科学, 2025, 33(9): 201-212.

Congcong Li, Haiming Liang, Yucheng Dong. Personalized Individual Semantic with Continuous Learning and Behavior Modeling to Support Consensus Reaching in Multi-granular Linguistic Environment[J]. Chinese Journal of Management Science, 2025, 33(9): 201-212.

图/表 9

图1

图2

图3

图4

表1

决策者e1，e3和e4的个性化数值标度"

$N S k, 0 s i k$	i=-5	i=-4	i=-3	i=-2	i=-1	i=0	i=1	i=2	i=3	i=4	i=5
$k = 1$	-	-	-	0	0.1	0.53	0.899	1	-	-	-
$k = 3$	-	0	0.15	0.3	0.45	0.6	0.7	0.8	0.9	1	-
$k = 4$	0	0.097	0.196	0.296	0.399	0.508	0.608	0.706	0.804	0.902	1

表1

图5

图6

图7

图8

参考文献 44

[1]	Zadeh L A. Fuzzy logic = computing with words［J］. IEEE Transactions on Fuzzy Systems， 1996， 4（2）： 103-111.
[2]	Zadeh L A. The concept of a linguistic variable and its application to approximate reasoning—I［J］. Information Sciences， 1975， 8（3）： 199-249.
[3]	Herrera-Viedma E， Palomares I， Li C C， et al. Revisiting fuzzy and linguistic decision making： Scenarios and challenges for making wiser decisions in a better way［J］. IEEE Transactions on Systems， Man， and Cybernetics： Systems， 2021， 51（1）： 191-208.
[4]	Dong Y C， Xu J P. Linguistic decision making - Numerical scale model and consistency-driven methodology［M］.Singapore： Springer-Verlag， 2019.
[5]	Herrera F， Martinez L. A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multi-expert decision-making［J］. IEEE Transactions on Systems， Man， and Cybernetics Part B， Cybernetics， 2001， 31（2）： 227-234.
[6]	Morente-Molinera J A， Pérez I J， Ureña M R， et al. On multi-granular fuzzy linguistic modeling in group decision making problems： A systematic review and future trends［J］. Knowledge-Based Systems， 2015， 74： 49-60.
[7]	Li C C， Dong Y， Herrera F， et al. Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching［J］. Information Fusion， 2017， 33： 29-40.
[8]	Herrera F， Martinez L. A 2-tuple fuzzy linguistic representation model for computing with words［J］. IEEE Transactions on Fuzzy Systems， 2000， 8（6）： 746-752.
[9]	Mendel J M， Wu H. Type-2 fuzzistics for symmetric interval type-2 fuzzy sets： Part 1， forward problems［J］. IEEE Transactions on Fuzzy Systems， 2006， 14（6）： 781-792.
[10]	Mendel J M， Wu H. Type-2 fuzzistics for symmetric interval type-2 fuzzy sets： Part 2， inverse problems［J］. IEEE Transactions on Fuzzy Systems， 2007， 15（2）： 301-308.
[11]	Wang H， Xu Z. Theory and approaches of group decision making with uncertain linguistic expressions［M］. Cham： Springer Nature Singapore， 2019.
[12]	徐选华，吴慧迪. 基于改进云模型的语言偏好信息多属性大群体决策方法［J］. 管理工程学报， 2018， 32（1）： 117-125.
	Xu X H， Wu H D. Approach for multi-attribute large group decision-making with linguistic preference information based on improved cloud model［J］. Journal of Industrial Engineering and Engineering Management， 2018， 32（1）： 117-125.
[13]	于文玉，仲秋雁，张震. 权重信息不完全的多粒度犹豫模糊语言群决策［J］. 系统工程理论与实践， 2018， 38（3）： 777-785.
	Yu W Y， Zhong Q Y， Zhang Z. Multi-granular hesitant fuzzy linguistic group decision making with incomplete weight information［J］. Systems Engineering-Theory & Practice， 2018， 38（3）： 777-785.
[14]	Li C C， Rodríguez R M， Martínez L， et al. Personalized individual semantics based on consistency in hesitant linguistic group decision making with comparative linguistic expressions［J］. Knowledge-Based Systems， 2018， 145： 156-165.
[15]	Li C C， Dong Y， Herrera F. A consensus model for large-scale linguistic group decision making with a feedback recommendation based on clustered personalized individual semantics and opposing consensus groups［J］. IEEE Transactions on Fuzzy Systems， 2019， 27（2）： 221-233.
[16]	Li C C， Dong Y， Liang H， et al. Data-driven method to learning personalized individual semantics to support linguistic multi-attribute decision making［J］. Omega， 2022， 111： 102642.
[17]	Li C C， Dong Y， Pedrycz W， et al. Integrating continual personalized individual semantics learning in consensus reaching in linguistic group decision making［J］. IEEE Transactions on Systems， Man， and Cybernetics： Systems， 2022， 52（3）： 1525-1536.
[18]	Dong Y， Xu Y， Yu S. Computing the numerical scale of the linguistic term set for the 2-tuple fuzzy linguistic representation model［J］. IEEE Transactions on Fuzzy Systems， 2009， 17（6）： 1366-1378.
[19]	Dong Y， Herrera-Viedma E. Consistency-driven automatic methodology to set interval numerical scales of 2-tuple linguistic term sets and its use in the linguistic GDM with preference relation［J］. IEEE Transactions on Cybernetics， 2015， 45（4）： 780-792.
[20]	Tang X， Peng Z， Zhang Q， et al. Consistency and consensus-driven models to personalize individual semantics of linguistic terms for supporting group decision making with distribution linguistic preference relations［J］. Knowledge-Based Systems， 2020， 189： 105078.
[21]	Tang X， Zhang Q， Peng Z， et al. Distribution linguistic preference relations with incomplete symbolic proportions for group decision making［J］. Applied Soft Computing， 2020， 88： 106005.
[22]	Zhang Z， Li Z. Personalized individual semantics-based consistency control and consensus reaching in linguistic group decision making［J］. IEEE Transactions on Systems， Man， and Cybernetics： Systems， 2022， 52（9）： 5623-5635.
[23]	Zhang H， Dong Y， Xiao J， et al. Personalized individual semantics-based approach for linguistic failure modes and effects analysis with incomplete preference information［J］. IISE Transactions， 2020， 52（11）： 1275-1296.
[24]	Liang H， Li C C， Dong Y， et al. Linguistic opinions dynamics based on personalized individual semantics［J］. IEEE Transactions on Fuzzy Systems， 2021， 29（9）： 2453-2466.
[25]	Gao Y， Zhang Z. Consensus reaching with non-cooperative behavior management for personalized individual semantics-based social network group decision making［J］. Journal of the Operational Research Society， 2022， 73（11）： 2518-2535.
[26]	Liu X， Zhang Y， Xu Y， et al. A consensus model for group decision-making with personalized individual self-confidence and trust semantics： A perspective on dynamic social network interactions［J］. Information Sciences， 2023， 627： 147-168.
[27]	Zuheros C， Li C C， Cabrerizo F J， et al. Computing with words： Revisiting the qualitative scale［J］. International Journal of Uncertainty， Fuzziness and Knowledge- Based Systems， 2018， 26（）： 127-143.
[28]	Xu X H， Du Z J， Chen X H. Consensus model for multi-criteria large-group emergency decision making considering non-cooperative behaviors and minority opinions［J］. Decision Support Systems， 2015， 79： 150-160.
[29]	刘培德，滕飞. 基于共识模型和ORESTE的扩展概率语言多属性群决策方法［J］. 中国管理科学， 2021， 29（3）： 199-209.
	Liu P D， Teng F. Multiple attribute group decision-making method based on consensus model and ORESTE method for extended probabilistic linguistic term set［J］. Chinese Journal of Management Science， 2021， 29（3）： 199-209.
[30]	姚升保，古淼. 移情关系影响下的群体共识决策模型研究［J］. 中国管理科学， 2021， 29（11）： 203-214.
	Yao S B， Gu M. A consensus model for group decision making under the influence of empathy［J］. Chinese Journal of Management Science， 2021， 29（11）： 203-214.
[31]	张恒杰，王芳，董庆兴，等. 群体共识决策的研究进展与展望［J］.电子科技大学学报（社科版）， 2021， 23（2）： 26-37.
	Zhang H J， Wang F， Dong Q X， et al. Consensus in group decision making： Research progress and prospect［J］. Journal of University of Electronic Science and Technology of China （Social Sciences Edition）， 2021， 23（2）： 26-37.
[32]	Lesort T， Lomonaco V， Stoian A， et al. Continual learning for robotics： Definition， framework， learning strategies， opportunities and challenges［J］. Information Fusion， 2020， 58： 52-68.
[33]	Kahneman D， Tversky A. Prospect theory： An analysis of decision under risk［J］. Econometrica， 1979， 47（2）： 263.
[34]	Schweitzer M E， Cachon G P. Decision bias in the newsvendor problem with a known demand distribution： Experimental evidence［J］. Management Science， 2000， 46（3）： 404-420.
[35]	周永务，刘敏，於慧琳，等. 基于前景理论的损失规避零售商的订货与融资策略研究［J］. 系统工程理论与实践， 2022， 42（11）： 2957-2975.
	Zhou Y W， Liu M， Yu H L， et al. Optimal ordering and financing decisions for a loss-averse retailer under prospect theory［J］. Systems Engineering-Theory & Practice， 2022， 42（11）： 2957-2975.
[36]	Herrera-Viedma E， Herrera F， Chiclana F， et al. Some issues on consistency of fuzzy preference relations［J］. European Journal of Operational Research， 2004， 154（1）： 98-109.
[37]	Palomares I， Estrella F J， Martínez L， et al. Consensus under a fuzzy context： Taxonomy， analysis framework AFRYCA and experimental case of study［J］. Information Fusion， 2014， 20： 252-271.
[38]	Herrera-Viedma E， Cabrerizo F J， Kacprzyk J， et al. A review of soft consensus models in a fuzzy environment［J］. Information Fusion， 2014， 17： 4-13.
[39]	Wu J， Dai L， Chiclana F， et al. A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust［J］. Information Fusion， 2018， 41： 232-242.
[40]	徐选华，侯宇舟. 大群体风险决策理论与方法研究现状及发展趋势［J］. 电子科技大学学报（社科版）， 2021， 23（4）： 1-6+74.
	Xu X H， Hou Y Z. Research status and development trend of large group risk decision-making theory and method［J］. Journal of University of Electronic Science and Technology of China （Social Sciences Edition）， 2021， 23（4）： 1-6+74.
[41]	Zhang Z， Yu W， Martínez L， et al. Managing multigranular unbalanced hesitant fuzzy linguistic information in multiattribute large-scale group decision making： A linguistic distribution-based approach［J］. IEEE Transactions on Fuzzy Systems， 2020， 28（11）： 2875-2889.
[42]	余高锋，李登峰，吴坚，等. 考虑决策者损失规避的异质信息多属性变权决策方法［J］. 中国管理科学， 2018， 26（9）： 141-147.
	Yu G F， Li D F， Wu J， et al. Heterogeneous multi-attribute variable weight decision making method considering decision maker’s loss aversion［J］. Chinese Journal of Management Science， 2018， 26（9）： 141-147.
[43]	徐宗本，唐年胜，程学旗. 数据科学：它的内涵、方法、意义与发展［M］. 北京：科学出版社， 2022.
	Xu Z B， Tang N S， Cheng X Q. Data science［M］. Beijing： Science Press， 2022.
[44]	鲁芳，吴健，罗定提. 考虑产品体验性和营销努力的分销渠道合作策略研究［J］. 中国管理科学， 2020， 28（10）： 144-155.
	Lu F， Wu J， Luo D T. Research on distribution channel cooperation strategy considering product experience and sales efforts［J］. Chinese Journal of Management Science， 2020， 28（10）： 144-155.