Three-dimensional dynamic trust-driven consensus model for social network group decision-making with application to sustainable supplier selection in a circular economy

Abstract

The trust degree between individuals plays an important role in social network group decision-making (SNGDM). The majority of current literature assumes that the trust degree between individuals is constant. However, the trust values among decision-makers (DMs) are subject to change over time. Thus, it is necessary to identify potential dynamic trust that is compatible with the actual SNGDM to support the consensus reaching. For this purpose, this article investigates consensus building that considers dynamic trust among DMs in SNGDM with linguistic distribution assessments (LDAs). Firstly, the three-dimensional trust degree among DMs is constructed from three perspectives: the trust relationship from social networks, the confidence level of DMs, and the similarity of DMs' preferences. Secondly, an optimization model is developed with the objective of maximizing consensus to determine how trust degree is assigned to each perspective, thereby determining the weights of DMs. Then, a double feedback-based consensus mechanism incorporating both opinion evolution and trust evolution is developed. Under the guidance of consensus mechanism, an improved SNGDM approach with LDAs is presented. Finally, we demonstrate our proposed approach through a case study of sustainable supplier selection in a circular economy. Comparative analysis and sensitive analysis verify our approach’s effectiveness.

Keywords

Social network group decision-making linguistic distribution assessments three-dimensional dynamic trust consensus feedback mechanism circular economy

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References

Alavi

Tavana

Mina

(2021). A dynamic decision support system for sustainable supplier selection in circular economy. Sustainable Production and Consumption, 27, 905–920. https://doi.org/10.1016/j.spc.2021.02.015

Cabrerizo

F. J.

Moreno

J. M.

Pérez

I. J.

Herrera-Viedma

(2010). Analyzing consensus approaches in fuzzy group decision making: Advantages and drawbacks. Soft Computing, 14(5), 451–463. https://doi.org/10.1007/s00500-009-0453-x

Chen

Ding

Dong

Liang

(2019). Managing consensus with minimum adjustments in group decision making with opinions evolution. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(4), 2299–2311. https://doi.org/10.1109/tsmc.2019.2912231

Cheng

Zhou

(2020). Reaching a minimum adjustment consensus in social network group decision-making. Information Fusion, 59, 30–43. https://doi.org/10.1016/j.inffus.2020.01.004

DeGroot

M. H.

(1974). Reaching a consensus. Journal of the American Statistical Association, 69(345), 118–121. https://doi.org/10.1080/01621459.1974.10480137

Dong

Ding

Martínez

Herrera

(2017). Managing consensus based on leadership in opinion dynamics. Information Sciences, 397–398, 187–205. https://doi.org/10.1016/j.ins.2017.02.052

Dong

Zha

Zhang

Herrera

(2020). Consensus reaching and strategic manipulation in group decision making with trust relationships. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(10), 6304–6318. https://doi.org/10.1109/tsmc.2019.2961752

Dong

Zha

Zhang

Kou

Fujita

Chiclana

Herrera-Viedma

(2018). Consensus reaching in social network group decision making: Research paradigms and challenges. Knowledge-Based Systems, 162, 3–13. https://doi.org/10.1016/j.knosys.2018.06.036

Fang

Van Kleef

G. A.

Sauter

D. A.

(2018). Person perception from changing emotional expressions: Primacy, recency, or averaging effect? Cognition and Emotion, 32(8), 1597–1610. https://doi.org/10.1080/02699931.2018.1432476

10.

French

J. R.

Jr. (1956). A formal theory of social power. Psychological Review, 63(3), 181–194. https://doi.org/10.1037/h0046123

11.

Gao

Santibanez Gonzalez

E. D.

Zhang

(2020). Green supplier selection in electronics manufacturing: An approach based on consensus decision making. Journal of Cleaner Production, 245, 118781. https://doi.org/10.1016/j.jclepro.2019.118781

12.

Geisendorf

Pietrulla

(2018). The circular economy and circular economic concepts—a literature analysis and redefinition. Thunderbird International Business Review, 60(5), 771–782. https://doi.org/10.1002/tie.21924

13.

Gou

Liao

(2017). Multiple criteria decision making based on Bonferroni means with hesitant fuzzy linguistic information. Soft Computing, 21, 6515–6529. https://doi.org/10.1007/s00500-016-2211-1

14.

Gray

R. M.

(2011). Entropy and information theory (2nd ed., pp. 17–18). Springer Science and Business Media.

15.

Hao

Cheng

Wang

Cheng

(2021). Consensus model with double feedback mechanism based on dynamic trust relationship in social network group decision-making. International Journal of Computational Intelligence Systems, 14(1), 491–502. https://doi.org/10.2991/ijcis.d.201228.001

16.

Guo

Gong

Zhang

W. G.

(2023). Minimum cost consensus modeling under dynamic feedback regulation mechanism considering consensus principle and tolerance level. European Journal of Operational Research, 306(3), 1279–1295. https://doi.org/10.1016/j.ejor.2022.08.033

17.

Hassani

Razavi-Far

Saif

Chiclana

Krejcar

Herrera-Viedma

(2022). Classical dynamic consensus and opinion dynamics models: A survey of recent trends and methodologies. Information Fusion, 88, 22–40. https://doi.org/10.1016/j.inffus.2022.07.003

18.

Herrera

Herrera-Viedma

Verdegay

J. L.

(1995). A sequential selection process in group decision making with a linguistic assessment approach. Information Sciences, 85(4), 223–239. https://doi.org/10.1016/0020-0255(95)00025-k

19.

Herrera

Martínez

(2000). A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on Fuzzy Systems, 8(6), 746–752. https://doi.org/10.1109/91.890332

20.

Herrera-Viedma

Cabrerizo

F. J.

Kacprzyk

Pedrycz

(2014). A review of soft consensus models in a fuzzy environment. Information Fusion, 17, 4–13. https://doi.org/10.1016/j.inffus.2013.04.002

21.

Herrera-Viedma

Palomares

C. C.

Cabrerizo

F. J.

Dong

Chiclana

Herrera

(2020). Revisiting fuzzy and linguistic decision making: Scenarios and challenges for making wiser decisions in a better way. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(1), 191–208. https://doi.org/10.1109/tsmc.2020.3043016

22.

Cao

Fujita

Liang

(2023). An online reviews-driven large-scale group decision making approach for evaluating user satisfaction of sharing accommodation. Expert Systems with Applications, 213(Part A), 118875. https://doi.org/10.1016/j.eswa.2022.118875

23.

Jin

Liu

Zhou

Martínez

(2021). Consensus-based linguistic distribution large-scale group decision making using statistical inference and regret theory. Group Decision and Negotiation, 30(4), 813–845. https://doi.org/10.1007/s10726-021-09736-z

24.

Kacprzyk

Fedrizzi

(1988). A ‘soft’measure of consensus in the setting of partial (fuzzy) preferences. European Journal of Operational Research, 34(3), 316–325. https://doi.org/10.1016/0377-2217(88)90152-x

25.

Kannan

Mina

Nosrati-Abarghooee

Khosrojerdi

(2020). Sustainable circular supplier selection: A novel hybrid approach. The Science of the Total Environment, 722, 137936. https://doi.org/10.1016/j.scitotenv.2020.137936

26.

C. C.

Gao

Dong

(2021). Managing ignorance elements and personalized individual semantics under incomplete linguistic distribution context in group decision making. Group Decision and Negotiation, 30(1), 97–118. https://doi.org/10.1007/s10726-020-09708-9

27.

Zhang

Wei

(2023). Consensus reaching in social network group decision making: A stochastic multicriteria acceptability analysis-based approach. Information Fusion, 97, 101825. https://doi.org/10.1016/j.inffus.2023.101825

28.

Kou

Peng

(2022). Consensus reaching process in large-scale group decision making based on bounded confidence and social network. European Journal of Operational Research, 303(2), 790–802. https://doi.org/10.1016/j.ejor.2022.03.040

29.

Liang

(2023). A group-based FMEA approach with dynamic heterogeneous social network consensus reaching model for uncertain reliability assessment. Journal of the Operational Research Society, 74(1), 33–47. https://doi.org/10.1080/01605682.2021.2020694

30.

Liu

Jiao

Shen

Chen

(2022). A dynamic hybrid trust network-based dual-path feedback consensus model for multi-attribute group decision-making in intuitionistic fuzzy environment. Information Fusion, 80, 266–281. https://doi.org/10.1016/j.inffus.2021.09.020

31.

Liu

Zhang

Wang

(2022). A clustering-and maximum consensus-based model for social network large-scale group decision making with linguistic distribution. Information Sciences, 602, 269–297. https://doi.org/10.1016/j.ins.2022.04.038

32.

Liu

Zhang

Herrera-Viedma

(2023). A consensus model for group decision-making with personalized individual self-confidence and trust semantics: A perspective on dynamic social network interactions. Information Sciences, 627, 147–168. https://doi.org/10.1016/j.ins.2023.01.087

33.

Zhu

Ponnambalam

Chen

Zhang

(2019). Group decision-making with linguistic cognition from a reliability perspective. Cognitive Computation, 11(2), 172–192. https://doi.org/10.1007/s12559-019-9622-0

34.

Mabrouk

(2021). Green supplier selection using fuzzy Delphi method for developing sustainable supply chain. Decision Science Letters, 10(1), 63–70. https://doi.org/10.5267/j.dsl.2020.10.003

35.

Rodriguez

R. M.

Martinez

Herrera

(2011). Hesitant fuzzy linguistic term sets for decision making. IEEE Transactions on Fuzzy Systems, 20(1), 109–119. https://doi.org/10.1109/tfuzz.2011.2170076

36.

Sun

Chiclana

Liu

Liang

Herrera-Viedma

(2023). A weight penalty mechanism for non-cooperative behavior in large-scale group decision making with unbalanced linguistic term sets. IEEE Transactions on Fuzzy Systems, 31(10), 3507–3521. https://doi.org/10.1109/tfuzz.2023.3260820

37.

Taghavi

Eslami

Herrera-Viedma

Ureña

(2020). Trust based group decision making in environments with extreme uncertainty. Knowledge-Based Systems, 191, 105168. https://doi.org/10.1016/j.knosys.2019.105168

38.

Tan

Zhu

Palomares

Liu

(2022). On consensus reaching process based on social network analysis in uncertain linguistic group decision making: Exploring limited trust propagation and preference modification attitudes. Information Fusion, 78, 180–198. https://doi.org/10.1016/j.inffus.2021.09.006

39.

Tian

Z. P.

Nie

R. X.

Wang

J. Q.

(2019). Social network analysis-based consensus-supporting framework for large-scale group decision-making with incomplete interval type-2 fuzzy information. Information Sciences, 502, 446–471. https://doi.org/10.1016/j.ins.2019.06.053

40.

Tushar

Z. N.

Bari

A. B. M. M.

Khan

M. A.

(2022). Circular supplier selection in the construction industry: A sustainability perspective for the emerging economies. Sustainable Manufacturing and Service Economics, 1, 100005. https://doi.org/10.1016/j.smse.2022.100005

41.

Wang

Zhang

Dong

(2022). A dynamic social network-driven consensus reaching model. Procedia Computer Science, 199, 1044–1051. https://doi.org/10.1016/j.procs.2022.01.132

42.

Wang

J. H.

Hao

(2006). A new version of 2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on Fuzzy Systems, 14(3), 435–445. https://doi.org/10.1109/tfuzz.2006.876337

43.

Wang

Y. M.

Jia

Song

H. H.

Martínez

(2023). Improving consistency based on regret theory: A multi-attribute group decision making method with linguistic distribution assessments. Expert Systems with Applications, 221, 119748. https://doi.org/10.1016/j.eswa.2023.119748

44.

Cao

Chiclana

Dong

Herrera-Viedma

(2020). An optimal feedback model to prevent manipulation behavior in consensus under social network group decision making. IEEE Transactions on Fuzzy Systems, 29(7), 1750–1763. https://doi.org/10.1109/tfuzz.2020.2985331

45.

Chiclana

(2014). A social network analysis trust–consensus based approach to group decision-making problems with interval-valued fuzzy reciprocal preference relations. Knowledge-Based Systems, 59, 97–107. https://doi.org/10.1016/j.knosys.2014.01.017

46.

Chiclana

Fujita

Herrera-Viedma

(2017). A visual interaction consensus model for social network group decision making with trust propagation. Knowledge-Based Systems, 122, 39–50. https://doi.org/10.1016/j.knosys.2017.01.031

47.

Chiclana

Herrera-Viedma

(2015). Trust based consensus model for social network in an incomplete linguistic information context. Applied Soft Computing, 35, 827–839. https://doi.org/10.1016/j.asoc.2015.02.023

48.

Zhao

Sun

Fujita

(2021). A maximum self-esteem degree based feedback mechanism for group consensus reaching with the distributed linguistic trust propagation in social network. Information Fusion, 67, 80–93. https://doi.org/10.1016/j.inffus.2020.10.010

49.

Zhang

Liu

Cao

(2019). A two-stage social trust network partition model for large-scale group decision-making problems. Knowledge-Based Systems, 163, 632–643. https://doi.org/10.1016/j.knosys.2018.09.024

50.

Zhang

Kou

Zhang

Chao

C. C.

Dong

Herrera

(2021). Distributed linguistic representations in decision making: Taxonomy, key elements and applications, and challenges in data science and explainable artificial intelligence. Information Fusion, 65, 165–178. https://doi.org/10.1016/j.inffus.2020.08.018

51.

Zhang

Chen

(2020). Consensus-based non-cooperative behaviors management in large-group emergency decision-making considering experts’ trust relations and preference risks. Knowledge-Based Systems, 190, 105108. https://doi.org/10.1016/j.knosys.2019.105108

52.

(2005). Deviation measures of linguistic preference relations in group decision making. Omega, 33(3), 249–254. https://doi.org/10.1016/j.omega.2004.04.008

53.

Yager

R. R.

(1988). On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on Systems, Man, and Cybernetics, 18(1), 183–190. https://doi.org/10.1109/21.87068

54.

Yao

(2019). A new distance-based consensus reaching model for multi-attribute group decision-making with linguistic distribution assessments. International Journal of Computational Intelligence Systems, 12(1), 395–409. https://doi.org/10.2991/ijcis.2018.125905656

55.

Zadeh

L. A.

(1983). A computational approach to fuzzy quantifiers in natural languages. Computers and Mathematics with Applications, 9(1), 149–184. https://doi.org/10.1016/0898-1221(83)90013-5

56.

Zhang

Dong

(2014). Consistency and consensus measures for linguistic preference relations based on distribution assessments. Information Fusion, 17, 46–55. https://doi.org/10.1016/j.inffus.2012.01.006

57.

Zhang

Palomares

Dong

Wang

(2018). Managing non-cooperative behaviors in consensus-based multiple attribute group decision making: An approach based on social network analysis. Knowledge-Based Systems, 162, 29–45. https://doi.org/10.1016/j.knosys.2018.06.008

58.

Zhang

Wang

Dong

Chiclana

Herrera-Viedma

(2021). Social trust driven consensus reaching model with a minimum adjustment feedback mechanism considering assessments-modifications willingness. IEEE Transactions on Fuzzy Systems, 30(6), 2019–2031. https://doi.org/10.1109/tfuzz.2021.3073251

59.

Zhang

Liu

(2023). Two-rank multi-attribute group decision-making with linguistic distribution assessments: An optimization-based integrated approach. Engineering Applications of Artificial Intelligence, 121, 106170. https://doi.org/10.1016/j.engappai.2023.106170

60.

Zhang

Chen

Gao

Dong

Pedryczc

(2022). Consensus reaching with trust evolution in social network group decision making. Expert Systems with Applications, 188, 116022. https://doi.org/10.1016/j.eswa.2021.116022

61.

Zhang

Gao

(2020). Consensus reaching for social network group decision making by considering leadership and bounded confidence. Knowledge-Based Systems, 204, 106240. https://doi.org/10.1016/j.knosys.2020.106240

62.

Zou

W. C.

Wan

S. P.

Dong

J. Y.

Martínez

(2023). A new social network driven consensus reaching process for multi-criteria group decision making with probabilistic linguistic information. Information Sciences, 632, 467–502. https://doi.org/10.1016/j.ins.2023.01.088