The maximum effort consensus model considering the emotions of decision-makers in the social network environment
DOI:
https://doi.org/10.65514/xt3dt990Keywords:
Social network; Personalized semantics; Decision-maker sentiment; Degree of effortAbstract
In language social network group decision-making, social network analysis often helps determine the importance weights of decision-makers. In fact, decision-maker emotions can have an impact on the spread of trust among decision-makers. For example, positive emotions can enhance trust, while negative emotions can suppress it. Therefore, this paper constructs a social network trust propagation mechanism that takes into account the emotions of decision-makers. Secondly, in the process of reaching consensus, managers often need to modify their opinions, and the probability of decision-makers' adjustment based on emotion is defined simultaneously. Also, the willingness and attitude of decision-makers to modify for a better consensus, that is, the degree of effort, can lead to different consensus outcomes. In the case of limited cost budgets, we propose a maximum effort consensus model driven by the maximization of decision-makers' efforts to advise individuals. Finally, the model approach proposed in this paper was applied to the case of brand selection of green wall insulation materials and compared with the consensus results guided by the identity-optimization rule method to verify the rationality and superiority of the method proposed in this paper.
References
1.Ben Arieh D, Esaton T. (2007) Multi-criteria group consensus under linear cost opinion elasticity. Decision Support Systems, 43: 713-721.
2.Chao XR, Kou G, Peng Y et al (2021) Large-scale group decision-making with non-cooperative behaviors and heterogeneous preferences: An application in financial inclusion. Eur J Oper Res 288(1):271–293.
3.Ding ZG, Chen X, Dong YC et al (2022) Consensus convergence speed in social network degroot model: The effects of the agents with high self-confidence levels. IEEE Trans Computation Soc Syst, 99:1–11
4.Dong Y C, Xu Y F, Li H Y, Feng B. (2010) The OWA-based consensus operator under linguistic representation models using position indexes. Euro J of Oper Res, 203: 455-463.
5.Dong Y, Ran Q, Chao X et al (2023) Personalized individual semantics learning to support a large-scale linguistic consensus process. ACM Trans Internet Technol 23(2):1–27
6.Dong YC. The study of group decision under language environment. Xi’an Jiaotong University Press, Xi’an, 2008.
7.Herrera F, Herrera-Viedma EE, Martínez-López L (2008) A fuzzy linguistic methodology to deal with unbalanced linguistic term sets. IEEE Trans Fuzzy Syst 16(2):354–370.
8.Herrera Viedma E, Cabrerizo F J, Kacprzyk J, Pedrycz W. 2014. A review of soft consensus models in a fuzzy environment. Information Fusion, 17: 4-13.
9.Hochbaum D S, Levin A. (2006) Methodologies and algorithms for group-rankings decision. Man Sci, 52: 1394-1408.
10.Jing FY, Chao XR (2021) Fairness concern: An equilibrium mechanism for consensus-reaching game in group decision-making. Inf Fusion 72:147–160
11.Kacprzyk J, Fedrizzi M. A ‘soft’ measure of consensus in the setting of partial (fuzzy) preferences. Euro J of Oper Res, 1988, 34: 316-325.
12.Li Y, Zhang H J, Dong Y C. (2017) The interactive consensus reaching process with the minimum and uncertain cost in group decision making. App Soft Comput, 60: 202-212.
13.Li Z, Zhang Z, Yu W (2024) Consensus reaching for ordinal classification-based group decision making with heterogeneous preference information. J Oper Res Soc 75(2):224–245.
14.Liao HC, Kuang LS, Liu YX et al (2021) Non-cooperative behavior management in group decision making by a conflict resolution process and its implementation for pharmaceutical supplier selection. Inf Sci, 567:131–145
15.Liu BS, Shen YH, Chen XH et al (2014) A partial binary tree dea-da cyclic classification model for decision makers in complex multi-attribute large-group interval-valued intuitionistic fuzzy decision-making problems. Inf Fusion 18:119–130.
16.Liu WQ, Dong YC, Chiclana F et al (2017) Group decision-making based on heterogeneous preference relations with self-confidence. Fuzzy Optim Decis Making,16(4):429–447
17.Mandal P, Samanta S, Pal M (2021) Multiplicative consistency analysis of linguistic preference relation with self-confidence level and self-doubting level and its application in a group decision making. Int J Intell Syst 36(10):5389–5418.
18.Quesada FJ, Palomares I, Martinez L (2015) Managing experts behavior in large-scale consensus reaching processes with uni-norm aggregation operators. Appl Soft Comput 35:873–887.
19.Ran Q, Chao X, Cabrerizo FJ et al (2022) Managing overconfidence behaviors from heterogeneous preference relations in linguistic group decision making. IEEE Trans Fuzzy Syst, 31(7):2435–2449.
20.Zha QB, Dong YC, Zhang HJ et al (2021) A personalized feedback mechanism based on bounded confidence learning to support consensus reaching in group decision making. IEEE Trans Syst Man Cybern Syst, 51(6):3900–3910
21.Zhang G Q, Dong Y C, Xu Y F, et.al. (2011) Minimum-cost consensus models under aggregation operators. IEEE Trans on Syst, Man, and Cybe Part A: Syst and Hum, 41: 1253-1261.
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