An adaptive consensus method based on feedback mechanism and social interaction in social network group decision making

C Shang, R Zhang, X Zhu, Y Liu - Information Sciences, 2023 - Elsevier
C Shang, R Zhang, X Zhu, Y Liu
Information Sciences, 2023Elsevier
Many consensus models in social network group decision making (SNGDM) have been
reported to obtain a collective solution despite the initial opinions of decision makers (DMs)
may be different. However, these models ignore the obstinacy of DMs to their initial opinions,
which violates the sociological research results. Aiming at the consensus reaching of
SNGDM where DMs are stubborn to their initial opinions, this paper proposes a novel
consensus model based on the passive adjustment based on feedback mechanism (PA-FM) …
Abstract
Many consensus models in social network group decision making (SNGDM) have been reported to obtain a collective solution despite the initial opinions of decision makers (DMs) may be different. However, these models ignore the obstinacy of DMs to their initial opinions, which violates the sociological research results. Aiming at the consensus reaching of SNGDM where DMs are stubborn to their initial opinions, this paper proposes a novel consensus model based on the passive adjustment based on feedback mechanism (PA-FM) and active adjustment based on social interaction (AA-SI), which adaptively adopts AA-SI or PA-FM in each round according to the opinion distribution of DMs. Specially, the proposed consensus model assimilates the advantage of PA-FM and AA-SI, where adjustment intensity of DMs affects the consensus level of PA-FM, and stubbornness degree of DMs affects that of AA-SI. To elucidate the performance and advantages of the proposed consensus model, a hypothetical application and three simulation analyses are constructed. The results show that (1) In the decision-making group that is stubborn to the initial opinions, social interaction has a beneficial or harmful effect on the consensus reaching, depending on the stubbornness degree and opinion distribution of DMs; and (2) Given any stubbornness degree and adjustment intensity, the proposed consensus model outperforms existing consensus methods in consensus efficiency and success rate.
Elsevier
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