المؤلفون: Qu, Shaojian, Xu, Yuan, Wu, Zhong, Xu, Zeshui, Ji, Ying, Qu, Deqiang, Han, Yefan

المصدر: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ); 2021, Vol. 46 Issue 2, p1771-1785, 15p

مصطلحات موضوعية: GAUSSIAN distribution, SOCIAL networks, FUZZY numbers, DECISION making, MATHEMATICAL models

مستخلص: A novel three-stage multi-criteria decision-making (MCDM) method considering the interval-valued best–worst method (BWM) and social networks including the following three main modules is put forward: (1) the weights of experts; (2) the weights of criteria; (3) the weighted sum method (WSM). Firstly, a new framework for computing the weights of experts in social networks is construct. Let the values of trust and distrust be interval values rather than fixed values, and then construct a complete trust preference matrix through trust propagation. Subsequently, trust score obtained by synthesizing the trust preference matrix is used to assign the weights of experts. Secondly, an interval-valued BWM model with normal distribution is developed to compute the weights of criteria. The proposed BWM model innovatively uses interval fuzzy numbers with normal distribution to represent the preference degree of experts instead of the fixed values in the basic BWM model. Thirdly, considering the WSM, this paper constructs a novel three-stage MCDM method considering the proposed novel BWM model and social networks. The proposed MCDM model innovatively aims at the situation where both the weights of experts and criteria are not known, and provides mathematical models for solving them respectively. Finally, a case is used to prove the utility and availability of the novel MCDM method. The results show that the novel MCDM method can not only reduce the subjectivity in decision making, but also can effectively solve the MCDM problems. [ABSTRACT FROM AUTHOR]

: Copyright of Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Ji, Ying, Li, Ping, Wu, Zhong, Qu, Deqiang

المصدر: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ); 2021, Vol. 46 Issue 2, p1677-1690, 14p

مصطلحات موضوعية: GROUP decision making, SOCIAL networks, SOCIAL dynamics

مستخلص: This paper studies the minimum consensus cost strategy in the whole group decision-making (GDM) process under the condition of experts' opinion and weight evolving over time. Based on the traditional minimum cost consensus model (MCCM), the updated algorithm of opinion dynamics is introduced to obtain the expert's opinion value and weight, which is used for the optimal strategy of group consensus decision making. In the existing MCCM, the consensus mechanism is established through a simple optimization model, in which each expert's opinion value and weight coefficient are in a static state and do not change dynamically with time. However, in actual GDM, the expert's opinion and their own importance (weight coefficient) are random and easily affected by the decision-making environment. Therefore, this paper proposes three novel kinds of MCCMs based on opinion dynamics in social networks. Numerical results show that this method can reach a higher degree of consensus in a reasonable time compared with traditional consensus model. In addition, some comparative experimental data also show that compared with some static consensus, the optimal decision strategy proposed can greatly reduce the cost. In particular, the MCCM model with confidence parameter reduces the consensus cost to the lowest and obtains the highest degree of consensus. [ABSTRACT FROM AUTHOR]

: Copyright of Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

المؤلفون: Ma, Yifan¹ (AUTHOR), Ji, Ying¹ (AUTHOR) jiying_1981@126.com, Qu, Deqiang² (AUTHOR), Zhang, Xuyuan³ (AUTHOR), Wang, Lun¹ (AUTHOR)

المصدر: Information Fusion. Aug2024, Vol. 108, pN.PAG-N.PAG. 1p.

مصطلحات موضوعية: *GROUP decision making, *EXTERNALITIES, *SOCIAL networks, *SOCIAL adjustment, *AGRICULTURAL insurance, *AGRICULTURAL technology

مصطلحات جغرافية: CHINA

مستخلص: • Explain the economic value of the quadratic cost of MECM in terms of marginal cost and elasticity. • Adopt an opinion modification mechanism based on social network. • The MECMs with uncertain adjustment costs are developed under three uncertain scenarios. • The proposed models are applied to the agricultural insurance premiums subsidy policymaking in China. In the realm of group decision making (GDM), the maximum expert consensus model (MECM) emerges as a potent tool for consensus optimization. The complexity of decision-making environment leads to the uncertainty of adjustment costs and the intricate social relationships between decision makers (DMs). Therefore, this paper aims to develop the MECM that integrates both social relationships and uncertain adjustment costs to support social network group decision making (SNGDM) problems. Specifically, we propose a MECM with quadratic cost that can more accurately reflect DMs' sensitivity for opinion adjustment. Additionally, we adopt an opinion modification mechanism based on the information obtained from the social network. The paper also develops the robust MECM (RMECM) to handle the uncertainty of the unit adjustment cost under three uncertain scenarios. Finally, the efficiency of the proposed models is demonstrated by applying them to the agricultural insurance premiums subsidy policymaking in China, further substantiated by sensitivity analysis and comparative analysis showcasing their robust performance. [ABSTRACT FROM AUTHOR]