Abstract
In real life, multiple network public opinion emergencies may break out in a certain place at the same time. So, it is necessary to invite emergency decision experts in multiple fields for timely evaluating the comprehensive crisis of the online public opinion, and then limited emergency resources can be utilized to give priority to respond to the one with the highest crisis. Due to the complexity of network public opinion emergencies and the limited cognition of experts, most of the decision problems for evaluating the network public opinion emergencies are highly uncertain. Also, prior to the selection of the highest crisis, it is preferable that experts reach a high degree of consensus among their assessments or opinions. To address such problems, this paper presents a novel adaptive consensus reaching model for multiattribute group decision making (MAGDM) with probabilistic linguistic decision matrices (PLDMs). First, to quantify the difference between any two probabilistic linguistic term sets (PLTSs) accurately and efficiently, we define a novel distance measure between PLTSs based on the Wasserstein metric. Then, by integrating the defined PLTSs-based Wasserstein (PL-Wasserstein) distance measure into the classical CCSD method, we construct an optimization model for objectively determining attribute weights. Subsequently, we develop the individual cumulative consensus contribution (ICCC) measure and the group consensus measure, respectively, following which is to present an integrated consensus improving strategy that considers both weight-updating (i.e., dynamic weights of experts and attributes) and assessment-adjusting. Finally, the feasibility and the applicability of the proposed approach are illustrated via a real evaluation of network public opinion emergencies. Through comparing with existing probabilistic linguistic MAGDM approaches, the proposed approach offers the advantages in terms of the accurate measurement of information difference and the integrated improvement of consensus efficiency.
Highlights
According to China Internet Network Information Center (CNNIC) 43th statistical report on Internet development in Beijing, it follows that as of the end of December, 2018, Chinese netizens reached 829 million, the Internet penetration rate reached 59.6%, and the usage rate of WeChat friends circle, QQ space, and Weibo as social media reached 83.4%, 58.8%, and 42.3%, respectively, among which Sina Weibo monthly active users have reached462 million
This paper aims to propose a novel consensus reaching model for multiattribute group decision making (MAGDM) problems with probabilistic linguistic assessment information. e main innovations and contributions of this paper are as follows: (1) We propose a probabilistic linguistic term sets (PLTSs)-based Wasserstein distance measure and show that the novel distance measure has been improved in both rationality and efficiency
Complexity e main problem to be solved in this paper is to establish a consensus reaching model for probabilistic linguistic MAGDM problems with probabilistic linguistic decision matrices (PLDMs). e following questions arise when investigating this research problem. (a) How do we quantify the difference between any two PLTSs accurately and efficiently after recognizing the shortcomings of existing measure methods? (b) How do we fully mine potential attribute weight information to serve the consensual sorting or selection of alternatives after collecting the PLDMs information? (c) How do we design a fast, valid, and adaptive consensus reaching process (CRP) to reach a certain degree of consensus in the group with PLDMs?
Summary
According to China Internet Network Information Center (CNNIC) 43th statistical report on Internet development in Beijing (released on the 28th of February, 2019), it follows that as of the end of December, 2018, Chinese netizens reached 829 million, the Internet penetration rate reached 59.6%, and the usage rate of WeChat friends circle, QQ space, and Weibo as social media reached 83.4%, 58.8%, and 42.3%, respectively, among which Sina Weibo monthly active users have reached. Two novel distance measures (i.e., a generalized relative distance and an extended Hausdorff distance) for PLTSs were defined by Wang et al [38] Considering that both Pang et al.’s distance measures [21] and Zhang et al.’s distance measures [39] fail to represent the differences between probabilities and the differences between linguistic terms, simultaneously, Wu et al [40] established a new distance measure of PLTSs after introducing an adjustment method to derive the same probability set of pairwise PLTSs. Further, Wu and Liao’s distance measure of the PLTSs has been improved by incorporating the unbalanced scenario [35], which is employed to set up the correlation coefficient between two PLTSs and to measure the consensus degree between the individuals and collective opinions.
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