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.

中文翻译：

---

基于PL-Wasserstein距离的集成共识改进策略及其在网络舆情突发事件评价中的应用

在现实生活中，可能同时在某个地方爆发多个网络舆论紧急事件。因此，有必要邀请多个领域的应急决策专家对网络舆情的全面危机进行及时评估，然后利用有限的应急资源来优先应对危机最大的一方。由于网络舆情紧急事件的复杂性和专家的认识有限，评估网络舆情紧急事件的大多数决策问题都是高度不确定的。同样，在选择最高危机之前，最好让专家们在他们的评估或意见之间达成高度共识。为了解决这些问题，本文提出了一种新的适应性共识达成模型，用于具有概率语言决策矩阵（PLDM）的多属性群决策（MAGDM）。首先，为了准确有效地量化任何两个概率语言术语集（PLTS）之间的差异，我们基于Wasserstein度量定义了一种新颖的PLTS之间的距离度量。然后，通过将定义的基于PLTSs的Wasserstein（PL-Wasserstein）距离度量集成到经典CCSD方法中，我们构建了用于客观确定属性权重的优化模型。随后，我们分别开发了个人累积共识贡献（ICCC）度量和小组共识度量，其后提出了一种综合共识改进策略，其中考虑了权重更新（即 专家和属性的动态权重）和评估调整。最后，通过对网络舆情紧急情况的真实评估，说明了该方法的可行性和适用性。通过与现有的概率语言MAGDM方法进行比较，该方法在准确测量信息差异和全面提高共识效率方面具有优势。