A review of soft consensus models in a fuzzy environment

In the consensus reaching processes developed in group decision making problems we need to measure the closeness among experts’ opinions in order to obtain a consensus degree. As it is known, to achieve a full and unanimous consensus is often not reachable in practice. An alternative approach is to use softer consensus measures, which reflect better all possible partial agreements, guiding the consensus process until high agreement is achieved among individuals. Consensus models based on soft consensus measures have been widely used because these measures represent better the human perception of the essence of consensus. This paper presents an overview of consensus models based on soft consensus measures, showing the pioneering and prominent papers, the main existing approaches and the new trends and challenges.     

基于DPoS共识机制的区块链社区演化的可视分析方法

DPoS(股权授权证明)是当前主流区块链共识机制之一,独特的节点竞选机制使其形成不断演化的区块链社区.对区块链社区演化模式进行分析可以发现共识机制的潜在风险,具有十分重要的研究意义.针对DPoS共识机制区块链数据,提出了一种新颖的共识机制效能组合分析策略,面向区块链社区演化模式,提出了一种多角度探索的可视分析方法.首先...     

A consensus model for hesitant fuzzy preference relations and its application in water allocation management

This paper investigates a consensus model for hesitant fuzzy preference relations (HFPRs). First, we present a revised definition of HFPRs, in which the values are not ordered for the hesitant fuzzy element. Second, we propose an additive consistency based estimation measure to normalize the HFPRs, based on which, a consensus model is developed. Here, two feedback mechanisms are proposed, namely, interactive mechanism and automatic mechanism, to obtain a solution with desired consistency and consensus levels. In the interactive mechanism, the experts are suggested to give their new preference values in a specific range. If the experts are unwilling to offer their updated preferences, the automatic mechanism could be adopted to carry out the consensus process. Induced ordered weighted averaging (IOWA) operator is used to aggregate the individual HFPRs into a collective one. A score HFPR is proposed for collective HFPR, and then the quantifier-guided dominance degrees of alternatives by using an OWA operator are obtained to rank the alternatives. Finally, both a case of study for water allocation management in Jiangxi Province of China and a comparison with the existing approaches are carried out to show the advantages of the proposed method.     

Multi-leader coordination algorithm for networks with switching topology and quantized information

This paper analyzes the stability and convergence properties of a proportional–integral protocol for coordination of a network of agents with dynamic information flow and quantized information exchange. In the setup adopted, each agent is only required to exchange its coordination state with its neighboring agents, and the desired reference rate is only available to a group of leaders. We show that the integral term of the protocol allows the agents to learn the reference rate, rather than have it available a priori, and also provides disturbance rejection capabilities. The paper addresses the case where the graph that captures the underlying network topology is not connected during some interval of time or even fails to be connected at all times.     

多尺度空间特征提取的脊柱图像拼接算法

针对脊柱图像视野有限,提出一种基于优化MSFE的脊柱图像自动拼接算法。设计出一种基于尺度因子变化的高斯卷积模板尺寸自适应调整以及双向配对办法;相似性度量采用城市距离;利用RANSAC算法去除错配,从而确定待拼接图像之间的变换参数,最后利用加权平均对图像融合。对实际取得的多幅脊柱图像拼接结果表明该算法具有很好的实时性和鲁棒性。     

From Constrained to Unconstrained Maximum Agreement Subtree in Linear Time

We propose and study the Maximum Constrained Agreement Subtree (MCAST) problem, which is a variant of the classical Maximum Agreement Subtree (MAST) problem. Our problem allows users to apply their domain knowledge to control the construction of the agreement subtrees in order to get better results. We show that the MCAST problem can be reduced to the MAST problem in linear time and thus we have algorithms for MCAST with running times matching the fastest known algorithms for MAST. A preliminary version of this paper appears in the Proceedings of the Fifth Workshop on Algorithms in Bioinformatics (WABI 2005). Research of H.F. Ting is supported in part by Hong Kong RGC Grant HKU-7172/06E.     

A priori ordering protocols to support consensus-building in multiple stakeholder contexts

In a complex social, political, economic, technological, and/or environmental context, corporate, military, government, and other organizations are often faced with collective decision-making situations. The rationale for group decision exercises is that the judgment of many will usually prove superior to the judgment of one. However, it has been shown that collective decision exercises are often highly dependent on matters of perspective, values and opinion, all of which - being essentially subjective in nature - are beyond the reach of existing formal decision technology. Furthermore, it can be expected that many collective decision exercises - particularly those of strategic import - will not lend themselves to the quantitative analysis instruments that have long dominated the management and decision science repertoire. This does not, however, mean that they must remain entirely and forever outside the bounds of scientific rationality. The ordering protocols which are currently available do not have enough technical mechanics to be relied upon to bring us to any satisfying resolution of a priori disputation. Hence, we propose an a priori ordering reference model that might support consensus-building in a multiple stakeholders context. In order to detect whether the subjective arguments are products of proper reasons or merely instances of raw rhetoric and to suggest how any logical or syntactical flaws might best be repaired, we propose logical ordering facilities and protocols for integrating procedural and instrumental provisions for a group-decision process with two lines of technical innovation: the superimpositional ordering function and the logical ordering support facilities.     

Obtaining the consensus and inconsistency among a set of assertions on a qualitative attribute

It is well understood how to compute the average or centroid of a set of numeric values, as well as their variance. In this way we handle inconsistent measurements of the same property. We wish to solve the analogous problem on qualitative data: How to compute the “average” or consensus of a set of affirmations on a non-numeric fact, as reported for instance by different Web sites? What is the most likely truth among a set of inconsistent assertions about the same attribute?Given a set (a bag, in fact) of statements about a qualitative feature, this paper provides a method, based in the theory of confusion, to assess the most plausible value or “consensus” value. It is the most likely value to be true, given the information available. We also compute the inconsistency of the bag, which measures how far apart the testimonies in the bag are. All observers are equally credible, so differences arise from perception errors, due to the limited accuracy of the individual findings (the limited information extracted by the examination method from the observed reality).Our approach differs from classical logic, which considers a set of assertions to be either consistent (True, or 1) or inconsistent (False, or 0), and it does not use Fuzzy Logic.