属性值为区间数的多属性决策对象排序研究

多属性决策问题的实质利用已有决策信息,通过一定方式对备选方案进行排序和择优。针对属性值为区间数的多属性决策对象排序问题,首先,提出决策对象优势关系这一概念,再次,得出决策对象的优势关系与其属性值两端点的实数值之和存在等价关系;最后,利用属性值为区间数的多属性决策实例对该算法进行了实例分析,并利用优势关系对决策对象进行排序并择优。     

Probabilistic Inversion in Priority Setting of Emerging Zoonoses

This article presents methodology of applying probabilistic inversion in combination with expert judgment in priority setting problem. Experts rank scenarios according to severity. A linear multi‐criteria analysis model underlying the expert preferences is posited. Using probabilistic inversion, a distribution over attribute weights is found that optimally reproduces the expert rankings. This model is validated in three ways. First, consistency of expert rankings is checked, second, a complete model fitted using all expert data is found to adequately reproduce observed expert rankings, and third, the model is fitted to subsets of the expert data and used to predict rankings in out‐of‐sample expert data.     

Understanding Cumulative Risk Perception from Judgments and Choices: An Application to Flood Risks

Catastrophic events, such as floods, earthquakes, hurricanes, and tsunamis, are rare, yet the cumulative risk of each event occurring at least once over an extended time period can be substantial. In this work, we assess the perception of cumulative flood risks, how those perceptions affect the choice of insurance, and whether perceptions and choices are influenced by cumulative risk information. We find that participants' cumulative risk judgments are well represented by a bimodal distribution, with a group that severely underestimates the risk and a group that moderately overestimates it. Individuals who underestimate cumulative risks make more risk‐seeking choices compared to those who overestimate cumulative risks. Providing explicit cumulative risk information for relevant time periods, as opposed to annual probabilities, is an inexpensive and effective way to improve both the perception of cumulative risk and the choices people make to protect against that risk.     

基于模糊判断矩阵信息确定专家权重的方法

本文对群组模糊判断矩阵集结过程中确定专家权重的问题进行了研究,建立了模糊判断矩阵的特征矩阵和求解群集结矩阵的最优化模型,通过矩阵之间距离度量判断信息自身逻辑一致性程度和群体相容性程度,给出一种基于专家判断信息的可信度计算其后验权重的方法,最后用算例予以说明.     

区间数互补判断矩阵的一致性及其排序研究

由于目前国内外文献对区间数互补判断矩阵的性质研究较少,从而使得对排序方法的相关研究缺乏理论依据.针对这些缺陷,本文研究了区间数互补判断矩阵的性质及其排序方法问题.根据区间数互补判断矩阵的定义,给出了区间数互补判断矩阵的一致性、严格强传递性与弱传递性等定义,并研究了一致性判断矩阵的性质,并说明这些性质更符合人们的思维特征.在一致性性质的基础上建立了区间数互补判断矩阵排序的非线性规划模型,算例分析表明该方法是有效可行的.     

Performance measurement and classification data in DEA: Input-oriented model

This paper presents an improved efficiency measurement tool by modifying the existing data envelopment analysis methodology to permit the incorporation of expert knowledge. A previous paper examined the inclusion of such knowledge within the additive model. This information appeared in the form of a binary classification of a subset of the decision making units under study (e.g. good versus poor performers). In the current paper, we extend this logic to the input-oriented radial projection model. We demonstrate that the inclusion of this and other forms of expert judgment can improve the performance of the DEA tool in the sense that the efficiency scores are more in line with expert/management beliefs.     

Challenges to the Acceptance of Probabilistic Risk Analysis

This paper discusses a number of the key challenges to the acceptance and application of probabilistic risk analysis (PRA). Those challenges include: (a) the extensive reliance on subjective judgment in PRA, requiring the development of guidance for the use of PRA in risk-informed regulation, and possibly the development of robust or reference prior distributions to minimize the reliance on judgment; and (b) the treatment of human performance in PRA, including not only human error per se but also management and organizational factors more broadly. All of these areas are seen as presenting interesting research challenges at the interface between engineering and other disciplines.     

Ranking and estimation of related means in two—way models—a Bayesian approach

A hierarchical Bayesian approach to ranking and selection as well as estimation of related means in two—way models is considered. Using the method of Monte Carlo simulation with importance sampling, we are able to carry out efficiently the three or four dimensional integrations as needed. An example is included to illustrate the methodology.     

Estimation of population mean and variance in flock management: a ranked set sampling approach in a finite population setting

Ranked set sampling is a sampling technique that provides substantial cost efficiency in experiments where a quick, inexpensive ranking procedure is available to rank the units prior to formal, expensive and precise measurements. Although the theoretical properties and relative efficiencies of this approach with respect to simple random sampling have been extensively studied in the literature for the infinite population setting, the use of ranked set sampling methods has not yet been explored widely for finite populations. The purpose of this study is to use sheep population data from the Research Farm at Ataturk University, Erzurum, Turkey, to demonstrate the practical benefits of ranked set sampling procedures relative to the more commonly used simple random sampling estimation of the population mean and variance in a finite population. It is shown that the ranked set sample mean remains unbiased for the population mean as is the case for the infinite population, but the variance estimators are unbiased only with use of the finite population correction factor. Both mean and variance estimators provide substantial improvement over their simple random sample counterparts.     

大学生价值判断能力的培养

多元文化的社会背景容易令当代大学生产生多元价值选择的困惑,高校德育不仅要加强社会主流价值观的教育,更要培养学生的价值判断能力.而要培养大学生价值判断能力,明确价值判断标准是前提,尊重学生的主体地位是关键,价值教育生活情景化、实践化则是有效措施.这是一项系统、长期的工程,只有明确了价值判断的标准,重视学生的主体地位,并有...