基于博弈论集成模型的战时军械物资需求优先级研究

军械物资在未来信息战条件下的供应不可盲目,有必要对其进行优先级的划分,确定优先级权重,而权重的确定直接关系着评判结果的科学性、可靠性,仅通过一种方法来确定各指标的权重显得太过片面。为此,将改进的博弈论集成模型引入军械物资需求优先级评价中,将两种主客观赋权方法有机融合,得到更为科学全面的综合权重,并给出了具体算例。     

回采工作面安全评价研究

当前煤矿安全评价大多以整个煤矿作为评价目标,通过评价能大致了解煤矿的整体安全状况,但影响煤矿安全的灾害种类较多,这类评价针对性不强且无法突出重点。为了解决这一问题,从回采工作面这一角度出发,采用模糊综合评价法,建立安全评价模型对突出矿井回采工作面进行安全评价。运用模糊层次分析(FAHP)和熵权法结合的主客观综合权重法确定各级指标的权重,建立隶属度函数计算相对应的评语集,将各级指标的权重值与相对应的评语集合成计算,得到评价结果。通过九里山煤矿16051回采工作面对该模型进行了实证分析,结果表明该模型评价结果符合现场实际,可用于其他工作面。     

石油资源安全评价中的指标赋权法研究

本文针对国家石油资源安全风险评价这一问题,尝试利用风险主体有规避风险的本能这一原理,引入"风险可控系数"这一概念,构建一种主客观相结合的指标赋权方法模型。通过该模型计算出来的指标权重与其他方法计算得出的结果基本吻合,从而表明该模型具有其可行性。     

基于灰色关联度的城市低碳经济综合评价模型

从社会系统、经济系统、技术系统和环境系统四个方面构建了评价城市低碳经济发展的递阶层次型指标体系,提出了一种基于灰色关联度的城市低碳经济综合评价模型,对每个二级指标类内强关联性指标进行筛选,消除了冗余,减小了评价过程的复杂度。同时将主客观赋权法结合确定各指标的综合权重,提高了评价结果的准确度。以某省公布的各项指标数据进行验证,实例表明了该方法的合理性和有效性。     

基于投影模型的不确定语言型多属性决策方法及应用

研究了不确定语言型多属性决策评价结果与决策者对方案的偏好信息之间存在偏差的问题,提出了基于投影模型的不确定多属性决策方法。该方法通过建立与区间型语言标度对应的术语指标矩阵,及方案综合属性值与决策者主观偏好值之间的投影模型,确定属性的权重,然后运用加权法得到方案的综合属性值,利用已有的可能度矩阵排序公式得到决策方案的排序。最后,通过算例对该方法的实用性和有效性进行了说明。     

供应链整体绩效的模糊综合评价

构建了供应链绩效评价指标体系，运用综合评价和模糊数学的相关理论建立了模糊综合评价模型，并用层次分析法中权的最小平方法来确定各评价指标的权重。最后通过具体案例的计算和分析，说明该评价模型有助于企业有针对性地改进供应链整体绩效。     

天津滨海新区房地产市场影响因素权重研究

要确定某个地区的房地产市场影响因素的权重,则首先要求对该地区房地产市场进行全面评价,但是由于指标较多并且涉及面广,如果只是简单地将各方面的问题和评价进行综合,就难以全面客观评价总体情况。采用计量模型回归分析结果,通过层次分析法建立递阶层次结构,建立滨海新区房地产市场影响因素的评价体系,通过合成层次总权重合理确定各指标权重,最终科学确定滨海新区房地产市场的影响因素权重。     

模糊综合评判在房地产估价中的应用

根据模糊数学的理论和计算方法,提出了一个有关房地产估价的模糊评判多层次模型。其中,指标权重的确定,通过层次分析法进行进一步探索,并指出权重矩阵的协调配合是直接影响对模糊综合评判结果做出科学、合理解释的关键。使评价更为客观有效,并加以实例论证,结果表明该模型合理可行,且在房地产估价方面具有明显的优越性。     

基于信息熵模糊数学的煤矿安全文化综合评估

煤矿安全文化系统是动态的、复杂的、非线性的、模糊的系统,对其进行综合评估具有一定的困难性。文章通过建立评价指标体系,建立确定指标权重的信息熵模型,建立模糊综合评价模型,完成对安全文化系统的综合评估。该方法综合考虑了主、客观的因素,得到的评价结果对于制定针对性的安全管理策略具有较重要的指导意义。     

基于AHP的法学硕士学位论文综合评价方法及应用

针对目前法学硕士学位论文评价方法中存在的准则权重确定片面性、专家决策模糊性和评价指标不确定性问题,提出了基于AHP的法学硕士学位论文综合评价方法。该方法首先将群体决策和模糊决策的AHP优化模型引入法学硕士学位论文的评价中,以确定各个层次的指标权重。然后,通过模糊算子加以集结,得到了定性的法学硕士学位论文综合模糊评价结果,最终基于定性结果得出了论文的定量评价分值。实验表明:该综合评价方法不仅能够对法学硕士学位论文进行有效地评价,而且对得到的评价结果能够进行定性与定量相结合的分析。     

基于组合权重的成长期创业企业价值评估方法

构建了一套成长期创业企业潜在价值评估指标体系,运用层次分析法(AHP)、熵值法分别确定了主观和客观权重,然后通过乘法合成的归一化处理得出组合权重,从而尽可能地消除了权重确定时的主观随意性和局限性。建立了基于欧式贴近度的模糊物元评估模型,经过案例分析证明了该方法的有效性。     

基于信息熵的港口物流能力模糊综合评价

提出了基于信息熵的模糊综合评价模型,这种模型避免了主观权重的不完整性,将人工经验的主观权重和由信息熵确定的客观权重结合起来进行模糊综合评价,更能科学、全面地对港口物流能力进行评价,从而为港口物流能力的提高提供决策依据。     

The pro-cyclicality of risk weights for credit exposures: Driven by the retail segment

This paper examines the pro-cyclicality of implicit risk weights of credit exposures and the potential contribution of accommodative monetary policy using data for the Czech Republic. The empirical results indicate that risk weights behave pro-cyclically under the IRB approach and acyclically under the STA approach. The pro-cyclical behaviour of IRB risk weights is caused primarily by the retail exposures, the strongest effects being in the highest and lowest quantiles of risk weights. The risk weights for retail exposures behave pro-cyclically not only with regard to the business cycle, but also with respect to the financial cycle and house price growth. In addition, monetary policy easing contributes to the pro-cyclicality in higher quantiles of risk weights.     

用模糊层次分析法确定QFD中消费者要求权重

质量功能配置(QualityFunctionDeployment,QFD),是能在产品开发及生产的各个阶段,将顾客要求转化为合理的技术要求的整体概念。在实施QFD的过程中,确定消费者的要求权重是一个关键而不可缺少的步骤。本文将模糊一致矩阵引入层次分析中,通过建立模糊一致矩阵来确定QFD中消费者要求权重。由于模糊一致矩阵更符合人们在决策过程中的心理特性,因此,它能提高确定消费者的要求权重的准确性。最后,用一个实例阐述了这种方法在QFD中确定消费者的要求权重中的应用。     

多目标群体层次决策中确定权重的一种方法

应用几何平均和最小方差等概念，探讨了多目标群体层次决策中考虑决策者水平的权重确定方法，并用实例给予了验证。     

基于模糊组合权的模糊TOPSIS方法在供应商选择中的应用研究

供应商的选择在供应链管理中是很重要环节,供应商选择的成功与否将直接影响到整个供应链的竞争优势,故有必要对供应商选择进行深入研究。本文将已有的模糊组合权方法和模糊TOPSIS方法相结合,提出一种基于模糊组合权的模糊TOPSIS方法,并将其应用于供应商的选择研究中。文章首先总结已有的相关研究结果,给出供应商评价的准则,接着介绍三角模糊数的相关理论,推出模糊组合权及模糊TOPSIS的算法步骤,最后通过一个算例验证方法的可行性。     

Econometric Causality

This paper presents the econometric approach to causal modelling. It is motivated by policy problems. New causal parameters are defined and identified to address specific policy problems. Economists embrace a scientific approach to causality and model the preferences and choices of agents to infer subjective (agent) evaluations as well as objective outcomes. Anticipated and realized subjective and objective outcomes are distinguished. Models for simultaneous causality are developed. The paper contrasts the Neyman–Rubin model of causality with the econometric approach.     

Application of evidential reasoning approach and OWA operator weights in road safety evaluation considering the best and worst practice frontiers

Data envelopment analysis (DEA) has recently become relatively popular with road safety experts. Therefore, various decision-making units (DMUs), such as EU countries, have been assessed in terms of road safety performance (RSP). However, the DEA has been criticized because it evaluates DMUs based only on the concept of self-assessment, and, therefore does not provide a unique ranking for DMUs. Therefore, cross efficiency method (CEM) was developed to overcome this shortcoming. Peer-evaluations in addition to self-evaluation have made the CEM to be recognized as an effective method for ranking DMUs. The traditional CEM is based only on the standard CCR (Charnes, Cooper and Rhodes) model, and it evaluates DMUs according to their position relative to the best practice frontier while neglecting the worst practice frontier. However, the DMUs can also be assessed based on their position relative to the worst practice frontier. In this regard, the present study aims to provide a double-frontier CEM for assessing RSP by taking into account the best and worst frontiers simultaneously. For this purpose, the cross efficiency and cross anti-efficiency matrices are generated.Even though a weighted average method (WAM) is most frequently used for cross efficiency aggregation, the decision maker's (DM) preference structure may not be reflected. For this reason, the present study mainly focuses on the evidential reasoning approach (ERA), as a nonlinear aggregation method, rather than the linear WAM. Equal weights are often used for cross efficiency aggregation; consequently, the effect of the DM's subjective judgments in obtaining the overall efficiency is ignored. In this respect, the minimax entropy approach (MEA) and the maximum disparity approach (MMDA) are applied for determining the ordered weighted averaging (OWA) operator weights for cross efficiency aggregation. The weighted cross efficiencies and cross anti-efficiencies are then aggregated using the ERA. Finally, the proposed method, called DF-CEM-ERA, is used to evaluate the RSP of EU countries as well as Serbian police departments (PDs).     

Portfolio selection in a data-rich environment

We model portfolio weights as a function of latent factors that summarize the information in a large number of economic variables. This approach (hereafter diffusion index approach) offers the opportunity to exploit a much richer information base to improve portfolio selection. We use factor analysis to estimate the space spanned by the factors. This provides consistent estimates for the optimal weights as the number of economic variables and sample size go to infinity. We consider an empirical application to illustrate the practical usefulness of our approach. The results indicate that the diffusion index approach helps to improve the portfolio performance.     

A new perspective on the competitiveness of nations

The capability of firms to survive and to have a competitive advantage in global markets depends on, amongst other things, the efficiency of public institutions, the excellence of educational, health and communications infrastructures, as well as on the political and economic stability of their home country. The measurement of competitiveness and strategy development is thus an important issue for policy-makers. Despite many attempts to provide objectivity in the development of measures of national competitiveness, there are inherently subjective judgments that involve, for example, how data sets are aggregated and importance weights are applied. Generally, either equal weighting is assumed in calculating a final index, or subjective weights are specified. The same problem also occurs in the subjective assignment of countries to different clusters. Developed as such, the value of these type indices may be questioned by users. The aim of this paper is to explore methodological transparency as a viable solution to problems created by existing aggregated indices. For this purpose, a methodology composed of three steps is proposed. To start, a hierarchical clustering analysis is used to assign countries to appropriate clusters. In current methods, country clustering is generally based on GDP. However, we suggest that GDP alone is insufficient for purposes of country clustering. In the proposed methodology, 178 criteria are used for this purpose. Next, relationships between the criteria and classification of the countries are determined using artificial neural networks (ANNs). ANN provides an objective method for determining the attribute/criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, in our third step, the countries of interest are ranked based on weights generated in the previous step. Beyond the ranking of countries, the proposed methodology can also be used to identify those attributes that a given country should focus on in order to improve its position relative to other countries, i.e., to transition from its current cluster to the next higher one.