城市商业银行商贸行业客户信用评级模型

本文针对中小商贸银行客户特点,从定性、定量及特殊因素三个方面建立了城市商业银行商贸行业客户信用评级指标体系,对于银行有效降低贷款风险具有重要的现实意义。     

基于图模型的反事实推理在决策后评估中的应用

决策后评估对衡量决策的效果具有重要作用。本文引入反事实推理方法实现对决策结果的对比后评估。首先提出了带有后评估环节的规范性决策流程,然后阐述了使用因果影响图模型进行反事实推理的步骤,定义了自满意度和相对满意度两个指标,通过反事实推理的结果计算指标的分值,从而实现对决策的评价。最后使用例子展示了计算过程,表明了该方法是可行和有效的。     

纵论关乎企业内部统计质量的决策分析

鉴于当前企业统计工作处于低位运转现象,在比较论证基础上．打破常规思维模式,通过对影响企业内部统计质量的决策因素进行定性分析,提升企业内部统计工作水平。     

决策分析的情景树方法及其应用

本文结合一个实例介绍了贝叶斯决策问题的情景树表示和求解.在情景树中,不需要计算条件概率,仅需要每条路径的联合概率.给出了求解情景树的方法--删除法,并将情景树方法与决策树方法进行了比较分析.     

债券信用评级与信用风险

债券信用评级是信用评级机构根据债券发行公司所处经济环境、行业特征、竞争形势、管理能力以及财务状况等多方面因素,使用一套科学的评级方法和技术评定,反映该债券违约可能性的符号级别,它有效地表示了债券的相对信用风险,为债券投资者提供了重要的投资决策参考.讨论了美国公司债券信用评级与债券信用风险的关系,对比分析了中美信用评级业的发展,以期使国内对信用评级有一个全面的认识,并给我国评级业发展提供参考.     

基于相似样本归并的大样本混合信用评估模型

当前面向大样本设计的信用评估模型,大多没有深入探究大样本的分布特征,只是简单地将传统评估方法应用在大样本上.首先提出了用于描述大样本分布特征的相关属性集、边界向量等若干概念及定义,并证明了其主要性质.之后在两个大样本数据集的基础上,研究了样本在相似性方面的分布特征,最后设计了一种大样本混合信用评估模型——HLSCE模型.HLSCE模型认为在大样本数据集中,样本的同一属性在不同局部区域内,对分类性能的贡献是不同的.具体地,HLSCE模型根据各样本与边界向量的相似性差异,结合生物启发式算法,将样本归并划分为若干子集并分别在其上训练基分类器.实证研究表明,HLSCE模型的分类精度相比于现有的代表性信用评估模型更高,同时也具有更为优越的平衡性与稳定性.     

基于关联的多属性决策分析理论研究综述

传统的多属性决策分析（Multiple Attribute Decision Making,MADM）理论通常都假设属性间是独立的,而现实决策问题中常存在关联,这给决策带来了困难,也是决策分析从理论迈向应用的瓶颈。结合近年来国内外相关文献的研究成果,本文介绍基于关联的MADM（即RMADM,MADM in the presence of relationships）理论的发展情况,分析其研究现状和热点,并在此基础上对RMADM理论未来的发展方向进行一些讨论。     

攀枝花航线运行控制中的风险管理与决策分析

作者以危险识别、风险评估与风险缓解为脉络，通过对攀枝花航线运行案例进行分析，将宏观风险管理理论运用于微观航线运行控制；以建立运行控制、风险管理、损益分析三位一体的运行决策机制为主要诉求点，以期提高航线安全运行水平及运行效率。     

攀枝花航线运行控制中的风险管理与决策分析

作者以危险识别、风险评估与风险缓解为脉络，以攀枝花航线运行为案例进行分析，将宏观风险管理理论运用于微观航线运行控制；以期建立运行控制、风险管理、损益分析三位一体的运行决策机制。     

台湾与大陆中小企业信用担保体系的比较研究与启示

通过对台湾地区中小企业信用担保体系与我国大陆中小企业信用担保体系运行机制的比较研究,以及对信用风险控制模型和信用担保基金持续稳定运营条件的深入分析,对建立和完善我国中小企业信用担保体系提出了几点建议.     

Multicriteria Decision Analysis: A Comprehensive Decision Approach for Management of Contaminated Sediments

Contaminated sediments and other sites present a difficult challenge for environmental decisionmakers. They are typically slow to recover or attenuate naturally, may involve multiple regulatory agencies and stakeholder groups, and engender multiple toxicological and ecotoxicological risks. While environmental decision-making strategies over the last several decades have evolved into increasingly more sophisticated, information-intensive, and complex approaches, there remains considerable dissatisfaction among business, industry, and the public with existing management strategies. Consequently, contaminated sediments and materials are the subject of intense technology development, such as beneficial reuse or in situ treatment. However, current decision analysis approaches, such as comparative risk assessment, benefit-cost analysis, and life cycle assessment, do not offer a comprehensive approach for incorporating the varied types of information and multiple stakeholder and public views that must typically be brought to bear when new technologies are under consideration. Alternatively, multicriteria decision analysis (MCDA) offers a scientifically sound decision framework for management of contaminated materials or sites where stakeholder participation is of crucial concern and criteria such as economics, environmental impacts, safety, and risk cannot be easily condensed into simple monetary expressions. This article brings together a multidisciplinary review of existing decision-making approaches at regulatory agencies in the United States and Europe and synthesizes state-of-the-art research in MCDA methods applicable to the assessment of contaminated sediment management technologies. Additionally, it tests an MCDA approach for coupling expert judgment and stakeholder values in a hypothetical contaminated sediments management case study wherein MCDA is used as a tool for testing stakeholder responses to and improving expert assessment of innovative contaminated sediments technologies.     

Uncertainty about Probability: A Decision Analysis Perspective

The issue of how to think about "uncertainty about probability" is framed and analyzed from the viewpoint of a decision analyst. The failure of nuclear power plants is used as an example. The key idea is to think of probability as describing a state of information on an uncertain event, and to pose the issue of uncertainty in this quantity as uncertainty about a number that would be definitive: it has the property that you would assign it as the probability if you knew it. Logical consistency requires that the probability to assign to a single occurrence in the absence of further information be the mean of the distribution of this definitive number, not the median as is sometimes suggested. Any decision that must be made without the benefit of further information must also be made using the mean of the definitive number's distribution. With this formulation, we find further that the probability of r occurrences in n exchangeable trials will depend on the first n moments of the definitive number's distribution. In making decisions, the expected value of clairvoyance on the occurrence of the event must be at least as great as that on the definitive number. If one of the events in question occurs, then the increase in probability of another such event is readily computed. This means, in terms of coin tossing, that unless one is absolutely sure of the fairness of a coin, seeing a head must increase the probability of heads, in distinction to usual thought. A numerical example for nuclear power shows that the failure of one plant of a group with a low probability of failure can significantly increase the probability that must be assigned to failure of a second plant in the group.     

基于风险损失最小化的供应链赊销决策模型

在产品价格可变动的情况下,将赊销方的赊销合同描述为三元组函数,分析了当存在信用高和信用低2种不同的赊销客户时,赊销方由于信息不对称无法准确判断客户信用水平而造成了赊销货款的损失。根据以上损失,从赊销损失最小化的角度,建立了存在拖欠概率影响下的赊销决策模型,并给出了其Kuhn-Tucker条件。同时,对存在担保和不存在担保2种情况下的赊销情况给出了相应的赊销决策机制,并分析了在该机制下赊销额度、赊销价格和第三方担保对赊销风险的影响。     

Cognitive and Motivational Biases in Decision and Risk Analysis

Behavioral decision research has demonstrated that judgments and decisions of ordinary people and experts are subject to numerous biases. Decision and risk analysis were designed to improve judgments and decisions and to overcome many of these biases. However, when eliciting model components and parameters from decisionmakers or experts, analysts often face the very biases they are trying to help overcome. When these inputs are biased they can seriously reduce the quality of the model and resulting analysis. Some of these biases are due to faulty cognitive processes; some are due to motivations for preferred analysis outcomes. This article identifies the cognitive and motivational biases that are relevant for decision and risk analysis because they can distort analysis inputs and are difficult to correct. We also review and provide guidance about the existing debiasing techniques to overcome these biases. In addition, we describe some biases that are less relevant because they can be corrected by using logic or decomposing the elicitation task. We conclude the article with an agenda for future research.     

信用风险模型比较分析

本文分析了现代信用风险模型迅速发展的主要原因,对主要派别的代表性模型用数学语言进行了总结,比较分析了各模型的原理及优缺点,并简要介绍了各模型的实证效果。     

The Role of Decision Analysis in Risk Analysis: A Retrospective

In commemorating the 40th anniversary of Risk Analysis, this article takes a retrospective look at some of the ways in which decision analysis (as a “sibling field”) has contributed to the development both of the journal, and of risk analysis as a field. I begin with some early foundational papers from the first decade of the journal's history. I then review a number of papers that have applied decision analysis to risk problems over the years, including applications of related methods such as influence diagrams, multicriteria decision analysis, and risk matrices. The article then reviews some recent trends, from roughly the last five years, and concludes with observations about the parallel evolution of risk analysis and decision analysis over the decades—especially with regard to the importance of representing multiple stakeholder perspectives, and the importance of behavioral realism in decision models. Overall, the extensive literature surveyed here supports the view that the incorporation of decision-analytic perspectives has improved the practice of risk analysis.     

A Multicriteria Decision Analysis Model and Risk Assessment Framework for Carbon Capture and Storage

Multicriteria decision analysis (MCDA) has been applied to various energy problems to incorporate a variety of qualitative and quantitative criteria, usually spanning environmental, social, engineering, and economic fields. MCDA and associated methods such as life‐cycle assessments and cost‐benefit analysis can also include risk analysis to address uncertainties in criteria estimates. One technology now being assessed to help mitigate climate change is carbon capture and storage (CCS). CCS is a new process that captures CO₂ emissions from fossil‐fueled power plants and injects them into geological reservoirs for storage. It presents a unique challenge to decisionmakers (DMs) due to its technical complexity, range of environmental, social, and economic impacts, variety of stakeholders, and long time spans. The authors have developed a risk assessment model using a MCDA approach for CCS decisions such as selecting between CO₂ storage locations and choosing among different mitigation actions for reducing risks. The model includes uncertainty measures for several factors, utility curve representations of all variables, Monte Carlo simulation, and sensitivity analysis. This article uses a CCS scenario example to demonstrate the development and application of the model based on data derived from published articles and publicly available sources. The model allows high‐level DMs to better understand project risks and the tradeoffs inherent in modern, complex energy decisions.     

Improving Default Risk Prediction Using Bayesian Model Uncertainty Techniques

Credit risk is the potential exposure of a creditor to an obligor's failure or refusal to repay the debt in principal or interest. The potential of exposure is measured in terms of probability of default. Many models have been developed to estimate credit risk, with rating agencies dating back to the 19th century. They provide their assessment of probability of default and transition probabilities of various firms in their annual reports. Regulatory capital requirements for credit risk outlined by the Basel Committee on Banking Supervision have made it essential for banks and financial institutions to develop sophisticated models in an attempt to measure credit risk with higher accuracy. The Bayesian framework proposed in this article uses the techniques developed in physical sciences and engineering for dealing with model uncertainty and expert accuracy to obtain improved estimates of credit risk and associated uncertainties. The approach uses estimates from one or more rating agencies and incorporates their historical accuracy (past performance data) in estimating future default risk and transition probabilities. Several examples demonstrate that the proposed methodology can assess default probability with accuracy exceeding the estimations of all the individual models. Moreover, the methodology accounts for potentially significant departures from “nominal predictions” due to “upsetting events” such as the 2008 global banking crisis.     

Does a Reactor Need a Safety Backfit? Case Study on Communicating Decision and Risk Analysis Information to Managers

An approach to communicating decision and risk analysis findings to managers is illustrated in a real case context. This article consists essentially of a report prepared for senior managers of the Nuclear Regulatory Commission to help them make a reactor safety decision. It illustrates the communication of decision analysis findings relating to technical risks, costs, and benefits in support of a major risk management decision: whether or not to require a safety backfit. Its focus is on the needs of decision makers, and it introduces some novel communication devices.     

A Social Decision Analysis of the Earthquake Safety Problem: The Case of Existing Los Angeles Buildings

In this paper we propose a framework for conducting a decision analysis for a societal problem such as earthquake safety. The application deals with the formulation and evaluation of alternative policies for the seismic safety problem faced by the city of Los Angeles with regard to its old masonry buildings. A social decision analysis compares the costs and benefits of the alternative policies from the viewpoints of the impacted constituents. The emphasis is on identifying acceptable policy that considers the interests of the impacted constituents and provides incentives for their cooperation. Alternatives ranging from strict regulation to free market are examined. In order to evaluate the trade-offs between additional cost and savings in lives, a direct willingness-to-pay and an economic approach, based on property value differential, are used. Recommendations range from strict regulation for the residential and critical buildings (schools, hospitals, fire stations, etc.) to simply informing the occupants (in the case of commercial and industrial buildings) of the risks involved.