Major Hazard Information Policy in the European Community: Implications for Risk Analysis

The main impetus to the development of information about major industrial hazards in the European Community comes from the so-called Seveso Directive, which defines an information network and requires the generation and transmission of information as the basis for accident prevention and risk management. This important policy development, which calls for the formal identification and analysis of major hazards and the communication of risk information to members of the public, presents new opportunities and challenges to risk analysis and research in Europe. This paper briefly reviews the accidents that gave rise to the Directive and shaped its content, and then summarizes its requirements. The status of its implementation in the EC Member States is discussed, with special emphasis given to the comparison of safety analysis practices, the Major Accident Reporting System (MARS), and risk communication. Some new research directions stimulated by the Directive are identified.     

风险中性原理及其在衍生证券定价中的作用探析

本文分析了风险中性原理,并对风险中性原理在衍生证券定价中的作用进行了分析与总结。     

Mechanism of Action of Community Education in the Social Management

当前我国社会面临构筑终身教育体系、创建学习型社会的大背景。同时,随着改革开放的不断深入,我国社会矛盾出现多样化、复杂化的趋势,社会管理难度在增加。要充分发挥社区教育在社会管理中的教育培训功能与作用,在学习型社会与创新社会管理之间架构一座桥梁,实现社会管理的新发展。     

基于SNA的智慧社区项目风险传导路径研究

利用社会网络分析(SNA),以项目利益相关者和项目全生命周期视角,研究智慧社区项目的风险传导路径,分析风险在不同利益相关者和不同阶段的传导机理,获得影响风险传导关键环节,提出相应风险应对策略,提高智慧社区项目成功概率。     

A Flexible Count Data Regression Model for Risk Analysis

In many cases, risk and reliability analyses involve estimating the probabilities of discrete events such as hardware failures and occurrences of disease or death. There is often additional information in the form of explanatory variables that can be used to help estimate the likelihood of different numbers of events in the future through the use of an appropriate regression model, such as a generalized linear model. However, existing generalized linear models (GLM) are limited in their ability to handle the types of variance structures often encountered in using count data in risk and reliability analysis. In particular, standard models cannot handle both underdispersed data (variance less than the mean) and overdispersed data (variance greater than the mean) in a single coherent modeling framework. This article presents a new GLM based on a reformulation of the Conway-Maxwell Poisson (COM) distribution that is useful for both underdispersed and overdispersed count data and demonstrates this model by applying it to the assessment of electric power system reliability. The results show that the proposed COM GLM can provide as good of fits to data as the commonly used existing models for overdispered data sets while outperforming these commonly used models for underdispersed data sets.     

Risk Management for Development—Assessing Obstacles and Prioritizing Action

Throughout the process of economic and social development, decisionmakers from the household to the state level are confronted with a multitude of risks: from health and employment risks, to financial and political crises, as well as environmental damages and from the local to global level. The World Bank's 2014 World Development Report (WDR) provides an in‐depth analysis of how the management of such risks can be improved. In particular, it argues that a proactive and integrated approach to risk management can create opportunities for fighting poverty and achieving prosperity—but also acknowledges substantial obstacles to its implementation in practice. This article presents and discusses these obstacles with respect to their causes, consequences, interlinkages, and solutions. In particular, these include obstacles to individual risk management, the obstacles that are beyond the control of individuals and thus require collective action, and, finally, the obstacles that affect the ability of governments and public authorities to manage risks. From these obstacles, this article derives a policy roadmap for the development of risk management strategies that are designed not only around the risk they have to cope with, but also around the practical obstacles to policy implementation.     

On the Need for Restricting the Probabilistic Analysis in Risk Assessments to Variability

It is common perspective in risk analysis that there are two kinds of uncertainties: i) variability as resulting from heterogeneity and stochasticity (aleatory uncertainty) and ii) partial ignorance or epistemic uncertainties resulting from systematic measurement error and lack of knowledge. Probability theory is recognized as the proper tool for treating the aleatory uncertainties, but there are different views on what is the best approach for describing partial ignorance and epistemic uncertainties. Subjective probabilities are often used for representing this type of ignorance and uncertainties, but several alternative approaches have been suggested, including interval analysis, probability bound analysis, and bounds based on evidence theory. It is argued that probability theory generates too precise results when the background knowledge of the probabilities is poor. In this article, we look more closely into this issue. We argue that this critique of probability theory is based on a conception of risk assessment being a tool to objectively report on the true risk and variabilities. If risk assessment is seen instead as a method for describing the analysts’ (and possibly other stakeholders’) uncertainties about unknown quantities, the alternative approaches (such as the interval analysis) often fail in providing the necessary decision support.     

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.     

Nano Risk Analysis: Advancing the Science for Nanomaterials Risk Management

Scientists, activists, industry, and governments have raised concerns about health and environmental risks of nanoscale materials. The Society for Risk Analysis convened experts in September 2008 in Washington, DC to deliberate on issues relating to the unique attributes of nanoscale materials that raise novel concerns about health risks. This article reports on the overall themes and findings of the workshop, uncovering the underlying issues for each of these topics that become recurring themes. The attributes of nanoscale particles and other nanomaterials that present novel issues for risk analysis are evaluated in a risk analysis framework, identifying challenges and opportunities for risk analysts and others seeking to assess and manage the risks from emerging nanoscale materials and nanotechnologies. Workshop deliberations and recommendations for advancing the risk analysis and management of nanotechnologies are presented.     

A Methodology for Seismic Risk Analysis of Nuclear Power Plants

This methodology begins by quantifying the fragility of all key components and structures in the plant. By means of the logic encoded in the plant event trees and fault trees, the component fragilities are combined to form fragilities for the occurrence of plant damage states or release categories. Combining these, in turn, with the seismicity curves yields the frequencies of those states or releases. Uncertainty is explicitly included at each step of the process.     

企业知识资本作用机理的经济学分析

在重新界定企业知识资本的概念和构成的基础上,为便于分析,从经济学的角度将企业知识资本的四个组成部分拟合为包括"硬技术"和"软技术"的企业技术整体.利用生产函数和生产可能性边界理论作为分析工具,对企业知识资本在企业经济增长中的作用机理进行了较深入的分析,并得出了对企业现有知识资本的充分利用和企业知识资本存量的增加是企业经济增长源泉的结论.     

Cyber Risk Management for Critical Infrastructure: A Risk Analysis Model and Three Case Studies

Managing cyber security in an organization involves allocating the protection budget across a spectrum of possible options. This requires assessing the benefits and the costs of these options. The risk analyses presented here are statistical when relevant data are available, and system‐based for high‐consequence events that have not happened yet. This article presents, first, a general probabilistic risk analysis framework for cyber security in an organization to be specified. It then describes three examples of forward‐looking analyses motivated by recent cyber attacks. The first one is the statistical analysis of an actual database, extended at the upper end of the loss distribution by a Bayesian analysis of possible, high‐consequence attack scenarios that may happen in the future. The second is a systems analysis of cyber risks for a smart, connected electric grid, showing that there is an optimal level of connectivity. The third is an analysis of sequential decisions to upgrade the software of an existing cyber security system or to adopt a new one to stay ahead of adversaries trying to find their way in. The results are distributions of losses to cyber attacks, with and without some considered countermeasures in support of risk management decisions based both on past data and anticipated incidents.     

社区赋权推动社区治理效能提升的思路

在我国当前的社区治理中,居民的参与意愿和参与能力不足制约着社区治理效能的提升。提升社区治理效能,要培育社区社会组织参与的组织化力量,增强居民集体行动能力;将信息技术嵌入赋权行动,拓展居民参与治理的渠道;夯实社区赋权的制度基础,为赋权居民提供坚实保障。     

Intelligent Adversary Risk Analysis: A Bioterrorism Risk Management Model

The tragic events of 9/11 and the concerns about the potential for a terrorist or hostile state attack with weapons of mass destruction have led to an increased emphasis on risk analysis for homeland security. Uncertain hazards (natural and engineering) have been successfully analyzed using probabilistic risk analysis (PRA). Unlike uncertain hazards, terrorists and hostile states are intelligent adversaries who can observe our vulnerabilities and dynamically adapt their plans and actions to achieve their objectives. This article compares uncertain hazard risk analysis with intelligent adversary risk analysis, describes the intelligent adversary risk analysis challenges, and presents a probabilistic defender–attacker–defender model to evaluate the baseline risk and the potential risk reduction provided by defender investments. The model includes defender decisions prior to an attack; attacker decisions during the attack; defender actions after an attack; and the uncertainties of attack implementation, detection, and consequences. The risk management model is demonstrated with an illustrative bioterrorism problem with notional data.