Modeling Risk‐Related Knowledge in Tunneling Projects

Knowledge on failure events and their associated factors, gained from past construction projects, is regarded as potentially extremely useful in risk management. However, a number of circumstances are constraining its wider use. Such knowledge is usually scarce, seldom documented, and even unavailable when it is required. Further, there exists a lack of proven methods to integrate and analyze it in a cost‐effective way. This article addresses possible options to overcome these difficulties. Focusing on limited but critical potential failure events, the article demonstrates how knowledge on a number of important potential failure events in tunnel works can be integrated. The problem of unavailable or incomplete information was addressed by gathering judgments from a group of experts. The elicited expert knowledge consisted of failure scenarios and associated probabilistic information. This information was integrated using Bayesian belief‐networks‐based models that were first customized in order to deal with the expected divergence in judgments caused by epistemic uncertainty of risks. The work described in the article shows that the developed models that integrate risk‐related knowledge provide guidance as to the use of specific remedial measures.     

Capturing and Integrating Knowledge for Managing Risks in Tunnel Works

Risk‐related knowledge gained from past construction projects is regarded as potentially extremely useful in risk management. This article describes a proposed approach to capture and integrate risk‐related knowledge to support decision making in construction projects. To ameliorate the problem related to the scarcity of risks information often encountered in construction projects, Bayesian Belief Networks are used and expert judgment is elicited to augment available information. Particularly, the article provides an overview of judgment‐based biases that can appear in the elicitation of judgments for constructing Bayesian Networks and the provisos that can be made in this respect to minimize these types of bias. The proposed approach is successfully applied to develop six models for top risks in tunnel works. More than 30 tunneling experts in the Netherlands and Germany were involved in the investigation to provide information on identifying relevant scenarios than can lead to failure events associated with tunneling risks. The article has provided an illustration of the applicability of the developed approach for the case of “face instability in soft soils using slurry shields.”     

Helping Individuals to Understand Synergistic Risks: An Assessment of Message Contents Depicting Mechanistic and Probabilistic Concepts

Accumulating evidence shows that certain hazard combinations interact to present synergistic risks. However, little is known about the most effective ways of helping individuals to understand this complex risk concept. More specifically, there is an absence of empirical research that has assessed the relative efficacy of messages that explain either the causal mechanism and/or the probabilistic components of synergistic risks. In an experiment designed to address this issue, we presented participants with messages concerning the synergistic risk of developing esophageal cancer for individuals who consume both tobacco and alcohol. Relative to a control group, we compared the extent to which messages featuring content detailing the underlying biological mechanism, the probabilistic risk, or both improved understanding of the synergistic risk. Our results showed that messages containing details of both the mechanism and probabilistic information were most effective at enabling individuals to understand that the alcohol‐tobacco combination presents a synergistic risk. In addition, large improvements in the accuracy of cancer frequency estimates were observed amongst individuals who received probabilistic information, and the highest relative increase in professed willingness to adopt precautionary behaviors was observed amongst individuals who received the mechanism information only. Importantly, these findings could be utilized in the development of a general model for the communication of synergistic risks. Furthermore, in contrast to previous findings, our study demonstrates that risk messages can be both effective and efficient in helping individuals to acquire a greater understanding of synergistic risks. Acquiring such knowledge could lead to significant improvements in risk‐related decisions concerning combined hazards.     

Is Risk Analysis Scientific?

This article discusses to what extent risk analysis is scientific in view of a set of commonly used definitions and criteria. We consider scientific knowledge to be characterized by its subject matter, its success in developing the best available knowledge in its fields of study, and the epistemic norms and values that guide scientific investigations. We proceed to assess the field of risk analysis according to these criteria. For this purpose, we use a model for risk analysis in which science is used as a base for decision making on risks, which covers the five elements evidence, knowledge base, broad risk evaluation, managerial review and judgment, and the decision; and that relates these elements to the domains experts and decisionmakers, and to the domains fact‐based or value‐based. We conclude that risk analysis is a scientific field of study, when understood as consisting primarily of (i) knowledge about risk‐related phenomena, processes, events, etc., and (ii) concepts, theories, frameworks, approaches, principles, methods and models to understand, assess, characterize, communicate, and manage risk, in general and for specific applications (the instrumental part).     

Testing and Expanding a Model of Cognitive Processing of Risk Information

Scholars have begun to explore the role of modes of information processing and related audience characteristics in reactions to risky situations and risk information.((11, 12, 14, 17, 18, 20))"Information processing" concerns how people attend to and consider available information: systematic processors analyze messages and situations carefully, while heuristic processors skim and use cues (e.g., opinions of trusted reference groups) for quick judgments. This article uses scenarios about a semi-hypothetical industrial facility, in particular risk comparisons being considered by its manager for inclusion in a talk to the community, to explore the impact of information processing. Information insufficiency, self-assessed capacity to understand information, and information-seeking propensities are tested for potential effects on information processing about industrial risks by people living near industry. As well as testing established models, this article explores the additional explanatory value of involvement, relevance, and ability (Earle et al., 1990) and objective knowledge. Both existing model variables and new ones have significant effects on information seeking and information processing in this case, and partly confirm earlier results. Trumbo((17,18)) found that heuristic processors saw lower risk and systematic processors higher risk from suspected cancer clusters. In this study, reporting knowledge about local industrial risks as insufficient for one's purposes and self-reported avoidance of such information both raised ratings of the facility's risk and lowered ratings of its acceptability. Neither type of information processing significantly affected risk or acceptability judgments, but both increased risk ratings and heuristic processing had more effect than systematic processing. Positive ratings of risk comparisons' clarity and meaningfulness decreased risk and increased acceptability ratings, dominated other information variables in predictive power, and exceeded risk, benefit, and trust in contribution to acceptability judgments. Despite differences across studies in designs and variables, and the embryonic development of appropriate (self-reported) measures for use in field surveys, these results confirm the potential value of further research in how information seeking and processing affect risk beliefs and reactions to risk communications.     

Shannon Entropy for Quantifying Uncertainty and Risk in Economic Disparity

The rise in economic disparity presents significant risks to global social order and the resilience of local communities. However, existing measurement science for economic disparity (e.g., the Gini coefficient) does not explicitly consider a probability distribution with information, deficiencies, and uncertainties associated with the underlying income distribution. This article introduces the quantification of Shannon entropy for income inequality across scales, including national‐, subnational‐, and city‐level data. The probabilistic principles of Shannon entropy provide a new interpretation for uncertainty and risk related to economic disparity. Entropy and information‐based conflict rise as world incomes converge. High‐entropy instances can resemble both happy and prosperous societies as well as a socialist–communist social structure. Low entropy signals high‐risk tipping points for anomaly and conflict detection with higher confidence. Finally, spatial–temporal entropy maps for U.S. cities offer a city risk profiling framework. The results show polarization of household incomes within and across Baltimore, Washington, DC, and San Francisco. Entropy produces reliable results at significantly reduced computational costs than Gini coefficients.     

Quantifying Flood Risks in the Netherlands

The Flood Risk in the Netherlands project (Dutch acronym: VNK2) is a large‐scale probabilistic risk assessment for all major levee systems in the Netherlands. This article provides an overview of the methods and techniques used in the VNK2 project. It also discusses two examples that illustrate the potential of quantitative flood risk assessments such as VNK2 to improve flood risk management processes: (i) informing political debates about the risks of flooding and the effectiveness of risk management actions, and (ii) (re)directing research efforts towards important sources of uncertainty.     

Overcoming Confirmation Bias in Causal Attribution: A Case Study of Antibiotic Resistance Risks

When they do not use formal quantitative risk assessment methods, many scientists (like other people) make mistakes and exhibit biases in reasoning about causation, if‐then relations, and evidence. Decision‐related conclusions or causal explanations are reached prematurely based on narrative plausibility rather than adequate factual evidence. Then, confirming evidence is sought and emphasized, but disconfirming evidence is ignored or discounted. This tendency has serious implications for health‐related public policy discussions and decisions. We provide examples occurring in antimicrobial health risk assessments, including a case study of a recently reported positive relation between virginiamycin (VM) use in poultry and risk of resistance to VM‐like (streptogramin) antibiotics in humans. This finding has been used to argue that poultry consumption causes increased resistance risks, that serious health impacts may result, and therefore use of VM in poultry should be restricted. However, the original study compared healthy vegetarians to hospitalized poultry consumers. Our examination of the same data using conditional independence tests for potential causality reveals that poultry consumption acted as a surrogate for hospitalization in this study. After accounting for current hospitalization status, no evidence remains supporting a causal relationship between poultry consumption and increased streptogramin resistance. This example emphasizes both the importance and the practical possibility of analyzing and presenting quantitative risk information using data analysis techniques (such as Bayesian model averaging (BMA) and conditional independence tests) that are as free as possible from potential selection, confirmation, and modeling biases.     

A Fuzzy‐Based Risk Assessment Framework for Autonomous Underwater Vehicle Under‐Ice Missions

The use of autonomous underwater vehicles (AUVs) for various scientific, commercial, and military applications has become more common with maturing technology and improved accessibility. One relatively new development lies in the use of AUVs for under‐ice marine science research in the Antarctic. The extreme environment, ice cover, and inaccessibility as compared to open‐water missions can result in a higher risk of loss. Therefore, having an effective assessment of risks before undertaking any Antarctic under‐ice missions is crucial to ensure an AUV's survival. Existing risk assessment approaches predominantly focused on the use of historical fault log data of an AUV and elicitation of experts’ opinions for probabilistic quantification. However, an AUV program in its early phases lacks historical data and any assessment of risk may be vague and ambiguous. In this article, a fuzzy‐based risk assessment framework is proposed for quantifying the risk of AUV loss under ice. The framework uses the knowledge, prior experience of available subject matter experts, and the widely used semiquantitative risk assessment matrix, albeit in a new form. A well‐developed example based on an upcoming mission by an ISE‐explorer class AUV is presented to demonstrate the application and effectiveness of the proposed framework. The example demonstrates that the proposed fuzzy‐based risk assessment framework is pragmatically useful for future under‐ice AUV deployments. Sensitivity analysis demonstrates the validity of the proposed method.     

Project risk management using multiple criteria decision-making technique and decision tree analysis: a case study of Indian oil refinery

This study proposes an integrated analytical framework for effective management of project risks using combined multiple criteria decision-making technique and decision tree analysis. First, a conceptual risk management model was developed through thorough literature review. The model was then applied through action research on a petroleum oil refinery construction project in the Central part of India in order to demonstrate its effectiveness. Oil refinery construction projects are risky because of technical complexity, resource unavailability, involvement of many stakeholders and strict environmental requirements. Although project risk management has been researched extensively, practical and easily adoptable framework is missing. In the proposed framework, risks are identified using cause and effect diagram, analysed using the analytic hierarchy process and responses are developed using the risk map. Additionally, decision tree analysis allows modelling various options for risk response development and optimises selection of risk mitigating strategy. The proposed risk management framework could be easily adopted and applied in any project and integrated with other project management knowledge areas.     

Some Limitations of “Risk = Threat × Vulnerability × Consequence” for Risk Analysis of Terrorist Attacks

Several important risk analysis methods now used in setting priorities for protecting U.S. infrastructures against terrorist attacks are based on the formula: Risk=Threat×Vulnerability×Consequence. This article identifies potential limitations in such methods that can undermine their ability to guide resource allocations to effectively optimize risk reductions. After considering specific examples for the Risk Analysis and Management for Critical Asset Protection (RAMCAP?) framework used by the Department of Homeland Security, we address more fundamental limitations of the product formula. These include its failure to adjust for correlations among its components, nonadditivity of risks estimated using the formula, inability to use risk‐scoring results to optimally allocate defensive resources, and intrinsic subjectivity and ambiguity of Threat, Vulnerability, and Consequence numbers. Trying to directly assess probabilities for the actions of intelligent antagonists instead of modeling how they adaptively pursue their goals in light of available information and experience can produce ambiguous or mistaken risk estimates. Recent work demonstrates that two‐level (or few‐level) hierarchical optimization models can provide a useful alternative to Risk=Threat×Vulnerability×Consequence scoring rules, and also to probabilistic risk assessment (PRA) techniques that ignore rational planning and adaptation. In such two‐level optimization models, defender predicts attacker's best response to defender's own actions, and then chooses his or her own actions taking into account these best responses. Such models appear valuable as practical approaches to antiterrorism risk analysis.     

Assessing the Benefits and Costs of Homeland Security Research: A Risk-Informed Methodology with Applications for the U.S. Coast Guard

This article describes a methodology for risk-informed benefit–cost analyses of homeland security research products. The methodology is field-tested with 10 research products developed for the U.S. Coast Guard. Risk-informed benefit–cost analysis is a tool for risk management that integrates elements of risk analysis, decision analysis, and benefit–cost analysis. The cost analysis methodology includes a full-cost accounting of research projects, starting with initial fundamental research costs and extending to the costs of implementation of the research products and, where applicable, training, maintenance, and upgrade costs. The benefits analysis methodology is driven by changes in costs and risks leading to five alternative models: cost savings at the same level of security, increased security at the same cost, signal detection improvements, risk reduction by deterrence, and value of information. The U.S. Coast Guard staff selected 10 research projects to test and generalize the methodology. Examples include tools to improve the detection of explosives, reduce the costs of harbor patrols, and provide better predictions of hurricane wind speeds and floods. Benefits models and estimates varied by research project and many input parameters of the benefit estimates were highly uncertain, so risk analysis for sensitivity testing and simulation was important. Aggregating across the 10 research products, we found an overall median net present value of about $385 million, with a range from $54 million (5th percentile) to $877 million (95th percentile). Lessons learned are provided for future applications.     

Public Perception of Climate Change: The Importance of Knowledge and Cultural Worldviews

The importance of knowledge for lay people's climate change concerns has been questioned in recent years, as it had been suggested that cultural values are stronger predictors of concern about climate change than knowledge. Studies that simultaneously measured knowledge related to climate change and cultural values have, however, been missing. We conducted a mail survey in the German‐speaking part of Switzerland (N = 1,065). Results suggested that cultural worldviews and climate‐related knowledge were significantly related with people's concern about climate change. Also, cultural worldviews and climate‐relevant knowledge appeared important for people's willingness to change behaviors and to accept climate change policies. In addition, different types of knowledge were found to have different impacts on people's concern about climate change, their willingness to change behaviors, and their acceptance of policies about climate change. Specifically, causal knowledge significantly increased concern about climate change and willingness to support climate‐friendly policies. We therefore concluded that risk communication should focus on causal knowledge, provided this knowledge does not threaten cultural values.     

Bewertung intertemporaler Abh?ngigkeiten zwischen IT-Projekten

Companies often decide in favour of IT investments (especially IT infrastructure investments) only because these investments build the foundation for more lucrative follow-up investments. Those intertemporal interdependencies among IT projects have to be considered within the scope of a value-based IT portfolio management. Therefore, current literature suggests the use of real options analysis??and therefore the application of option pricing models such as the binomial model or the Black-Scholes model. Both models require a complete market. However, because IT projects are characterized by project specific risks, which cannot be duplicated on a capital market, the forthright application of these models is problematic. This issue has been addressed within the scope of the discrete binomial model so far. In this paper we transfer these findings to the Black-Scholes model. Furthermore, we apply this approach to a real case and illustrate how a correct consideration of project specific risks using the Black-Scholes model can affect investment decisions.     

风险环境下企业信息系统项目控制对绩效的影响机制研究:基于项目经理和用户代表的双重视角

虽然当前项目管理水平不断提高,但是企业信息系统项目实施的成功率仍然不容乐观。项目干系人缺乏准确的相互认知和角色定位,以及风险与控制的关系无法有效处理是导致项目绩效偏低的重要原因。为了从不同视角探索风险和控制对企业信息系统项目绩效的共同作用,通过对65位项目经理和63位用户代表所经历的128个项目的调查,利用结构方程模型进行实证分析,并采用层次回归分析技术进行检验。实证结果表明,正式控制和非正式控制对信息系统项目绩效都具有积极的影响,但是项目经理认为正式控制具有更加重要的作用,而用户代表认为非正式控制的作用更为显著。此外,项目经理和用户代表均认为组织风险和技术风险会减弱正式和非正式控制对绩效的有效作用。因此在控制方式的选择和组合上不仅要基于项目自身的特点,而且要基于干系人的角色。同时,信息系统项目绩效并非单纯由成功或失败因素所决定,而是取决于两类因素的均衡程度。研究结论为信息系统项目的风险控制提供了全新的理论依据和决策支持。     

基于copula的追随者银行的企业项目总体风险评价模型

基于追随者银行的企业项目总体风险评价问题是指当有银行作为先行者介入一个项目时,后续的其它另一个银行作为追随者银行需要将先行者银行的信用风险和所要参与投资的企业项目风险综合加以考虑、从而独立判断项目投资的总体风险大小并进行投资决策。由于任何一家银行都只能熟悉某一些领域、某一些地区、某一些国家的项目,这就导致追随者银行在无法充分掌握项目信息时,需要以先行者银行的信用风险大小为参照物之一来推断企业项目的总体风险,这不仅仅对投资和贷款业务开展较晚的例如中国邮政储蓄银行这样的商业银行有着重要现实意义,而且对所有商业银行的投资活动都有重要的指导意义。通过先行者银行信用风险与项目风险反映企业项目总体风险,本研究建立了基于Copula函数的追随者银行的企业项目总体风险评价模型。本文主要的创新与特色是通过确定先行者银行的信用风险R_F与项目风险R_p的函数关系,进而确定企业项目总体风险R_T,解决了追随者银行所要测算企业项目风险的问题。总体风险模型的稳定性检验表明,在95%的置信水平下,对追随者银行来说,不论多大样本,其所要投资项目的总体风险中的先行者银行信用风险R_F与所要投资的项目风险R_p的重要程度分别为W₁=0.428、W₂=0.572。     

Assessing Major Accident Risks to Support Land‐Use Planning Using a Severity‐Vulnerability Combination Method: A Case Study in Dagushan Peninsula,China

Major accident risks posed by chemical hazards have raised major social concerns in today's China. Land‐use planning has been adopted by many countries as one of the essential elements for accident prevention. This article aims at proposing a method to assess major accident risks to support land‐use planning in the vicinity of chemical installations. This method is based on the definition of risk by the Accidental Risk Assessment Methodology for IndustrieS (ARAMIS) project and it is an expansion application of severity and vulnerability assessment tools. The severity and vulnerability indexes from the ARAMIS methodology are employed to assess both the severity and vulnerability levels, respectively. A risk matrix is devised to support risk ranking and compatibility checking. The method consists of four main steps and is presented in geographical information‐system‐based maps. As an illustration, the proposed method is applied in Dagushan Peninsula, China. The case study indicated that the method could not only aid risk regulations on existing land‐use planning, but also support future land‐use planning by offering alternatives or influencing the plans at the development stage, and thus further enhance the roles and influence of land‐use planning in the accident prevention activities in China.     

基于元网络分析的重大基础设施建设项目风险评估框架与实证

重大基础设施项目具有战略性、集成性、复杂性等特征，项目容易受到多种风险因素的综合影响，导致项目目标的偏离。现有风险评估与风险决策的方法缺乏对于风险因素、风险事件之间关联的分析。为了实现重大基础设施建设项目综合系统的风险评估，本文采用元网络分析方法，构建项目目标、风险事件和风险因素的交互模型，揭示重大基础设施风险事件发生机制的黑箱过程。风险评估过程中，通过多个网络叠加运算分析每个风险因素对于各种风险事件以及项目各目标的影响情况，改进了以往仅对风险因素单一影响程度的风险评估方法。同时，本研究选择我国某河流水电站过坝运输项目方案比选的风险评估过程验证方法的适用性。     

Forest Fire Risk Management and Public Participation in Changing Socioenvironmental Conditions: A Case Study in a Mediterranean Region

Under current conditions of accelerated socioenvironmental change in the Mediterranean forested landscapes, fire is one of the most critical and difficult risks to tackle within the region. This article summarizes the lessons learned from a project based on the participatory integration of qualitative local stakeholders' knowledge with expert GIS fire simulations carried out in the County of El Bages, Catalonia, Spain. First, in this article, a theoretical model--the forest fire circle--is presented in order to explain the reasons for the rise in the damage and frequency of forest fires in this Mediterranean area. Second, it describes the methodology developed and the stages followed during the project. Results show that: (1) the advocacy of old forest reactive management paradigm assumptions and practices based on uncontrolled forest succession can put vast wooded areas of the Mediterranean basin at critical risk; and (2) forest fire management approaches that ignore the crucial role of long-term prevention and local capacity building strategies have failed. In the final section, the content and the specific dimensions of the old reactive paradigm that has characterized forest fire risk management in Catalonia are discussed and contrasted with the possibly emerging preventative paradigm.     

Stakeholders’ roles in virtual project environment: A case study

The rising cost of resources within an organization, increasing capability of information technology, the need to conduct project at different places and availability of competent and inexpensive manpower elsewhere have promoted many companies to adopt project development in a virtual environment. Large scale projects have several full fledged projects conducted by different organizations. Therefore, such projects are called the project of projects (PoPs) here. As PoPs are influenced by stakeholders throughout the project lifecycle, the integration of various projects becomes challenging due to the risks associated with misaligned perceptions, cost, schedule, quality, human resources and communication.This paper describes project context and influencing factors for a PoP's progress in virtual environment and they are explained in terms of two case projects conducted by the author. It is highlighted that communication is an important issue when projects are conducted in virtual environment with different work culture. The communication models and case examples provided here are expected to add knowledge to managing projects in virtual environment.