Use of Advances in Technology for Maritime Risk Assessment

The maritime industry is moving toward a "goal-setting" risk-based regime. This opens the way to safety engineers to explore and exploit flexible and advanced risk modeling and decision-making approaches in the design and operation processes. In this article, following a brief review of the current status of maritime risk assessment, a design/operation selection framework and a design/operation optimization framework are outlined. A general discussion of control engineering techniques and their application to risk modeling and decision making is given. Four novel risk modeling and decision-making approaches are then outlined with illustrative examples to demonstrate their use. Such approaches may be used as alternatives to facilitate risk modeling and decision making in situations where conventional techniques cannot be appropriately applied. Finally, recommendations on further exploitation of advances in general engineering and technology are suggested with respect to risk modeling and decision making.     

A framework for supporting health capability-based planning: Identifying and structuring health capabilities

The COVID-19 pandemic has highlighted that health security systems must be redesigned, in a way that they are better prepared and ready to cope with multiple and diverse health threats, from predictable and well-known epidemics to unexpected and challenging pandemics. A powerful way of accomplishing this goal is to focus the planning on health capabilities. This focus may enhance the ability to respond to and recover from health threats and emergencies, while helping to identify the level of resources required to maintain and build up those capabilities that are critical in ensuring the preparedness of health security systems. However, current attempts for defining and organizing health capabilities have some important limitations. First, such attempts were not designed to consider diverse scenarios and multiple health threats. Second, they provide a limited representation of capabilities and lack a systemic perspective. Third, they struggle to identify capability and resource gaps. In this article, we thus propose a new framework for identifying and structuring health capabilities and support health capability planning. The suggested framework has three main potential benefits. First, the framework may help policymakers in planning under high levels of uncertainty, by considering multiple realistic and stressful scenarios. Second, it can provide risk analysts with a more comprehensive representation of health capabilities and their complex relationships. Third, it can support planners in identifying resource and capability gaps. We illustrate the use of the framework in practice considering an outbreak scenario caused by three different health threats (COVID-19, Ebola, and Influenza viruses).     

Assessing Uncertainty in Simulation-Based Maritime Risk Assessment

Recent work in the assessment of risk in maritime transportation systems has used simulation-based probabilistic risk assessment techniques. In the Prince William Sound and Washington State Ferries risk assessments, the studies' recommendations were backed up by estimates of their impact made using such techniques and all recommendations were implemented. However, the level of uncertainty about these estimates was not available, leaving the decisionmakers unsure whether the evidence was sufficient to assess specific risks and benefits. The first step toward assessing the impact of uncertainty in maritime risk assessments is to model the uncertainty in the simulation models used. In this article, a study of the impact of proposed ferry service expansions in San Francisco Bay is used as a case study to demonstrate the use of Bayesian simulation techniques to propagate uncertainty throughout the analysis. The conclusions drawn in the original study are shown, in this case, to be robust to the inherent uncertainties. The main intellectual merit of this work is the development of Bayesian simulation technique to model uncertainty in the assessment of maritime risk. However, Bayesian simulations have been implemented only as theoretical demonstrations. Their use in a large, complex system may be considered state of the art in the field of computational sciences.     

Risk-Based Ranking of Dominant Contributors to Maritime Pollution Events

This report describes a conceptual approach for identifying dominant contributors to risk from maritime shipping of hazardous materials. Maritime transportation accidents are relatively common occurrences compared to more frequently analyzed contributors to public risk. Yet research on maritime safety and pollution incidents has not been guided by a systematic, risk-based approach. Maritime shipping accidents can be analyzed using event trees to group the accidents into "bins," or groups, of similar characteristics such as type of cargo, location of accident (e.g., harbor, inland waterway), type of accident (e.g., fire, collision, grounding), and size of release. The importance of specific types of events to each accident bin can be quantified. Then the overall importance of accident events to risk can be estimated by weighting the events' individual bin importance measures by the risk associated with each accident bin.     

Hybrid Processing of Stochastic and Subjective Uncertainty Data

Uncertainty analyses typically recognize separate stochastic and subjective sources of uncertainty, but do not systematically combine the two, although a large amount of data used in analyses is partly stochastic and partly subjective. We have developed methodology for mathematically combining stochastic and subjective sources of data uncertainty, based on new "hybrid number" approaches. The methodology can be utilized in conjunction with various traditional techniques, such as PRA (probabilistic risk assessment) and risk analysis decision support. Hybrid numbers have been previously examined as a potential method to represent combinations of stochastic and subjective information, but mathematical processing has been impeded by the requirements inherent in the structure of the numbers, e.g., there was no known way to multiply hybrids. In this paper, we will demonstrate methods for calculating with hybrid numbers that avoid the difficulties. By formulating a hybrid number as a probability distribution that is only fuzzily known, or alternatively as a random distribution of fuzzy numbers, methods are demonstrated for the full suite of arithmetic operations, permitting complex mathematical calculations. It will be shown how information about relative subjectivity (the ratio of subjective to stochastic knowledge about a particular datum) can be incorporated. Techniques are also developed for conveying uncertainty information visually, so that the stochastic and subjective components of the uncertainty, as well as the ratio of knowledge about the two, are readily apparent. The techniques demonstrated have the capability to process uncertainty information for independent, uncorrelated data, and for some types of dependent and correlated data. Example applications are suggested, illustrative problems are shown, and graphical results are given.     

An overview of maritime waterway quantitative risk assessment models

The safe navigation of ships, especially in narrow shipping waterways, is of the utmost concern to researchers as well as maritime authorities. Many researchers and practitioners have conducted studies on risk assessment for maritime transportation and have proposed risk reduction/control measures accordingly. This article provides a detailed review and assessment of various quantitative risk assessment models for maritime waterways. Eighty-seven academic papers and/or project reports are summarized and discussed. The review then proceeds to analyze the frequency and consequence estimation models separately. It should be pointed out that we further summarize the advantages and disadvantages of frequency estimation models and provide recommendations for their application. From the overview, we find that the quantification of the impact of human error is of great importance and should be considered in future studies. Possible solutions are also proposed in the discussions.     

Expert Judgment-Based Risk Assessment Using Statistical Scenario Analysis: A Case Study—Running the Bulls in Pamplona (Spain)

In this article, we present a methodology to assess the risk incurred by a participant in an activity involving danger of injury. The lack of high-quality historical data for the case considered prevented us from constructing a sufficiently detailed statistical model. It was therefore decided to generate a risk assessment model based on expert judgment. The methodology is illustrated in a real case context: the assessment of risk to participants in a San Fermin bull-run in Pamplona (Spain). The members of the panel of "experts on the bull-run" represented very different perspectives on the phenomenon: runners, surgeons and other health care personnel, journalists, civil defense workers, security staff, organizers, herdsmen, authors of books on the bull-run, etc. We consulted 55 experts. Our methodology includes the design of a survey instrument to elicit the experts' views and the statistical and mathematical procedures used to aggregate their subjective opinions.     

Speaking the Truth in Maritime Risk Assessment

Several major risk studies have been performed in recent years in the maritime transportation domain. These studies have had significant impact on management practices in the industry. The first, the Prince William Sound risk assessment, was reviewed by the National Research Council and found to be promising but incomplete, as the uncertainty in its results was not assessed. The difficulty in assessing this uncertainty is the different techniques that need to be used to model risk in this dynamic and data-scarce application area. In previous articles, we have developed the two pieces of methodology necessary to assess uncertainty in maritime risk assessment, a Bayesian simulation of the occurrence of situations with accident potential and a Bayesian multivariate regression analysis of the relationship between factors describing these situations and expert judgments of accident risk. In this article, we combine the methods to perform a full-scale assessment of risk and uncertainty for two case studies. The first is an assessment of the effects of proposed ferry service expansions in San Francisco Bay. The second is an assessment of risk for the Washington State Ferries, the largest ferry system in the United States.     

Life-Cycle Assessment in Relation to Risk Assessment: An Evolving Perspective

A life-cycle approach takes a cradle-to-grave perspective of a product's numerous activities from the raw material extraction to final disposal. There have been recent efforts to develop life-cycle assessment (LCA) to assess both environmental and human health issues. The question then arises: what are the capabilities of LCA, especially in relation to risk assessment? To address this question, this paper first describes the LCA mass-based accounting system and then analyzes the use of this approach for environmental and human health assessment. The key LCA limitations in this respect are loss of spatial, temporal, dose-response, and threshold information. These limitations affect LCA's capability to assess several environmental issues, and human health in particular. This leads to the conclusion that LCA impact assessment does not predict or measure actual effects, quantitate risks, or address safety. Instead, LCA uses mass loadings with simplifying assumptions and subjective judgments to add independent effects and exposures into an overall score. As a result, LCA identifies possible human health issues on a systemwide basis from a worst case, hypothetical hazard perspective. Ideally, the identified issues would then be addressed by more detailed assessment methods, such as risk assessment.     

Human Factors Analysis for Maritime Accidents Based on a Dynamic Fuzzy Bayesian Network

Human factors are widely regarded to be highly contributing factors to maritime accident prevention system failures. The conventional methods for human factor assessment, especially quantitative techniques, such as fault trees and bow-ties, are static and cannot deal with models with uncertainty, which limits their application to human factors risk analysis. To alleviate these drawbacks, in the present study, a new human factor analysis framework called multidimensional analysis model of accident causes (MAMAC) is introduced. MAMAC combines the human factors analysis and classification system and business process management. In addition, intuitionistic fuzzy set theory and Bayesian Network are integrated into MAMAC to form a comprehensive dynamic human factors analysis model characterized by flexibility and uncertainty handling. The proposed model is tested on maritime accident scenarios from a sand carrier accident database in China to investigate the human factors involved, and the top 10 most highly contributing primary events associated with the human factors leading to sand carrier accidents are identified. According to the results of this study, direct human factors, classified as unsafe acts, are not a focus for maritime investigators and scholars. Meanwhile, unsafe preconditions and unsafe supervision are listed as the top two considerations for human factors analysis, especially for supervision failures of shipping companies and ship owners. Moreover, potential safety countermeasures for the most highly contributing human factors are proposed in this article. Finally, an application of the proposed model verifies its advantages in calculating the failure probability of accidents induced by human factors.     

Information and communications technologies, strategic asymmetry and national security

In the history of warfare, there are a number of examples of strategic uses of asymmetric technologies. Consistent with history and theory, individuals, organizations and nations have spotted opportunities to employ information and communications technologies to gain and exploit asymmetric advantages and to counter asymmetric weaknesses. This article discusses various asymmetries associated with institutions, nations and organizations that influence the ICT-national security nexus. Regulative, normative and cognitive institutions in a country provide various mechanisms that affect the nature of positive and negative asymmetries. Nations and organizations also differ in terms of their capability to assimilate ICT tools to gain positive asymmetries and deal with vulnerabilities of negative asymmetries. Integrative approaches that combine policy and technological measures at various levels are likely to make the world more secure.     

Project agility assessment: an integrated decision analysis approach

Agility is the ability of a project to respond to a changing environment effectively. This may include the capability of a project to be adapted to the dynamism that exists in the stakeholders’ needs, technological changes, etc. To assure such a capability, it is necessary to assess the extent of projects’ adaptability to change. This could be done by expressing the agility in terms of some quantifiable parameters such as size of the project's organisation, levels of expertise, etc. However, there are uncertainties embedded in measurement of such parameters caused by imprecision and lack of well-defined information, which cannot be well treated by conventional assessment approaches. To address such a complexity, in this article, a decision aid model using fuzzy set theory is proposed for agility assessment of projects. The applicability of the proposed model will be demonstrated by a case study in software development project management.     

A Model for Measuring Supplier Risk: Do Operational Capability Indicators Enhance the Prediction Accuracy of Supplier Risk?

The purpose of this study was to develop a supplier risk assessment model for buyers to estimate supplier risk. It is one of the few empirical studies that considers both operational capability indicators and financial indicators; a standard logit model with five key variables (switching cost, operating profit margin, asset turnover ratio, quality capability and technological capability) was suggested as a practical tool. This model not only enhanced the accuracy of supplier risk assessment, but also served as a core element of a new supply chain management tool, ‘supplier management at risk’. More practically, the model enables purchasing firms to assess supplier risk and take proactive measures against the estimated risk.     

Vulnerability assessment of strait/canals in maritime transportation using fuzzy evidential reasoning approach

The safety and security of straits and canals have been playing an important role in maritime transportation. The disruption of a strait or canal will lead to increased transportation costs and world trade problems. Therefore, an advanced approach incorporating fuzzy logic and an evidential reasoning (ER) algorithm is developed to conduct the vulnerability assessment of straits or canals in this paper. A hierarchical structure is first developed taking into account both qualitative and quantitative factors. The fuzzy rule-based transformation technique is applied to convert quantitative factors into qualitative ones, which enables the application of a fuzzy ER method to synthesize all the information from the bottom to the top along the developed hierarchical structure. The software of intelligent decision system (IDS) is used to facilitate the process of vulnerability assessment. The developed framework then is validated and demonstrated in a case study for vulnerability prioritization which can be used as a reference to ensure the safety and security of straits and canals for decision-makers.     

Marine and Offshore Safety Assessment by Incorporative Risk Modeling in a Fuzzy-Bayesian Network of an Induced Mass Assignment Paradigm

The incorporation of the human element into a probabilistic risk-based model is one that requires a possibilistic integration of appropriate techniques and/or that of vital inputs of linguistic nature. While fuzzy logic is an excellent tool for such integration, it tends not to cross its boundaries of possibility theory, except via an evidential reasoning supposition. Therefore, a fuzzy-Bayesian network (FBN) is proposed to enable a bridge to be made into a probabilistic setting of the domain. This bridge is formalized by way of the mass assignment theory. A framework is also proposed for its use in maritime safety assessment. Its implementation has been demonstrated in a maritime human performance case study that utilizes performance-shaping factors as the input variables of this groundbreaking FBN risk model.     

Producing ratio measures of effect with quantitative microbial risk assessment

Estimating the risk of infections or other outcomes incident to pathogen exposure is a primary goal of quantitative microbial risk assessment (QMRA). Such estimates are useful to predict population-level risks, to evaluate exposures based on normative or tolerable risk guidelines, and to interpret the likely public health relevance of microbial measurements in environmental media. To evaluate alternative control measures (interventions), ratio estimates of effect (e.g., odds and risk ratios) are needed that are more broadly interpretable in the health sciences and consistent with convention in epidemiology. In this paper, we propose a general method for estimating widely used ratio measures of effect derived from stochastic QMRA approaches, including the generation of appropriate confidence intervals. Such QMRA-derived ratios can be used as a basis for evaluating interventions via hypothesis testing and for inclusion in systematic reviews and meta-analyses in a form consistent with risk estimation approaches commonly used in epidemiology.     

An Emerging New Risk Analysis Science: Foundations and Implications

To solve real‐life problems—such as those related to technology, health, security, or climate change—and make suitable decisions, risk is nearly always a main issue. Different types of sciences are often supporting the work, for example, statistics, natural sciences, and social sciences. Risk analysis approaches and methods are also commonly used, but risk analysis is not broadly accepted as a science in itself. A key problem is the lack of explanatory power and large uncertainties when assessing risk. This article presents an emerging new risk analysis science based on novel ideas and theories on risk analysis developed in recent years by the risk analysis community. It builds on a fundamental change in thinking, from the search for accurate predictions and risk estimates, to knowledge generation related to concepts, theories, frameworks, approaches, principles, methods, and models to understand, assess, characterize, communicate, and (in a broad sense) manage risk. Examples are used to illustrate the importance of this distinct/separate risk analysis science for solving risk problems, supporting science in general and other disciplines in particular.     

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.     

Systemic Valuation of Strategic Preparedness Through Application of the Inoperability Input-Output Model with Lessons Learned from Hurricane Katrina

The U.S. Department of Homeland Security (DHS) has mandated all regions to "carefully weigh the benefit of each homeland security endeavor and only allocate resources where the benefit of reducing risk is worth the amount of additional cost" (DHS, 2006, p. 64). This mandate illuminates the need to develop methods for systemic valuation of preparedness measures that support strategic decision making. This article proposes an analysis method that naturally emerges from the structure of the inoperability input-output model (IIM) through which various regional- and sector-specific impact analyses can be cost-effectively integrated for natural and man-made disasters. The IIM is described extensively in a companion paper (Lian et al., 2007). Its reliance on data classifications structured by the U.S. Census Bureau and its extensive accounting of economic interdependencies enables us to decompose a risk analysis activity, perform independent assessments, and properly integrate the assessment for a systemic valuation of risk and risk management activity. In this article, we account for and assess some of the major impacts of Hurricanes Katrina and Rita to demonstrate this use of the IIM and illustrate hypothetical, reduced impacts resulting from various strategic preparedness decisions. Our results indicate the capability of the IIM to guide the decision-making processes involved in developing a preparedness strategy.     

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.