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  1. Law TH, Noland RB, Evans AW
    Risk Anal, 2013 Jul;33(7):1367-78.
    PMID: 23106188 DOI: 10.1111/j.1539-6924.2012.01916.x
    It has been shown that road safety laws, such as motorcycle helmet and safety belt laws, have a significant effect in reducing road fatalities. Although an expanding body of literature has documented the effects of these laws on road safety, it remains unclear which factors influence the likelihood that these laws are enacted. This study attempts to identify the factors that influence the decision to enact safety belt and motorcycle helmet laws. Using panel data from 31 countries between 1963 and 2002, our results reveal that increased democracy, education level, per capita income, political stability, and more equitable income distribution within a country are associated with the enactment of road safety laws.
  2. Bristow M, Fang L, Hipel KW
    Risk Anal, 2012 Nov;32(11):1935-55.
    PMID: 22804565 DOI: 10.1111/j.1539-6924.2012.01867.x
    The domain of risk analysis is expanded to consider strategic interactions among multiple participants in the management of extreme risk in a system of systems. These risks are fraught with complexity, ambiguity, and uncertainty, which pose challenges in how participants perceive, understand, and manage risk of extreme events. In the case of extreme events affecting a system of systems, cause-and-effect relationships among initiating events and losses may be difficult to ascertain due to interactions of multiple systems and participants (complexity). Moreover, selection of threats, hazards, and consequences on which to focus may be unclear or contentious to participants within multiple interacting systems (ambiguity). Finally, all types of risk, by definition, involve potential losses due to uncertain events (uncertainty). Therefore, risk analysis of extreme events affecting a system of systems should address complex, ambiguous, and uncertain aspects of extreme risk. To accomplish this, a system of systems engineering methodology for risk analysis is proposed as a general approach to address extreme risk in a system of systems. Our contribution is an integrative and adaptive systems methodology to analyze risk such that strategic interactions among multiple participants are considered. A practical application of the system of systems engineering methodology is demonstrated in part by a case study of a maritime infrastructure system of systems interface, namely, the Straits of Malacca and Singapore.
  3. Buurman J, Zhang S, Babovic V
    Risk Anal, 2009 Mar;29(3):366-79.
    PMID: 19076327 DOI: 10.1111/j.1539-6924.2008.01160.x
    Complex engineering systems are usually designed to last for many years. Such systems will face many uncertainties in the future. Hence the design and deployment of these systems should not be based on a single scenario, but should incorporate flexibility. Flexibility can be incorporated in system architectures in the form of options that can be exercised in the future when new information is available. Incorporating flexibility comes, however, at a cost. To evaluate if this cost is worth the investment a real options analysis can be carried out. This approach is demonstrated through analysis of a case study of a previously developed static system-of-systems for maritime domain protection in the Straits of Malacca. This article presents a framework for dynamic strategic planning of engineering systems using real options analysis and demonstrates that flexibility adds considerable value over a static design. In addition to this it is shown that Monte Carlo analysis and genetic algorithms can be successfully combined to find solutions in a case with a very large number of possible futures and system designs.
  4. Yao S, Wu Q, Kang Q, Chen YW, Lu Y
    Risk Anal, 2024 Feb;44(2):459-476.
    PMID: 37330273 DOI: 10.1111/risa.14175
    The Northern Sea Route (NSR) makes travel between Europe and Asia shorter and quicker than a southern transit via the Strait of Malacca and Suez Canal. It provides greater access to Arctic resources such as oil and gas. As global warming accelerates, melting Arctic ice caps are likely to increase traffic in the NSR and enhance its commercial viability. Due to the harsh Arctic environment imposing threats to the safety of ship navigation, it is necessary to assess Arctic navigation risk to maintain shipping safety. Currently, most studies are focused on the conventional assessment of the risk, which lacks the validation based on actual data. In this study, actual data about Arctic navigation environment and related expert judgments were used to generate a structured data set. Based on the structured data set, extreme gradient boosting (XGBoost) and alternative methods were used to establish models for the assessment of Arctic navigation risk, which were validated using cross-validation. The results show that compared with alternative models, XGBoost models have the best performance in terms of mean absolute errors and root mean squared errors. The XGBoost models can learn and reproduce expert judgments and knowledge for the assessment of Arctic navigation risk. Feature importance (FI) and shapley additive explanations (SHAP) are used to further interpret the relationship between input data and predictions. The application of XGBoost, FI, and SHAP is aimed to improve the safety of Arctic shipping using advanced artificial intelligence techniques. The validated assessment enhances the quality and robustness of assessment.
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