The Call for a Shift from Risk to Resilience: What Does it Mean?

A research on urban disaster resilience assessment system for rainstorm and flood disasters: A case study of Beijing

Under the background of global climate change, rainstorm and flood disasters have become the most serious cataclysm. Under the circumstances of an increasingly severe risk situation, it is necessary to enhance urban disaster resilience. Based on the disaster resilience process of prevention, absorption, and enhancement, and considering the safety factors such as personnel, facility, environment and management, this paper forms a dual dimension of the urban disaster resilience assessment model covering the key elements of urban disaster response and the core capacity of urban disaster recovery. Furthermore, if taking into account the characteristics of rainstorm and flood disasters, the paper screens the key indicators to build up an assessment index system of an urban rainstorm and flood disaster. The practical application was implemented in Beijing to have an assessment of the ability to recover from rainstorm and flood disasters in all districts of Beijing. And then, some pertinent suggestions for enhancing the resilience of Beijing to rainstorm and flood disasters were proposed.

Improving the resilience of power grids against typhoons with data-driven spatial distributionally robust optimization

In recent years, the increased frequency of natural hazards has led to more disruptions in power grids, potentially causing severe infrastructural damages and cascading failures. Therefore, it is important that the power system resilience be improved by implementing new technology and utilizing optimization methods. This paper proposes a data-driven spatial distributionally robust optimization (DS-DRO) model to provide an optimal plan to install and dispatch distributed energy resources (DERs) against the uncertain impact of natural hazards such as typhoons. We adopt an accurate spatial model to evaluate the failure probability with regard to system components based on wind speed. We construct a moment-based ambiguity set of the failure distribution based on historical typhoon data. A two-stage DS-DRO model is then formulated to obtain an optimal resilience enhancement strategy. We employ the combination of dual reformulation and a column-and-constraints generation algorithm, and showcase the effectiveness of the proposed approach with a modified IEEE 13-node reliability test system projected in the Hong Kong region.

Data-driven predictive modeling in risk assessment: Challenges and directions for proper uncertainty representation

Data-driven predictive modeling is increasingly being used in risk assessments. While such modeling may provide improved consequence predictions and probability estimates, it also comes with challenges. One is that the modeling and its output does not measure and represent uncertainty due to lack of knowledge, that is, "epistemic uncertainty." In this article, we demonstrate this point by conceptually linking the main elements and output of data-driven predictive models with the main elements of a general risk description, thereby placing data-driven predictive modeling on a risk science foundation. This allows for an evaluation of such modeling with reference to risk science recommendations for what constitutes a complete risk description. The evaluation leads us to conclude that, as a minimum, to cover all elements of a complete risk description a risk assessment using data-driven predictive modeling needs to be supported by assessments of the uncertainty and risk related to the assumptions underlying the modeling. In response to this need, we discuss an approach for assessing assumptions in data-driven predictive modeling.

Strategies to be prepared for a risk communication crisis

This introductory article describes a multistep process for communicating complex information from the perspective of the communicator. As part of the introduction to a special issue, it suggests three premises grounded in the literature and practice. One is an organization cannot risk communicate its way out of problems created by poor risk assessment and risk management. Second, poor risk communication can undermine satisfactory risk assessment and management. Third, a proactive plan grounded in risk analysis is essential and implemented with periodic training exercises. The article presents a step-by-step communication planning process that has been used in the field. Much of the special issue is devoted to the experiences of practitioners and communication experts in successfully communicating and listening to government and private organization representatives, media representatives, and the public about complex risk issues, focusing on nuclear-related ones. The goal is to add to our collective experience on practices that work and do not work under the many conditions that involve risk communications. The ground is changing under risk communication because of the rapid expansion of media sources and technologies. What worked a decade ago may no longer be best practice. Common to success across media and audience is the need for a planning process that is adaptable to changing conditions.

On Some Foundational Issues Concerning the Relationship Between Risk and Resilience

"Risk" and "resilience" are both terms with a long history, but how they are related and should be related are strongly debated. This article discusses the appropriateness of a perspective advocated by an active "resilience school" that sees risk as a change in critical system functionality, as a result of an event (disturbance, hazard, threat, accident), but not covering the recovery from the event. From this perspective, two theses are examined: risk and resilience are disjunct concepts, and risk is an aspect of resilience. Through the use of several examples and reasoning, the article shows that this perspective challenges daily-life uses of the risk term, common practices of risk assessments and risk management, as well as contemporary risk science. A fundamental problem with the perspective is that system recovery is also an important aspect of risk, not only of resilience. Risk and resilience analysis and management implications of the conceptual analysis are also discussed.

Adoption of a Data-Driven Bayesian Belief Network Investigating Organizational Factors that Influence Patient Safety

Medical errors pose high risks to patients. Several organizational factors may impact the high rate of medical errors in complex and dynamic healthcare systems. However, limited research is available regarding probabilistic interdependencies between the organizational factors and patient safety errors. To explore this, we adopt a data-driven Bayesian Belief Network (BBN) model to represent a class of probabilistic models, using the hospital-level aggregate survey data from U.K. hospitals. Leveraging the use of probabilistic dependence models and visual features in the BBN model, the results shed new light on relationships existing among eight organizational factors and patient safety errors. With the high prediction capability, the data-driven approach results suggest that "health and well-being" and "bullying and harassment in the work environment" are the two leading factors influencing the number of reported errors and near misses affecting patient safety. This study provides significant insights to understand organizational factors' role and their relative importance in supporting decision-making and safety improvements.

Framework for Operational Resilience Management of Critical Infrastructures and Organizations

Progressing digitalization and networking of systems and organizations representing Critical Infrastructures opens promising new potentials and opportunities, which on the downside, are accompanied by rising complexity and increasingly opaque interdependencies. The consequently increasing lack of knowledge leads to uncertainties affecting risk assessment and decision-making in case of adverse events. This trend motivated recent discussions and developments in risk science, emphasizing the need to handle such uncertainties. Complementarily, research in the resilience domain focuses on system capabilities to handle surprising hazardous situations. Several frameworks presented in the literature aim at combining both perspectives but either lack the focus on operational management, have a rather theoretical approach, or are designed for specific applications. Based on this observation, we propose an approach that integrates resilience management into the actual operation of Critical Infrastructure Systems and Organizations by providing an operational process that coordinates the fundamental resilience capabilities of responding, monitoring, anticipation, and learning. Furthermore, we tackle the challenge of uncertainties resulting from a lack of knowledge by aligning the concepts of digital twin and resilience management. The proposed framework is extensively discussed, and required processes are presented in detail. Eventually, its applicability and potential are reviewed by means of a complex hazardous situation at a Bavarian district heating power plant.

The Importance of Risk Management: What is Missing in ISO Standards?

The overall aim of this article is to contribute to the further development of the area of risk analysis and risk management in the International Organization for for Standardization (ISO) standards by strengthening its scientific basis. Industrial standards, especially ISO standards, are the tools organizations use to manage their risk, through following their guidance and complying with their requirements. Organizations confirm their compliance with these standards through certification, which means that they heavily depend upon the quality of the ISO standards to enable them to effectively manage their risk. The purpose of this study is to investigate what guidance is given on key elements of risk management and how well ISO standards are aligned with state-of-the-art risk management literature. Eighteen ISO standards, all addressing risk management, were reviewed in this study with regard to risk terminology and guidance. The results of the study confirm the increasing importance of risk management for business. However, the study also shows a lack of guidance on doing risk analysis in the industrial standards examined. The ISO management system standards and guidelines are not aligned with the scientific literature on risk and are not appropriate for the management of risk arising from complex interactions and emergent behavior that is inherent in present-day sociotechnical systems.

Organizational Resilience to Disruption Risks: Developing Metrics and Testing Effectiveness of Operational Strategies

This study draws from the system resilience literature to propose three different metrics for evaluating the resilience performance of organizations against disruptions: the initial loss due to the disruption, the maximum loss, and the total loss over time. In order to show the usefulness of the developed metrics in practice, we deploy these metrics to study the effectiveness of two resilience strategies: maintaining operational slack and broadening operational scope, by empirically analyzing the performance of manufacturing firms that experienced a disruption during the period from 2005 to the end of 2014. The results show that maintaining certain aspects of operational slack and broadening business scope and geographic scope can affect these different metrics in different ways. Our results help decisionmakers in risk management to gain a better understanding of the conditions under which the recommended strategies actually improve organizations' resilience, as well as the ways in which they may do so.

Prioritizing Multidimensional Interdependent Factors Influencing COVID-19 Risk

COVID-19 has significantly affected various industries and domains worldwide. Since such pandemics are considered as rare events, risks associated with pandemics are generally managed through reactive approaches, which involve seeking more information about the severity of the pandemic over time and adopting suitable strategies accordingly. However, policy-makers at a national level must devise proactive strategies to minimize the harmful impacts of such pandemics. In this article, we use a country-level data-set related to humanitarian crises and disasters to explore critical factors influencing COVID-19 related hazard and exposure, vulnerability, lack of coping capacity, and the overall risk for individual countries. The main contribution is to establish the relative importance of multidimensional factors associated with COVID-19 risk in a probabilistic network setting. This study provides unique insights to policy-makers regarding the identification of critical factors influencing COVID-19 risk and their relative importance in a network setting.

Risk Science in Higher Education: The Current and Future Role of Risk Science in the University Curriculum

Risk and uncertainty are critical elements for decision making across fields, such as business, policy, engineering, and healthcare. As universities maintain and adapt curriculums to ensure their graduates are prepared for risk-related roles, there is momentum for risk science to be included in the curriculum. The study of risk science can be observed in programs devoted to risk fundamentals (for example on basic concepts like risk and probability) and risk assessment, risk perception and communication, and risk management and governance. Additionally, selected concepts related to risk science, such as safety and resilience analysis and management, are increasingly being embedded into a broader range of university curriculums. The present article presents a structure for classifying these programs, by distinguishing between generic (fundamental) risk science and applied risk science, with subcategories reflecting both subject (topic) and domain (application area). An overall evaluation of the broad offerings in risk science through devoted curriculums and selected topics within other specialized fields is conducted on the basis of the study programs currently offered. Perspectives are also provided on how to further enhance risk science studies at our universities and colleges.

The Role of Time in Risk and Risk Analysis: Implications for Resilience, Sustainability, and Management

There is a persistent misconception that risk analysis is only suited for considering the immediate consequences of an event. Such a limitation would make risk analysis unsuitable for many challenges, including resilience, sustainability, and adaptation. Fortunately, there is no such limitation. However, this notion has stemmed from a lack of clarity regarding how time is considered in risk analysis and risk characterization. In this article, we discuss this issue and show that risk science provides concepts and frameworks that can appropriately address time. Ultimately, we propose an adjusted nomenclature for explicitly reflecting time in risk conceptualization and characterizations.

Resilience Assessment of Wind Farms in the Arctic with the Application of Bayesian Networks

Infrastructure systems, such as wind farms, are prone to various human-induced and natural disruptions such as extreme weather conditions. There is growing concern among decision makers about the ability of wind farms to withstand and regain their performance when facing disruptions, in terms of resilience-enhanced strategies. This paper proposes a probabilistic model to calculate the resilience of wind farms facing disruptive weather conditions. In this study, the resilience of wind farms is considered to be a function of their reliability, maintainability, supportability, and organizational resilience. The relationships between these resilience variables can be structured using Bayesian network models. The use of Bayesian networks allows for analyzing different resilience scenarios. Moreover, Bayesian networks can be used to quantify resilience, which is demonstrated in this paper with a case study of a wind farm in Arctic Norway. The results of the case study show that the wind farm is highly resilient under normal operating conditions, and slightly degraded under Arctic operating conditions. Moreover, the case study introduced the calculation of wind farm resilience under Arctic black swan conditions. A black swan scenario is an unknowable unknown scenario that can affect a system with low probability and very high extreme consequences. The results of the analysis show that the resilience of the wind farm is significantly degraded when operating under Arctic black swan conditions. In addition, a backward propagation of the Bayesian network illustrates the percentage of improvement required in each resilience factor in order to attain a certain level of resilience of the wind farm under Arctic black swan conditions.

Exploring Probabilistic Network-Based Modeling of Multidimensional Factors Associated with Country Risk

Assessment of country risk provides a vital source of information to organizations for expanding and globalizing their operations. Various rating agencies are involved in developing models for assessing country risk, which utilize different statistical techniques for establishing the overall impact of individual factors on country risk. The main limitation of existing studies on country risk is their limited focus on exploring the relative contribution of individual factors to country risk in a probabilistic network setting. Utilizing real data, we develop a probabilistic network model that captures dependencies among multidimensional factors associated with country risk. Further, we assess the network-wide vulnerability and resilience potential of individual factors to identify critical factors. The findings of this study provide policy-makers with some unique insights into prioritizing strategies to mitigate country risk. Further, this study provides the context for multinational enterprises to utilize the proposed methodology for prioritizing key factors associated with the relative variables of interest such as regional stability and business environment among others.

Risk Analysis: Celebrating the Accomplishments and Embracing Ongoing Challenges

As part of the celebration of the 40th anniversary of the Society for Risk Analysis and Risk Analysis: An International Journal, this essay reviews the 10 most important accomplishments of risk analysis from 1980 to 2010, outlines major accomplishments in three major categories from 2011 to 2019, discusses how editors circulate authors' accomplishments, and proposes 10 major risk-related challenges for 2020-2030. Authors conclude that the next decade will severely test the field of risk analysis.

Foundational Challenges for Advancing the Field and Discipline of Risk Analysis

Risk analysis as a field and discipline is about concepts, principles, approaches, methods, and models for understanding, assessing, communicating, managing, and governing risk. The foundation of this field and discipline has been subject to continuous discussion since its origin some 40 years ago with the establishment of the Society for Risk Analysis and the Risk Analysis journal. This article provides a perspective on critical foundational challenges that this field and discipline faces today, for risk analysis to develop and have societal impact. Topics discussed include fundamental questions important for defining the risk field, discipline, and science; the multidisciplinary and interdisciplinary features of risk analysis; the interactions and dependencies with other sciences; terminology and fundamental principles; and current developments and trends, such as the use of artificial intelligence.

Answerable and Unanswerable Questions in Risk Analysis with Open-World Novelty

Decision analysis and risk analysis have grown up around a set of organizing questions: what might go wrong, how likely is it to do so, how bad might the consequences be, what should be done to maximize expected utility and minimize expected loss or regret, and how large are the remaining risks? In probabilistic causal models capable of representing unpredictable and novel events, probabilities for what will happen, and even what is possible, cannot necessarily be determined in advance. Standard decision and risk analysis questions become inherently unanswerable ("undecidable") for realistically complex causal systems with "open-world" uncertainties about what exists, what can happen, what other agents know, and how they will act. Recent artificial intelligence (AI) techniques enable agents (e.g., robots, drone swarms, and automatic controllers) to learn, plan, and act effectively despite open-world uncertainties in a host of practical applications, from robotics and autonomous vehicles to industrial engineering, transportation and logistics automation, and industrial process control. This article offers an AI/machine learning perspective on recent ideas for making decision and risk analysis (even) more useful. It reviews undecidability results and recent principles and methods for enabling intelligent agents to learn what works and how to complete useful tasks, adjust plans as needed, and achieve multiple goals safely and reasonably efficiently when possible, despite open-world uncertainties and unpredictable events. In the near future, these principles could contribute to the formulation and effective implementation of more effective plans and policies in business, regulation, and public policy, as well as in engineering, disaster management, and military and civil defense operations. They can extend traditional decision and risk analysis to deal more successfully with open-world novelty and unpredictable events in large-scale real-world planning, policymaking, and risk management.

Toward General Principles for Resilience Engineering

Maintaining the performance of infrastructure-dependent systems in the face of surprises and unknowable risks is a grand challenge. Addressing this issue requires a better understanding of enabling conditions or principles that promote system resilience in a universal way. In this study, a set of such principles is interpreted as a group of interrelated conditions or organizational qualities that, taken together, engender system resilience. The field of resilience engineering identifies basic system or organizational qualities (e.g., abilities for learning) that are associated with enhanced general resilience and has packaged them into a set of principles that should be fostered. However, supporting conditions that give rise to such first-order system qualities remain elusive in the field. An integrative understanding of how such conditions co-occur and fit together to bring about resilience, therefore, has been less clear. This article contributes to addressing this gap by identifying a potentially more comprehensive set of principles for building general resilience in infrastructure-dependent systems. In approaching this aim, we organize scattered notions from across the literature. To reflect the partly self-organizing nature of infrastructure-dependent systems, we compare and synthesize two lines of research on resilience: resilience engineering and social-ecological system resilience. Although some of the principles discussed within the two fields overlap, there are some nuanced differences. By comparing and synthesizing the knowledge developed in them, we recommend an updated set of resilience-enhancing principles for infrastructure-dependent systems. In addition to proposing an expanded list of principles, we illustrate how these principles can co-occur and their interdependencies.

Risk, uncertainty, ignorance and myopia: Their managerial implications for B2B firms

Rare events are common: Even though any particular type of ‘rare event’ - a world war, global economic collapse, or pandemic for that matter - should only occur once every 100 years, there are enough of those types of ‘rare events’ that overall, they commonly occur about once every 10 years. As we are currently experiencing with the COVID-19 pandemic, we do not sufficiently leverage the rich toolset that risk management offers to prepare for and mitigate the resulting uncertainty. This article highlights four aspects of risk management, and their practical and theorical implications. They are: 1) Risk (in the narrower sense), where possible future outcomes can be captured through probability distributions. 2) A situation of uncertainty, where there is transparency regarding what is not known, but probability distributions are unknown, as well as causal relationships influencing the outcome in question. 3) A situation of ignorance, where there is no understanding that certain possible future developments are even relevant. And finally: 4) The emergence of organizational and inter-organizational myopia as an effect of risk, uncertainty and ignorance on collective human behaviour.

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Discusses four conditions of decreasing knowledge and their effect: risk, uncertainty, ignorance, and myopia.

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Introduces four corresponding management responses: risk management, robust decision making, resilience, and mindfulness.

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Highlights inter-organizational challenges using UK National Health Service challenges during COVID-19 pandemic.

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Summarizes open challenges, e.g. reconciliation of risk definitions and tailoring of response to specific context.

How Business Interests and Government Inaction Led to the Humidifier Disinfectant Disaster in South Korea: Implications for Better Risk Governance

Humidifier sterilizers were regarded as innovative and health-promoting products; they were widespread in South Korea until 2011. However, hospitals reported mysterious deaths, and a legal investigation in April 2016 found that hundreds of people have died due to use of the sterilizing disinfectant. This article takes up that topic, discussing the government's role in dealing with the risk regarding the humidifier disinfectant. We pay particular attention to the unequal nature of the uncertainty produced by the distorted socioeconomic structure. Through in-depth interviews with key informants and an examination of relevant documents from the government, civic groups, and newspapers, we find that the government had increasingly acknowledged the risk, yet their inaction failed to stop the high number of casualties, and they have only recently responded proactively. The uncertainty of the risk was unevenly distributed between companies, the government, experts, and citizens. We argue that the proactive and transparent role of the government with the precautionary principle could fix the unequal structure of knowledge production and preserve public health.