On How to Deal with Deep Uncertainties in a Risk Assessment and Management Context

Recently, several authors have presented interesting contributions on how to meet deep or severe uncertainties in a risk analysis setting. In this article, we provide some reflections on some of the foundational pillars that this work is based on, including the meaning of concepts such as deep uncertainty, known probabilities, and correct models, the aim being to contribute to a strengthening of the scientific platform of the work, as well as providing new insights on how to best implement management policies meeting these uncertainties. We also provide perspectives on the boundaries and limitations of analytical approaches for supporting decision making in cases of deep uncertainties. A main conclusion of the article is that deep uncertainties call for managerial review and judgment that sees beyond the analytical frameworks studied in risk assessment and risk management contexts, including those now often suggested to be used, such as robust optimization techniques. This managerial review and judgment should be seen as a basic element of the risk management.     

Foundational Issues in Risk Assessment and Risk Management

This is a perspective article on foundational issues in risk assessment and management. The aim is to discuss the needs, obstacles, and challenges for the establishment of a renewed, strong scientific foundation for risk assessment and risk management suited for the current and future technological challenges. The focus is on (i) reviewing and discussing the present situation and (ii) identifying how to best proceed in the future, to develop the risk discipline in the directions needed. The article provides some reflections on the interpretation and understanding of the concept of “foundations of risk assessment and risk management” and the challenges therein. One main recommendation is that different arenas and moments for discussion are needed to specifically address foundational issues in a way that embraces the many disciplinary communities involved (from social scientists to engineers, from behavioral scientists to statisticians, from health physicists to lawyers, etc.). One such opportunity is sought in the constitution of a novel specialty group of the Society of Risk Analysis.     

Foundational Issues in Risk Assessment and Risk Management

In spite of the maturity reached by many of the methods used in risk assessment and risk management, broad consensus has not been established on fundamental concepts and principles. The risk fields still suffer from a lack of clarity on many key scientific pillars. The purpose of this article is to point to this situation and through some illustrating examples discuss the challenges that the fields here face. Moreover, the purpose of the article is to reflect on how to improve the present situation and enhance the risk fields. We argue that the establishment of some common scientific pillars as well as a strong and continuous research focus on foundational issues are critical success factors. The article specifically addresses the role of the peer‐reviewed journals and the international standards in the fields. We hope that the article can contribute to a revitalization of the discussion of foundational issues in risk assessment and risk management.     

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.     

The Role of Decision Analysis in Risk Analysis: A Retrospective

In commemorating the 40th anniversary of Risk Analysis, this article takes a retrospective look at some of the ways in which decision analysis (as a “sibling field”) has contributed to the development both of the journal, and of risk analysis as a field. I begin with some early foundational papers from the first decade of the journal's history. I then review a number of papers that have applied decision analysis to risk problems over the years, including applications of related methods such as influence diagrams, multicriteria decision analysis, and risk matrices. The article then reviews some recent trends, from roughly the last five years, and concludes with observations about the parallel evolution of risk analysis and decision analysis over the decades—especially with regard to the importance of representing multiple stakeholder perspectives, and the importance of behavioral realism in decision models. Overall, the extensive literature surveyed here supports the view that the incorporation of decision-analytic perspectives has improved the practice of risk analysis.     

The Architecture of Dynamic Capability Research Identifying the Building Blocks of a Configurational Approach

The dynamic capability view (DCV) of the firm has become one of the leading frameworks aimed at identifying drivers of long-term firm survival and growth. Yet, despite considerable academic interest, there are many questions about what dynamic capabilities (DCs) are, how they relate to other organizational operations, and how they relate to firm performance. In this article, we provide a unique and comprehensive examination of the DCV literature that goes beyond past reviews by combining text-based analysis with surveys of, and interviews with, researchers in the field. With this approach, we are able to examine the evolution of the DCV in written literature and identify missing research themes. Based on this review, we argue that future research will benefit from integrating the DCV with configuration theory and the recent micro foundational thinking. We encapsulate this discussion via an architectural model of the DCV (entitled “House of Dynamic Capabilities”) that combines micro foundations underlying DCs at the varying levels of analysis (individual, business unit, and organizational) while also accounting for important enablers of DCs and firm strategic orientation. We also show how this logic requires a completely different set of methodological approaches to those currently in use.     

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).     

Concerns,Challenges, and Directions of Development for the Issue of Representing Uncertainty in Risk Assessment

In the analysis of the risk associated to rare events that may lead to catastrophic consequences with large uncertainty, it is questionable that the knowledge and information available for the analysis can be reflected properly by probabilities. Approaches other than purely probabilistic have been suggested, for example, using interval probabilities, possibilistic measures, or qualitative methods. In this article, we look into the problem and identify a number of issues that are foundational for its treatment. The foundational issues addressed reflect on the position that “probability is perfect” and take into open consideration the need for an extended framework for risk assessment that reflects the separation that practically exists between analyst and decisionmaker.     

Organizational Sensemaking About Risk Controls: The Case of Offshore Hydrocarbons Production

In the same way that individuals’ risk perceptions can influence how they behave toward risks, how organizational members make sense of risk controls is an important influence on how they apply and maintain such controls. In this article, we describe an analysis of sensemaking about the control of risk in offshore hydrocarbons production, an industry that continues to produce disasters of societal significance. A field study of 80 interviews was conducted in five offshore oil and gas companies and the agency that regulates them. The interviews were analyzed using qualitative template analysis. This provided a categorization of the many ways of acting through which informants made sense of the risk control task, and indicated that the organizations placed substantially different emphases on different ways of acting. Nevertheless, this sensemaking fell into two broad classes: that which tended to limit or be pessimistic about organizational controls, and that which tended to extend or be optimistic about organizational controls. All the participating organizations collectively placed a balanced emphasis on these two classes. We argue that this balanced sensemaking is an adaptation rather than a deliberate choice, but that it is an important element of controlling risk in its own right.     

Introducing Fear of Crime to Risk Research

This article introduces the fear of crime to risk research, noting a number of areas for future interdisciplinary study. First, the article analyzes both the career of the concept of fear of crime and the politics of fear. Second, it considers research and theory on the psychology of risk, particularly the interplay between emotion and cognition, and what might be called the risk as image perspective. Third, the article speculates how people learn about risk and suggests how to customize a social amplification of risk framework to fear of crime. Finally, the article advances the argument that fear of crime may be an individual response to community social order and a generalized attitude toward the moral trajectory of society. Each of these areas of discussion has implications for future theoretical developments within risk research; each highlights how risk research can contribute to the social scientific understanding of an important issue of the day.     

Conjoint effects of interacting strategy-making processes and lines of defense practices in strategic Risk Management: An empirical study

The ability to contain adverse effects of major risks under turbulent conditions and exploit the opportunities they present are fundamental concerns in strategic management and various institutions promote enterprise risk management (ERM) to deal with these challenges. Yet, our knowledge about how ERM affects performance and interacts with corporate strategy-making processes is limited. The ERM frameworks impose first and second lines of defense practices to integrate business operations and corporate risk oversight. Emergent strategies generate responsive initiatives and strategic planning coordinates updated actions. Hence, this study analyzes the conjoint effects of these ERM practices and strategy-making processes based on a large corporate sample and finds that ERM practices depend on strategy-making to attain effective risk outcomes. The application of ERM frameworks can, therefore, not be assessed in isolation, but must consider corporate strategy-making. This has implications for the way we conduct research on strategic risk management, how executives approach risk oversight and policy-makers impose formal risk governance requirements.     

The Ethical Foundations of Risk Analysis

In the field of risk analysis, the normative value systems underlying accepted methodology are rarely explicitly discussed. This perspective provides a critique of the various ethical frameworks that can be used in risk assessments and risk management decisions. The goal is to acknowledge philosophical weaknesses that should be considered and communicated in order to improve the public acceptance of the work of risk analysts.     

On the Meaning and Use of the Risk Appetite Concept

The risk appetite concept has been given considerable attention recently in enterprise risk management contexts. A number of definitions exist, most with a link to risk acceptability, but also values and goals. The usefulness of the concept is, however, disputed; some authors argue that we can in fact do better without it. In this article, we provide a thorough discussion of what the risk appetite concept is actually trying to express and how it best can be used in the relevant decision making. The main purposes of the article are (i) to argue that the risk appetite concept, suitably interpreted, has a role to play in risk management, (ii) to show that the risk appetite concept is well supported by some types of risk perspectives and not by others, and (iii) to show how the risk appetite concept is linked to other related concepts, such as risk seeking and risk acceptability. The risk perspectives studied range from expected value and probability based definitions of risk to views on risk, that are founded on uncertainties.     

Supply Network Structure,Visibility, and Risk Diffusion: A Computational Approach

Understanding and managing supply chain risks is a critical functional competency for today's global enterprises. A lack of this competency can have significant negative outcomes, including costly production and delivery delays, loss of future sales, and a tarnished corporate image. The ability to identify and mitigate risks, however, is complicated as supply chains are becoming increasingly global, complex, and interconnected. Drawing on the complex systems and epidemiology literature, and using a computational modeling and network analysis approach, we examine the impact of global supply network structure on risk diffusion and supply network health and demonstrate the importance of supply network visibility. Our results show a significant association between network structure and both risk diffusion and supply network health. In particular, our results indicate that small‐world supply network topologies consistently outperform supply networks with scale‐free characteristics. Theoretically, our study contributes to our understanding of risk management and supply networks as complex networked systems using a computational approach. Managerially, our study illustrates how decision makers can benefit from a network analytic approach to develop a more holistic understanding of system‐wide risk diffusion and to guide network governance policies for more favorable health level outcomes. The article concludes by highlighting the main findings and discussing possibilities of future research directions.     

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

In recent years calls have been made for a shift from risk to resilience. The basic idea is that we need to be prepared when threatening events occur, whether they are anticipated or unforeseen. This article questions the extent to which this call will have and should have implications for the risk field and science. Is the call based on a belief that this field and science should be replaced by resilience analysis and management, or is it more about priorities: Should more weight be placed on improving resilience? The article argues that the only meaningful interpretation of the call is the latter. Resilience analysis and management is today an integrated part of the risk field and science, and risk analysis in a broad sense is needed to increase relevant knowledge, develop adequate policies, and make the right decisions, balancing different concerns and using our limited resources in an effective way.     

An Integrated Physical Dispersion and Behavioral Response Model for Risk Assessment of Radiological Dispersion Device (RDD) Events

A radiological dispersion device (RDD) or "dirty" bomb is a conventional explosive wrapped in radiological material. Terrorists may use an RDD to disperse radioactive material across a populated area, causing casualties and/or economic damage. Nearly all risk assessment models for RDDs make unrealistic assumptions about public behavior in their health assessments, including assumptions that the public would stand outside in a single location indefinitely. In this article, we describe an approach for assessing the risks of RDD events incorporating both physical dispersion and behavioral response variables. The general approach is tested using the City of Pittsburgh, Pennsylvania as a case study. Atmospheric models simulate an RDD attack and its likely fallout, while radiation exposure models assess fatal cancer risk. We model different geographical distributions of the population based on time of day. We evaluate aggregate health impacts for different public responses (i.e., sheltering-in-place, evacuating). We find that current RDD models in use can be improved with the integration of behavioral components. Using the results from the model, we show how risk varies across several behavioral and physical variables. We show that the best policy to recommend to the public depends on many different variables, such as the amount of trauma at ground zero, the capability of emergency responders to get trauma victims to local hospitals quickly and efficiently, how quickly evacuations can take place in the city, and the amount of shielding available for shelterers. Using a parametric analysis, we develop behaviorally realistic risk assessments, we identify variables that can affect an optimal risk reduction policy, and we find that decision making can be improved by evaluating the tradeoff between trauma and cancer fatalities for various RDD scenarios before they occur.     

Probability of Causation and the Attributable Proportion Risk

A uranium miner who smokes develops lung cancer: what is the probability that radiation, rather than tobacco, caused it? This paper briefly explains the principles and limits of probability models for which this question makes sense, and then shows how principles of risk accounting can be applied to obtain a solution to the general problem of attributing risk in the presence of joint, possibly interacting, causes. A procedure for calculating each factor's “share” in a jointly caused risk is proposed, and shown to be a generalization of the “probability of causation” concept. Problems of implementation and interpretation for the proposed attribution procedure are discussed, and illustrative error bounds are derived for a simple decision rule, in which probability of causation or attributable risk share calculations are made using aggregate data as a proxy for unknown individual data.     

Mental Models in Risk Assessment: Informing People About Drugs

One way to communicate about the risks of drugs is through the use of package inserts. The problems associated with this medium of informing patients have been investigated by several researchers who found that people require information about drugs they are using, including extensive risk information, and that they are willing to take this information into account in their usage of drugs. But empirical results also show that people easily misinterpret the information given. A conceptual framework is proposed that might be used for better understanding the cognitive processes involved in such a type of risk assessment and communication. It is based on the idea that people develop, through experience, a mental model of how a drug works, which effects it might produce, that contraindications have to be considered, etc. This mental model is "run" when a specific package insert has been read and a specific question arises such as, for example, whether certain symptoms can be explained as normal or whether they require special attention and action. We argue that the mental model approach offers a useful perspective for examining how people understand package inserts, and consequently for improving their content and design. The approach promises to be equally useful for other aspects of risk analysis that are dependent upon human judgment and decision making, e.g., threat diagnosis and human reliability analysis.     

On Funtowicz and Ravetz's “Decision Stake—System Uncertainties” Structure and Recently Developed Risk Perspectives

The Funtowicz and Ravetz model for classifying problem‐solving strategies into applied sciences, professional consultancy, and postnormal sciences is well known in the social science risk literature. The model is illustrated in a diagram based on the two axes: (i) decision stakes—the value dimension (costs, benefits) and (ii) the system uncertainties—the knowledge dimension. These axes resemble the same two dimensions that characterize some recently developed risk perspectives: (a) consequences and the severity of these consequences and (b) associated uncertainties. In this article, we make a detailed comparison of these two types of risk frameworks. We point to similarities and differences in motivation and use. A main conclusion of the article is that these risk perspectives all provide adequate scientific bases for the Funtowicz and Ravetz model. New insights are provided on the understanding of what the outcome stakes/consequences and uncertainty dimensions really capture in these perspectives and frameworks.