A risk science perspective on the discussion concerning Safety I, Safety II and Safety III

Recently, there has been a discussion in the safety science community concerning the validity of basic approaches to safety, referred to as Safety I, Safety II and Safety III, with Erik Hollnagel and Nancy Leveson in leading roles. The present paper seeks to obtain new knowledge concerning the issues raised, by adding a contemporary risk science perspective to the discussion. Risk is, to a limited degree, addressed in the literature presenting and studying these three approaches; however, as argued in the paper, the concept of risk and risk analysis and management principles and methods are highly relevant and useful for understanding the three safety approaches, deciding on their suitability, and eventually applying them. The paper underlines the importance of an integration of the safety and risk sciences, to further enhance concepts, approaches, principles, methods and models for understanding, assessing, communicating and managing system performance.publishedVersio

A risk and safety science perspective on the precautionary principle

The precautionary principle is strongly debated as a policy for handling risk and safety concerns. It is commonly claimed that the principle is paralyzing, unscientific and promotes a culture of irrational fear. The risk and safety literature contains considerable work providing support for such claims but also argumentation backing the principle. The present paper aims at contributing to this discussion by investigating the principle in view of what is here referred to as contemporary risk and safety science. Common beliefs about the principle are revisited. New insights are obtained by clarifying the risk and safety fundamentals necessary to understand the principle’s motivation, applicability and limitations. The paper concludes that the precautionary principle is only relevant when the uncertainties and risks are considerable and scientific. Confusion arises, as the principle is mixed with the basic idea of risk management to give weight to uncertainties, in order to prudently handle risk. Properly understood and implemented, the precautionary principle can be aligned with decision analysis and other scientific methods.publishedVersio

On the gap between theory and practice in defining and understanding risk

The risk concept is used in all types of situations and applications, ranging from technology to medicine and security issues. Many definitions of the concept exist, and there is an ongoing discussion on what is the most suitable way of defining and understanding the concept. In recent years, several overriding frameworks have been developed, aiming at providing conceptual clarity and structure and including most of the existing definitions as special cases. A key feature of these frameworks is that uncertainty is a main component of risk. Risk science literature and recognized societies and organizations have actively promoted these frameworks and definitions. Nonetheless, applied risk analysis and management is characterized by all types of definitions and understandings of risk, many that go back to conventions made several decades ago. It can be argued that there is a considerable gap between contemporary risk science knowledge and the practice of risk analysis and risk management in these areas. This paper discusses why we have this gap, why it is important to close it and how this can be achieved. A main goal of the paper is to refute the claim that the gap is due to a disconnection between risk science and the application of risk science.publishedVersio

Implications of black swans to the foundations and practice of risk assessment and management

AbstractIn this article, we discuss how to deal with black swans in a risk context. A black swan is here understood as a surprising extreme event relative to one׳s knowledge/beliefs, and can be of different types: a) unknown unknowns, b) unknown knowns (we do not have the knowledge but others do) and c) events that are judged to have a negligible probability of occurrence and thus are not believed to occur. In the article, we review the current approaches for confronting black swans, the aim being to gain new insights by addressing the three types of black swans separately, motivated by the fact that they require different types of measures. The main conclusions of the article are that there is a need to i) extend the current risk conceptualisation and treatment frameworks to include the black swan risk, ii) develop a new generation of risk assessment and decision support methods that place more emphasis on the black swan risk and iii) better understand what analysis captures and what lies within the management domain

Risk literacy : Foundational issues and its connection to risk science

A new research area is developing, risk literacy. The term “risk literacy” basically refers to one's ability to understand and evaluate risk, in order to support and make appropriate decisions. In this article, we discuss how risk literacy relates to risk analysis/science with its topics of risk fundamentals (concepts), risk understanding, risk assessments, risk characterizations, risk perception, risk communication, and risk handling (covering risk management, risk governance, and policies on risk). We question how issues and research topics addressed in risk literacy relate to risk analysis/science knowledge, particularly on risk understanding. The main conclusion of the article is that risk literacy addresses an important topic—from both a theoretical and a practical societal relevancy perspective—and brings the potential for many additional developments and further insights if the topic is better integrated with risk science knowledge more broadly.publishedVersio

Cases of real-life policies related to risk: how can they enhance risk analysis and risk science

Policies on risk constitute a core topic of risk analysis and risk science, and it is common at risk conferences to present real-life cases of such policies, for example related to the handling of climate change and pandemics. Although these are of broad interest, showing how important issues in society are dealt with, it can be questioned to what extent and how these cases contribute to enhancing risk analysis and risk science. The present paper addresses this concern. It is argued that, in order to learn from the cases, they need in general to be more thoroughly followed up with discussions of concepts, principles, approaches, and methods for assessing, characterizing, communicating and handling risk. Describing a governmental policy on, for example, the handling of COVID-19 is a point of departure for interesting discussions concerning its justification and performance, in particular in relation to risk and the most updated knowledge from the risk analysis field. Such discussions are, however, often lacking. The paper points to some key obstacles and challenges for the learning process, including the difficulty of distinguishing between policies, policy analysis, and politics.publishedVersio

A classification system for characterizing the integrity and quality of evidence in risk studies

Risk management requires a balance between knowledge and values. Knowledge consists of justified beliefs and evidence, with evidence including data, assumptions, and models. While quality and integrity of evidence are valued in the sciences, risk science involves uncertainty, which suggests that evidence can be incomplete or imperfect. The use of inappropriate evidence can invalidate risk studies and contribute to misinformation and poor risk management decisions. Additionally, the interpretation of quality and integrity of evidence may vary by the risk study mission, decision-maker values, and stakeholder needs. While risk science has developed standards for risk studies, there remains a lack of clarity for how to demonstrate quality and integrity of evidence, recognizing that evidence can be presented in many formats (e.g., data, ideas, and theories), be leveraged at various stages of a risk study (e.g., hypotheses, analyses, and communication), and involve differing expectations across stakeholders. This study develops and presents a classification system to evaluate quality and integrity of evidence that is based on current risk science guidance, best practices from non-risk disciplines, and lessons learned from recent risk events. The classification system is demonstrated on a cyber-security application. This study will be of interest to risk researchers, risk professionals, and data analysts.publishedVersio

A risk science perspective on liability/guilt and uncertainty judgements in courts

This article aims to provide new insights about risk and uncertainty in law contexts, by incorporating ideas and principles of contemporary risk science. The main focus is on one particular aspect of the law: its operation in courts where a defendant has been charged with a violation of civil or criminal law. Judgements about risk and uncertainty—typically using the probability concept—and how these relate to the evidence play a central role in such situations. The decision on whether the defendant is liable/guilty or not may strongly depend on how these concepts are understood and communicated. Considerable work has been conducted to provide theoretical and practical foundations for the risk and uncertainty characterizations in these contexts. Yet, it can be argued that a proper foundation for linking the evidence and the uncertainty (probability) judgements is lacking, the result being poor communication in courts about risk and uncertainties. The present article seeks to clarify what the problems are and provide guidance on how to rectify them.publishedVersio

Understanding the implications of low knowledge and high uncertainty in risk studies

Risk analysis has existed for thousands of years and will continue to grow in importance across professions and industries. Of special importance is the need to understand and manage risk when there is low knowledge and high uncertainties. Even with pristine and high-quality risk analysis in these situations, integrity and credibility can be questioned, and risk events can happen. Although these issues do not prove some shortcoming in risk analysis and risk management, they can directly impact the risk analyst and decision-makers. The risk literature has addressed the issues of defining and promoting integrity and credibility for risk studies, but there is little existing guidance for the analyst when handling the commonly encountered low knowledge and high uncertainty contexts. In this article, we explore the implications of low knowledge and high uncertainty in risk studies to understand how the risk analyst can acknowledge those features in a risk study, with recognition that those features may be questioned later. The topic of this article will be of interest to risk managers, professionals, and analysts in general who are tasked with analyzing and communicating with studies.publishedVersio

A predictive Bayesian approach to risk analysis in health care

<p>Abstract</p> <p>Background</p> <p>The Bayesian approach is now widely recognised as a proper framework for analysing risk in health care. However, the traditional text-book Bayesian approach is in many cases difficult to implement, as it is based on abstract concepts and modelling.</p> <p>Methods</p> <p>The essential points of the risk analyses conducted according to the predictive Bayesian approach are identification of observable quantities, prediction and uncertainty assessments of these quantities, using all the relevant information. The risk analysis summarizes the knowledge and lack of knowledge concerning critical operations and other activities, and give in this way a basis for making rational decisions.</p> <p>Results</p> <p>It is shown that Bayesian risk analysis can be significantly simplified and made more accessible compared to the traditional text-book Bayesian approach by focusing on predictions of observable quantities and performing uncertainty assessments of these quantities using subjective probabilities.</p> <p>Conclusion</p> <p>The predictive Bayesian approach provides a framework for ensuring quality of risk analysis. The approach acknowledges that risk cannot be adequately described and evaluated simply by reference to summarising probabilities. Risk is defined by the combination of possible consequences and associated uncertainties.</p