An uncertainty-based risk perspective on risk perception and communication: Opportunities for new empirical-based research.

Risk perception and communication research has to a large extent discussed risk issues on the basis of an understanding of risk as probabilities and expected values. The present article aims at providing some reflections on this research when adopting an uncertainty-based risk perspective, in line with contemporary conceptualizations of risk. This type of perspective sees uncertainty, rather than probability, as a main component of risk, in addition to the consequences and the severity of these consequences.

Risk science and politics: What is and should be the relationship?

Risk science is the most updated and justified knowledge-in the form of concepts, principles, approaches, methods, and models-for understanding, assessing, characterizing, communicating, and handling risk, with applications. It is also about the practice that gives us this knowledge. It is commonly stated that risk science is politically neutral.

On the use of the term "real risk".

The term "real risk" and variations of this term are commonly used in everyday speech and writing, and in the scientific literature. There are mainly two types of use: i) in statements about what the real risk related to an activity is and ii) in statements about the risk related to an activity being real. The former type of use has been extensively discussed in the literature, whereas the latter type has received less attention.

Artificial intelligence for risk analysis-A risk characterization perspective on advances, opportunities, and limitations.

Artificial intelligence (AI) has seen numerous applications for risk analysis and provides ample opportunities for developing new and improved methods and models for this purpose. In the present article, we conceptualize the use of AI for risk analysis by framing it as an input-algorithm-output process and linking such a setup to three tasks in establishing a risk description: consequence characterization, uncertainty characterization, and knowledge management. We then give an overview of currently used concepts and methods for AI-based risk analysis and outline potential future uses by extrapolating beyond currently produced types of output.

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.

Risk science applied to major risk events in history.

Major risk events in history are often labeled as black swans or as unforeseeable given the risk policies and procedures existing at the time. Hindsight suggests that many of these events could have been foreseeable. This article explores past risk events, (1) analyzes how risk science principles apply to those events, and (2) studies gaps and opportunities for risk science using the lenses of consequences, uncertainty, and knowledge as they relate to evidence used for risk assessment prior to the risk event.

A risk science perspective on vaccines.

Vaccines can be seen as one of the greatest successes in modern medicine. Good examples are the vaccines against smallpox, polio, and measles. Unfortunately, vaccines can have side effects, but the risks are considered by the health authorities and experts to be small compared to their benefits.

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).

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.

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.

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.

Risk analysis under attack: How risk science can address the legal, social, and reputational liabilities faced by risk analysts.

The role of the risk analyst is critical in understanding and managing uncertainty. However, there is another type of uncertainty that is rarely discussed: The legal, social, and reputational liabilities of the risk analyst. Recent events have shown that professionals participating in risk analysis can be held personally liable.

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.

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.

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.

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.

Risk Analysis in the Age of Big Data: The Promises and Pitfalls.

Despite its rising popularity, the novelty and merits of big data risk analysis are still debated. This perspective article contributes to the debate by clarifying what constitutes big data in the context of risk analysis and proposing that the discussions of big data attributes (i.e.

A Risk-Science Approach to Vulnerability Classification.

Advancements in the risk literature and recent events have highlighted the need for recognizing and managing system vulnerabilities. However, established definitions of vulnerability typically involve only static concepts that are limited to measurement of system characteristics. Advancements in risk modeling, combined with the dynamic nature of data availability, and processing call for the need to understand the various dimensions and time-dependent properties of vulnerability within risk-informed decision making.

Globalization and global risk: How risk analysis needs to be enhanced to be effective in confronting current threats.

In the last 20-30 years, technological innovation has enabled the advancement of industry at a global scale, giving rise to a truly global society, resting on an interdependent web of transnational technical, economic and social systems. These systems are exposed to scenarios of cascading outbreaks, whose impacts can ripple to very large scales through their strong interdependencies, as recently shown by the pandemic spreading of the Coronavirus. Considerable work has been conducted in recent years to develop frameworks to support the assessment, communication, management and governance of this type of risk, building on concepts like systemic risks, complexity theory, deep uncertainties, resilience engineering, adaptive management and black swans.

Creating conditions for critical trust - How an uncertainty-based risk perspective relates to dimensions and types of trust.

Although some disagreement about the strength of the relationship, it is generally agreed within risk research, that trust plays a central role in shaping risk perception and risk responses. Over recent decades, risk managing institutions have experienced what by many has been described as a decline in public trust. Strategies like stakeholder involvement and communication of scientific uncertainties are increasingly implemented to rebuild levels of trust but often prove less effective.

Risk Science Contributions: Three Illustrating Examples.

This article aims to demonstrate that risk science is important for society, industry and all of us. Rather few people today, including scientists and managers, are familiar with what this science is about-its foundation and main features-and how it is used to gain knowledge and improve communication and decision making in real-life situations. The article seeks to meet this challenge, by presenting three examples, showing how risk science works to gain new generic, fundamental knowledge on risk concepts, principles, and methods, as well as supporting the practical tackling of actual risk problems.

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.