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On the Meaning of Assurance
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To solve real-life problems-such as those related to technology, health, security, or climate change-and make suitable decisions, risk is nearly always a main issue. Different types of sciences are often supporting the work, for example, statistics, natural sciences, and social sciences. Risk analysis approaches and methods are also commonly used, but risk analysis is not broadly accepted as a science in itself. A key problem is the lack of explanatory power and large uncertainties when assessing risk. This article presents an emerging new risk analysis science based on novel ideas and theories on risk analysis developed in recent years by the risk analysis community. It builds on a fundamental change in thinking, from the search for accurate predictions and risk estimates, to knowledge generation related to concepts, theories, frameworks, approaches, principles, methods, and models to understand, assess, characterize, communicate, and (in a broad sense) manage risk. Examples are used to illustrate the importance of this distinct/separate risk analysis science for solving risk problems, supporting science in general and other disciplines in particular.
A quantitative risk analysis (QRA) should provide a broad, informative and balanced picture of risk, in order to support decisions. To achieve this, a proper treatment of uncertainty is a prerequisite. Most approaches to treatment of uncertainty in QRA seem to be based on the thinking that uncertainty relates to the calculated probabilities and expected values. This causes difficulties when it comes to communicating what the analysis results mean, and could easily lead to weakened conclusions if large uncertainties are involved. An alternative approach is to hold uncertainty, not probability, as a main component of risk, and regard probabilities purely as epistemic-based expressions of uncertainty. In the paper the latter view is taken, and we describe what should be the main components of a risk description when following this approach. We also indicate how this approach relates to decision-making. An important issue addressed is how to communicate the shortcomings and limitations of probabilities and expected values. Sensitivity analysis plays a key role in this regard. Examples are included to illustrate ideas and findings.
The use of “levels of abstraction” in philosophical analysis (levelism) has recently come under attack. In this paper, I argue that a refined version of epistemological levelism should be retained as a fundamental method, called the method of levels of abstraction. After a brief introduction, in section “Some Definitions and Preliminary Examples” the nature and applicability of the epistemological
method of levels of abstraction is clarified. In section “A Classic Application of the Method of Abstraction”, the philosophical
fruitfulness of the new method is shown by using Kant’s classic discussion of the “antinomies of pure reason” as an example.
In section “The Philosophy of the Method of Abstraction”, the method is further specified and supported by distinguishing
it from three other forms of “levelism”: (i) levels of organisation; (ii) levels of explanation and (iii) conceptual schemes.
In that context, the problems of relativism and antirealism are also briefly addressed. The conclusion discusses some of the
work that lies ahead, two potential limitations of the method and some results that have already been obtained by applying
the method to some long-standing philosophical problems.
The concept of integrated operations (IO) introduces new ways of operations in the offshore petroleum industry. IO is often characterized by virtual decision arenas where many safety critical decisions are supported and made by distributed actors with different rationalities and responsibilities. This will challenge more traditional decision processes in several ways and it has been questioned whether the risk analysis framework can handle what some authors denote as emerging and escaping risks. Complexity is often considered as a source of such risks. In this paper risk is defined as uncertainty regarding occurrence and severity of undesired events. Next a variety of techniques for structuring and quantifying uncertainty are listed. To approach complexity it is proposed to identify a set of complexity characteristics in relation to the accidental scenarios to be undertaken in the analysis. This enables uncertainty due to complexity to be approached within the same framework as that used to cope with other sources of uncertainty. The important steps in such an integrated risk and complexity analysis are listed, and some of these steps are discussed in the light of examples relevant to IO.
In recent years several authors have argued for the adoption of certain new types of risk perspectives which highlight uncertainties rather than probabilities in the way risk is understood and measured. The theoretical rationale for these new perspectives is well established, but the practical implications have not been so clearly demonstrated. There is a need to show how the new perspectives change the way risk is described and communicated in real-life situations and in its turn the effects on risk management and decision making. The present paper aims at contributing to this end by considering two cases, related to a national risk level, and a specific analysis concerning an LNG plant. The paper concludes that the new risk perspectives influence the current regime in many ways, in particular the manner in which the knowledge dimension is described and dealt with. Two methods for characterising the strength of knowledge are presented, one of them based on a new concept, the “assumption deviation risk”, reflecting risks related to the deviations from the conditions/states defined by the assumption made.
This paper reviews the definition and meaning of the concept of risk. The review has a historical and development trend perspective, also covering recent years. It is questioned if, and to what extent, it is possible to identify some underlying patterns in the way risk has been, and is being understood today. The analysis is based on a new categorisation of risk definitions and an assessment of these categories in relation to a set of critical issues, including how these risk definitions match typical daily-life phrases about risk. The paper presents a set of constructed development paths for the risk concept and concludes that over the last 15–20 years we have seen a shift from rather narrow perspectives based on probabilities to ways of thinking which highlight events, consequences and uncertainties. However, some of the more narrow perspectives (like expected values and probability-based perspectives) are still strongly influencing the risk field, although arguments can be provided against their use. The implications of this situation for risk assessment and risk management are also discussed.
A central theme throughout the impressive series of philosophical books and articles Stephen Toulmin has published since 1948 is the way in which assertions and opinions concerning all sorts of topics, brought up in everyday life or in academic research, can be rationally justified. Is there one universal system of norms, by which all sorts of arguments in all sorts of fields must be judged, or must each sort of argument be judged according to its own norms? In The Uses of Argument (1958) Toulmin sets out his views on these questions for the first time. In spite of initial criticisms from logicians and fellow philosophers, The Uses of Argument has been an enduring source of inspiration and discussion to students of argumentation from all kinds of disciplinary background for more than forty years.
A quantitative definition of risk is suggested in terms of the idea of a “set of triplets”. The definition is extended to include uncertainty and completeness, and the use of Bayes' theorem is described in this connection. The definition is used to discuss the notions of “relative risk”, “relativity of risk”, and “acceptability of risk”.
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