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Probabilistic Risk Assessment (PRA) has proven to be an invaluable tool for evaluating risks in complex engineered systems. However, there is increasing concern that PRA may not be adequate in situations with little underlying knowledge to support probabilistic representation of uncertainties. As analysts and policy makers turn their attention to deeply uncertain hazards such as climate change, a number of alternatives to traditional PRA have been proposed. This paper systematically compares three diverse approaches for risk analysis under deep uncertainty (qualitative uncertainty factors, probability bounds, and robust decision making) in terms of their representation of uncertain quantities, analytical output, and implications for risk management. A simple example problem is used to highlight differences in the way that each method relates to the traditional risk assessment process and fundamental issues associated with risk assessment and description. We find that the implications for decision making are not necessarily consistent between approaches, and that differences in the representation of uncertain quantities and analytical output suggest contexts in which each method may be most appropriate. Finally, each methodology demonstrates how risk assessment can inform decision making in deeply uncertain contexts, informing more effective responses to risk problems characterized by deep uncertainty. [ABSTRACT FROM AUTHOR]

Animals can shape their timed behaviors based on experienced probabilistic relations in a nearly optimal fashion. On the other hand, it is not clear if they adopt these timed decisions by making computations based on previously learnt task parameters (time intervals, locations, and probabilities) or if they gradually develop their decisions based on trial and error. To address this question, we tested mice in the timed-switching task, which required them to anticipate when (after a short or long delay) and at which of the two delay locations a reward would be presented. The probability of short trials differed between test groups in two experiments. Critically, we first trained mice on relevant task parameters by signaling the active trial with a discriminative stimulus and delivered the corresponding reward after the associated delay without any response requirement (without inducing switching behavior). During the test phase, both options were presented simultaneously to characterize the emergence and temporal characteristics of the switching behavior. Mice exhibited timed-switching behavior starting from the first few test trials, and their performance remained stable throughout testing in the majority of the conditions. Furthermore, as the probability of the short trial increased, mice waited longer before switching from the short to long location (experiment 1). These behavioral adjustments were in directions predicted by reward maximization. These results suggest that rather than gradually adjusting their time-dependent choice behavior, mice abruptly adopted temporal decision strategies by directly integrating their previous knowledge of task parameters into their timed behavior, supporting the model-based representational account of temporal risk assessment. [ABSTRACT FROM AUTHOR]

This article draws on an ethnographic research project that examined how New Zealand child protection social workers discussed decision making about reported risks for children. The study involved a combination of detailed interviews and observations of social work practice in social work offices from late 2002 to mid 2003. In this article, I show how the initial responses by social workers in statutory child protective services were filtered through everyday heuristics about risk as they reasoned with harm and danger probabilities. Social workers who participated in this study tended to treat risk as something that needed to be known early on in the case trajectory. By retaining a focus on what was known and recorded and, therefore, could be identified and used as the basis of interventions, social workers effectively excluded the possibility of a challenge from alternate views or positions about risk. Importantly, this approach to risk prevented social workers, their supervisors and colleagues from exploring the ways in which risk was constructed and used. Thus, they did not consider the extent to which social workers decisions, such as those to remove a child exposed families to other types of risk, to the potential harm of placing children in alternative or foster care. [ABSTRACT FROM PUBLISHER]

Abstract: In engineering risk assessments, probability is the common tool used to describe the epistemic uncertainties about unknown quantities. Probability is considered a main component of risk. However, a number of alternative approaches exist for representing and describing uncertainties in risk assessments, including possibility theory and evidence theory. For these approaches and theories a probability-based risk definition cannot in general serve as a conceptual framework for risk assessments. A broader risk perspective is required. The purpose of the present paper is to present such a perspective and show how both the probabilistic and the alternative approaches and theories can be supported by this perspective. The key feature of this perspective is that uncertainty replaces probability in the definition of risk. [Copyright &y& Elsevier]