Risk analysis : an official publication of the Society for Risk Analysis最新文献

In crisis situations, time is of the essence. Effective messaging to individuals at risk is critical to mitigating the most severe outcomes. Extant crisis communication literature has focused on differentiating crisis types based on perceived blame, particularly in cases of for-profit company malfeasance, but less work has been done to understand how the public makes these types of attributions. This quantitative systematic review investigates the relationship between severity of a large-scale crisis outcome and attributions of blame toward relevant entities. Moderators of interest include the attribution term used with participants (e.g., blame, responsibility), the type of crisis event, and the entity presented as at fault. Overall, a small but significant positive relationship is identified in the majority of studies between severity of a large-scale crisis outcome and attributions of blame. Results suggest that while crisis type and entity to blame are moderators, the attribution term(s) used with participants plays a less significant role. Implications and future directions are considered.

Climate change is a paradigmatic example of systemic risk. Recently, proposals for large-scale interventions-carbon dioxide removal (CDR) and solar radiation management (SRM)-have started to redefine climate governance strategies. We describe how evolving modeling practices are trending toward optimized and "best-case" projections-portraying deployment schemes that create both technically slanted and politically sanitized profiles of risk, as well as ideal objectives for CDR and SRM as mitigation-enhancing, time-buying mechanisms for carbon transitions or vulnerable populations. As promises, stylized and hopeful projections may selectively reinforce industry and political activities built around the inertia of the carbon economy. Some evidence suggests this is the emerging case for certain kinds of CDR, where the prospect of future carbon capture substitutes for present mitigation. Either of these implications are systemic: explorations of climatic futures may entrench certain carbon infrastructures. We point out efforts and recommendations to forestall this trend in the implementation of the Paris Agreement, by creating more stakeholder input and strengthening political realism in modeling and other assessments, as well as through policy guardrails.

Systemic risks, as opposed to conventional risks, bear the danger of destroying entire systems. Their understanding and governance remain a serious challenge. The phenomena of systemic risks show many analogies with those of dynamic structure generation in the systems of nature, technology, and society, including simple model systems of physics and chemistry. By analyzing these model systems, the elementary processes and the generic mechanisms by which they generate macroscopic dynamic structures become evident. Generalizing these insights makes it possible to formulate the basic framework of a theory of systemic risks with elements providing hints for adequate governance strategies. Although these insights cannot be applied to societal processes one by one, they reveal generic patterns and clusters.

This article deals with household-level flood risk mitigation. We present an agent-based modeling framework to simulate the mechanism of natural hazard and human interactions, to allow evaluation of community flood risk, and to predict various adaptation outcomes. The framework considers each household as an autonomous, yet socially connected, agent. A Beta-Bernoulli Bayesian learning model is first applied to measure changes of agents' risk perceptions in response to stochastic storm surges. Then the risk appraisal behaviors of agents, as a function of willingness-to-pay for flood insurance, are measured. Using Miami-Dade County, Florida as a case study, we simulated four scenarios to evaluate the outcomes of alternative adaptation strategies. Results show that community damage decreases significantly after a few years when agents become cognizant of flood risks. Compared to insurance policies with pre-Flood Insurance Rate Maps subsidies, risk-based insurance policies are more effective in promoting community resilience, but it will decrease motivations to purchase flood insurance, especially for households outside of high-risk areas. We evaluated vital model parameters using a local sensitivity analysis. Simulation results demonstrate the importance of an integrated adaptation strategy in community flood risk management.

Systemic risks are characterized by high complexity, multiple uncertainties, major ambiguities, and transgressive effects on other systems outside of the system of origin. Due to these characteristics, systemic risks are overextending established risk management and create new, unsolved challenges for policymaking in risk assessment and risk governance. Their negative effects are often pervasive, impacting fields beyond the obvious primary areas of harm. This article addresses these challenges of systemic risks from different disciplinary and sectorial perspectives. It highlights the special contributions of these perspectives and approaches and provides a synthesis for an interdisciplinary understanding of systemic risks and effective governance. The main argument is that understanding systemic risks and providing good governance advice relies on an approach that integrates novel modeling tools from complexity sciences with empirical data from observations, experiments, or simulations and evidence-based insights about social and cultural response patterns revealed by quantitative (e.g., surveys) or qualitative (e.g., participatory appraisals) investigations. Systemic risks cannot be easily characterized by single numerical estimations but can be assessed by using multiple indicators and including several dynamic gradients that can be aggregated into diverse but coherent scenarios. Lastly, governance of systemic risks requires interdisciplinary and cross-sectoral cooperation, a close monitoring system, and the engagement of scientists, regulators, and stakeholders to be effective as well as socially acceptable.