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Confronting Deep Uncertainties in Risk Analysis

机译：面对风险分析中的深层不确定性

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摘要

How can risk analysts help to improve policy and decision making when the correct probabilistic relation between alternative acts and their probable consequences is unknown? This practical challenge of risk management with model uncertainty arises in problems from preparing for climate change to managing emerging diseases to operating complex and hazardous facilities safely. We review constructive methods for robust and adaptive risk analysis under deep uncertainty. These methods are not yet as familiar to many risk analysts as older statistical and model-based methods, such as the paradigm of identifying a single "best-fitting" model and performing sensitivity analyses for its conclusions. They provide genuine breakthroughs for improving predictions and decisions when the correct model is highly uncertain. We demonstrate their potential by summarizing a variety of practical risk management applications.
机译：当替代行为与其可能后果之间的正确概率关系未知时，风险分析师如何帮助改善政策和决策制定？模型不确定性风险管理的这一实际挑战出现在以下问题中：从为气候变化做准备到管理新出现的疾病再到安全操作复杂和危险的设施。我们回顾了在深度不确定性下进行健壮和自适应风险分析的建设性方法。这些方法尚未像旧的基于统计和模型的方法那样被许多风险分析人员所熟悉，例如识别单个“最合适”模型并对其结论进行敏感性分析的范例。当正确的模型高度不确定时，它们为改进预测和决策提供了真正的突破。我们通过总结各种实际风险管理应用程序来展示其潜力。

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