Cox Louis Anthony Tony
Cox Associates, Denver, CO 80218, USA.
Risk Anal. 2008 Dec;28(6):1749-61. doi: 10.1111/j.1539-6924.2008.01142.x. Epub 2008 Oct 16.
Several important risk analysis methods now used in setting priorities for protecting U.S. infrastructures against terrorist attacks are based on the formula: Risk = Threat x Vulnerability x Consequence. This article identifies potential limitations in such methods that can undermine their ability to guide resource allocations to effectively optimize risk reductions. After considering specific examples for the Risk Analysis and Management for Critical Asset Protection (RAMCAP) framework used by the Department of Homeland Security, we address more fundamental limitations of the product formula. These include its failure to adjust for correlations among its components, nonadditivity of risks estimated using the formula, inability to use risk-scoring results to optimally allocate defensive resources, and intrinsic subjectivity and ambiguity of Threat, Vulnerability, and Consequence numbers. Trying to directly assess probabilities for the actions of intelligent antagonists instead of modeling how they adaptively pursue their goals in light of available information and experience can produce ambiguous or mistaken risk estimates. Recent work demonstrates that two-level (or few-level) hierarchical optimization models can provide a useful alternative to Risk = Threat x Vulnerability x Consequence scoring rules, and also to probabilistic risk assessment (PRA) techniques that ignore rational planning and adaptation. In such two-level optimization models, defender predicts attacker's best response to defender's own actions, and then chooses his or her own actions taking into account these best responses. Such models appear valuable as practical approaches to antiterrorism risk analysis.
目前在美国确定保护基础设施免受恐怖袭击优先级时所使用的几种重要风险分析方法,是基于这样一个公式:风险 = 威胁×脆弱性×后果。本文指出了这些方法中可能存在的局限性,这些局限性可能会削弱它们指导资源分配以有效优化风险降低的能力。在考虑了国土安全部使用的关键资产保护风险分析与管理(RAMCAP)框架的具体示例后,我们探讨了该乘积公式更基本的局限性。这些局限性包括未能对其各组成部分之间的相关性进行调整、使用该公式估计的风险的非可加性、无法利用风险评分结果来最优地分配防御资源,以及威胁、脆弱性和后果数值固有的主观性和模糊性。试图直接评估智能对手行动的概率,而不是对他们如何根据可用信息和经验适应性地追求目标进行建模,可能会产生模糊或错误的风险估计。最近的研究表明,两级(或少数级)分层优化模型可以为风险 = 威胁×脆弱性×后果评分规则以及忽略理性规划和适应性的概率风险评估(PRA)技术提供有用的替代方法。在这种两级优化模型中,防御者预测攻击者对防御者自身行动的最佳反应,然后在考虑这些最佳反应的情况下选择自己的行动。作为反恐风险分析的实用方法,此类模型似乎很有价值。