Center for Risk Management of Engineering Systems, University of Virginia, PO Box 400736, Charlottesville, VA 22904, USA.
Risk Anal. 2009 Dec;29(12):1647-54. doi: 10.1111/j.1539-6924.2009.01310.x. Epub 2009 Nov 23.
The premise of this article is that risk to a system, as well as its vulnerability and resilience, can be understood, defined, and quantified most effectively through a systems-based philosophical and methodological approach, and by recognizing the central role of the system states in this process. A universally agreed-upon definition of risk has been difficult to develop; one reason is that the concept is multidimensional and nuanced. It requires an understanding that risk to a system is inherently and fundamentally a function of the initiating event, the states of the system and of its environment, and the time frame. In defining risk, this article posits that: (a) the performance capabilities of a system are a function of its state vector; (b) a system's vulnerability and resilience vectors are each a function of the input (e.g., initiating event), its time of occurrence, and the states of the system; (c) the consequences are a function of the specificity and time of the event, the vector of the states, the vulnerability, and the resilience of the system; (d) the states of a system are time-dependent and commonly fraught with variability uncertainties and knowledge uncertainties; and (e) risk is a measure of the probability and severity of consequences. The above implies that modeling must evaluate consequences for each risk scenario as functions of the threat (initiating event), the vulnerability and resilience of the system, and the time of the event. This fundamentally complex modeling and analysis process cannot be performed correctly and effectively without relying on the states of the system being studied.
本文的前提是，通过基于系统的哲学和方法方法，以及通过认识到系统状态在这一过程中的核心作用，可以最有效地理解、定义和量化系统面临的风险及其脆弱性和弹性。人们一直难以为风险制定一个普遍认可的定义；原因之一是这个概念具有多维性和细微差别。它需要理解到，系统面临的风险从本质上说是启动事件、系统及其环境的状态以及时间框架的函数。在定义风险时，本文提出：(a) 系统的性能能力是其状态向量的函数；(b) 系统的脆弱性和弹性向量分别是输入（例如启动事件）、发生时间以及系统状态的函数；(c) 后果是事件的特异性和时间、状态向量、脆弱性和系统弹性的函数；(d) 系统的状态是随时间变化的，通常充满了可变性不确定性和知识不确定性；(e) 风险是后果的概率和严重程度的度量。这意味着，建模必须将每种风险情景的后果评估为威胁（启动事件）、系统的脆弱性和弹性以及事件时间的函数。如果不依赖于所研究系统的状态，就无法正确有效地进行这种基本复杂的建模和分析过程。
