Collaborative Research in Decision, Risk, and Management Science

确定风险分析模型的详细程度是一个 复杂技术风险分析中的重要问题 核电站、液化天然气储存等系统 设施、危险材料运输和航天飞机。 过于详细和过于简单的模型都可能导致 错误的风险评估。 然而，这一问题实际上已经 尽管风险分析在全球范围内广泛增长， 过去15年。 过去的工作，由一个调查员在这个项目上， 确定了风险分析中的三个潜在错误来源： 建模误差、参数误差和聚集误差。 不同 通过简单模型与详细模型的对比，将错误类型最小化。 在 特别是，在聚合错误的情况下，过度使用 简单的模型可能会忽略可能在更高级别上可用的信息， 详细层次。 例如，在挑战者号的时候， 灾难，仅基于23起事故中的0起事故的综合分析 每次飞行都表明可靠性不断提高， 飞行 然而，包括下列证据的分类分析表明， 23次飞行中有6次的O形环腐蚀可以得出结论， 风险仍然很大。 该项目的主要研究人员已经确定 集结误差为零的充要条件 简单的风险分析模型。 然而，这些条件是 在实践中不太可能实现。 因此，有必要 了解完美时聚合误差的决定因素 没有实现聚集。 拟议的工作将提供 关于在何种情况下会出现汇总错误的指导意见 严重，因此可以通过使用更多的 在这种情况下的详细模型。

Determining how detailed risk analysis models should be is an important question in analyzing the risks of complex technological systems such as nuclear power plants, liquid natural gas storage facilities, hazardous material transport, and the space shuttle. Both overly detailed and overly simplistic models can lead to erroneous risk estimates. However, this issue has gone virtually unstudied despite the widespread growth of risk analysis in the last 15 years. Past work by one of the investigators on this project has identified three potential sources of error in risk analyses: modeling error; parameter error; and aggregation error. Different types of errors are minimized by simple versus detailed models. In particular, in the case of aggregation error, the use of overly simple models can neglect information that may be available at more detailed levels. For example, at the time of the Challenger disaster, an aggregate analysis based only on 0 accidents in 23 flights would have indicated increasing reliability with each flight. However, a disaggregate analysis including the evidence of O-ring erosion on 6 of the 23 flights would have concluded that a substantial level of risk remained. The principal investigators for this project have identified necessary and sufficient conditions for aggregation error to vanish in simple risk analysis models. However, these conditions are unlikely to be achieved in practice. Therefore, there is a need to understand the determinants of aggregation error when perfect aggregation is not achieved. The proposed work will provide guidance on the circumstances under which aggregation error will be severe, so that large errors can be avoided through the use of more detailed models in such cases.