A novel probabilistic approach to counterfactual reasoning in system safety
A novel probabilistic approach to counterfactual reasoning in system safety
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DOI：
10.1016/j.ress.2022.108785
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发表时间：
2022-08
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通讯作者：
Andres Ruiz-Tagle;E. Droguett;K. Groth
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作者：
Andres Ruiz-Tagle;E. Droguett;K. Groth
安全关键型系统不能等待来自多个严重后果事件的数据可用，以便为安全建议提供信息。反事实推理已被广泛用于系统安全，以解决这个问题，使从单一事件的证据与分析师的系统的当前知识，从过去的事件中学习。然而，目前的反事实方法被批评为使分析师倾向于线性化和过度简化复杂事件。为了克服这些局限性，这项工作建立了一个新的概率方法来反事实推理称为“可能世界”反事实。这种方法能够将分析师关于系统的因果知识（以基于贝叶斯网络的风险评估模型的形式）与关于感兴趣事件的最佳可用证据（例如，意外）。因此，通常用于系统安全实践的反事实假设现在可以通过因果可靠的概率方法进行严格评估。我们通过对2018年太阳草原天然气爆炸的真实案例研究，展示了“可能世界”反事实的能力，并展示了这种方法如何提供超出当局在事件发生时提供的额外经验教训和见解。
Safety–critical systems cannot afford to wait for data from multiple high-consequence events to become available in order to inform safety recommendations. Counterfactual reasoning has been widely used in system safety to address this issue, enabling the incorporation of evidence from single events with an analyst’s current knowledge of a system to learn from past events. However, current counterfactual methods have been criticized for making analysts prone to linearizing and oversimplifying complex events. In order to overcome these limitations, this work establishes a novel probabilistic approach to counterfactual reasoning called “possible worlds” counterfactuals. This methodology enables the integration of an analyst’s causal knowledge about a system (in the form of a Bayesian network-based risk assessment model) with the best available evidence about an event of interest (e.g., an accident). As a result, counterfactual hypotheses, commonly used in the practice of system safety, can now be rigorously assessed through causally-sound probabilistic methods. We demonstrate the capabilities of “possible worlds” counterfactuals with a real-world case study on the 2018 Sun Prairie gas explosion and show how this approach can provide additional lessons and insights beyond those provided by authorities at the time of the event.