Vulnerability analysis is an emerging technique that discovers concise descriptions of the conditions that lead to decision-relevant outcomes (i.e., scenarios) by applying machine learning methods to a large ensemble of simulation model runs. This review organizes vulnerability analysis methods into a taxonomy and compares them in terms of interpretability, flexibility, and accuracy. Our review contextualizes interpretability in terms of five purposes for vulnerability analysis, such as adaptation systems and choosing between policies. We make recommendations for designing a vulnerability analysis that is interpretable for a specific purpose. Furthermore, a numerical experiment demonstrates how methods can be compared based on interpretability and accuracy. Several research opportunities are identified, including new developments in machine learning that could reduce computing requirements and improve interpretability. Throughout the review, a consistent example of reservoir operation policies in the Colorado River Basin illustrates the methods.

中文翻译：

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漏洞分析的目的、方法和建议的分类


漏洞分析是一种新兴技术，它通过将机器学习方法应用于大量仿真模型运行，发现导致决策相关结果（即场景）的条件的简明描述。这篇综述将漏洞分析方法组织成一个分类法，并在可解释性、灵活性和准确性方面对它们进行了比较。我们的综述根据脆弱性分析的五个目的将可解释性置于上下文中，例如适应系统和在策略之间进行选择。我们提出了设计可针对特定目的解释的漏洞分析的建议。此外，数值实验演示了如何根据可解释性和准确性对方法进行比较。确定了几个研究机会，包括机器学习的新发展，这些发展可以减少计算要求并提高可解释性。在整个审查过程中，科罗拉多河流域水库运营政策的一个一致示例说明了这些方法。