函数型响应的全局敏感性与领域选择性测试：在气候经济模型中的应用
Global Sensitivity and Domain-Selective Testing for Functional-Valued Responses: An Application to Climate Economy Models
AI总结：
本文扩展了全局敏感性分析方法，提出一种领域选择性推断技术，以处理具有有限变化输入的随机函数输出，并在模拟与真实数据中验证了其在检测气候经济模型敏感性模式时间动态方面的稳健性与有效性。
摘要：
理解气候变化的动态与演变及其相关不确定性，是设计稳健政策行动的关键。计算机模型是这一科学努力中的关键工具，现已达到高度的成熟与复杂程度。为了更好地理解其结果，并应对这类模型在内部运作机制上日益不透明的问题，需要进行模型审计。当前的技术，如全局敏感性分析（GSA），仅限于处理多元输出、随机输出或有限变化输入。这限制了它们对时变变量（如温室气体的未来路径）的适用性。为了在模型集合的分析中提供额外的语义，我们提供了一种 GSA 方法的扩展，以处理具有有限变化输入的随机函数输出。为了处理有限变化输入和函数输出，我们提出了对现有 GSA 方法的扩展，同时通过引入一种新颖的、用于敏感性指数的领域选择性推断技术来处理随机部分。我们通过一项模拟研究探讨了我们的方法，该研究显示了其在检测敏感性模式方面的稳健性和有效性。我们将其应用于真实世界数据，其功能可以为从业者与政策制定者提供关于敏感性模式时间动态的额外信息，以及关于稳健性的信息。
英文摘要：
Understanding the dynamics and evolution of climate change and associated uncertainties is key for designing robust policy actions. Computer models are key tools in this scientific effort, which have now reached a high level of sophistication and complexity. Model auditing is needed in order to better understand their results, and to deal with the fact that such models are increasingly opaque with respect to their inner workings. Current techniques such as Global Sensitivity Analysis (GSA) are limited to dealing either with multivariate outputs, stochastic ones, or finite-change inputs. This limits their applicability to time-varying variables such as future pathways of greenhouse gases. To provide additional semantics in the analysis of a model ensemble, we provide an extension of GSA methodologies tackling the case of stochastic functional outputs with finite change inputs. To deal with finite change inputs and functional outputs, we propose an extension of currently available GSA methodologies while we deal with the stochastic part by introducing a novel, domain-selective inferential technique for sensitivity indices. Our method is explored via a simulation study that shows its robustness and efficacy in detecting sensitivity patterns. We apply it to real world data, where its capabilities can provide to practitioners and policymakers additional information about the time dynamics of sensitivity patterns, as well as information about robustness.
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