Surrogate modeling and active subspaces have emerged as powerful paradigms in computational science and engineering. Porting such techniques to computational models in the social sciences brings into sharp relief their limitations in dealing with discontinuous simulators, such as Agent-Based Models, which have discrete outputs. Nevertheless, prior applied work has shown that surrogate estimates of active subspaces for such estimators can yield interesting results. But given that active subspaces are defined by way of gradients, it is not clear what quantity is being estimated when this methodology is applied to a discontinuous simulator. We begin this article by showing some pathologies that can arise when conducting such an analysis. This motivates an extension of active subspaces to discontinuous functions, clarifying what is actually being estimated in such analyses. We also conduct numerical experiments on synthetic test functions to compare Gaussian process estimates of active subspaces on continuous and discontinuous functions. Finally, we deploy our methodology on Flee, an agent-based model of refugee movement, yielding novel insights into which parameters of the simulation are most important across 8 displacement crises in Africa and the Middle East.

翻译：替代建模与活性子空间已成为计算科学与工程领域的重要范式。将这些技术迁移至社会科学计算模型时，其处理基于智能体模型等具有离散输出的不连续仿真器的局限性凸显出来。然而，先前的应用研究表明，对此类估计器进行活性子空间的替代估计可产生有趣结果。但由于活性子空间通过梯度定义，尚不明确将该方法应用于不连续仿真器时实际估计的定量目标。本文首先展示此类分析中可能出现的病态现象，进而推动将活性子空间扩展至不连续函数，阐明此类分析中实际估计的物理量。我们还在合成测试函数上进行数值实验，比较高斯过程估计在连续与不连续函数上的活性子空间性能。最终，我们将该方法应用于难民迁移的基于智能体模型Flee，在非洲与中东的8个流离失所危机案例中，揭示了仿真参数重要性的全新见解。