We explore the application of uncertainty quantification methods to agent-based models (ABMs) using a simple sheep and wolf predator-prey model. This work serves as a tutorial on how techniques like emulation can be powerful tools in this context. We also highlight the importance of advanced statistical methods in effectively utilising computationally expensive ABMs. Specifically, we implement stochastic Gaussian processes, Gaussian process classification, sequential design, and history matching to address uncertainties in model input parameters and outputs. Our results show that these methods significantly enhance the robustness, accuracy, and predictive power of ABMs.

翻译：本文通过一个简单的羊与狼捕食者-猎物模型，探讨不确定性量化方法在基于智能体模型中的应用。本工作作为教程，展示了仿真等技术在此类场景中如何成为有力工具。同时，我们强调了先进统计方法在有效利用计算成本高昂的基于智能体模型中的重要性。具体而言，我们采用随机高斯过程、高斯过程分类、序贯设计与历史匹配等方法，以处理模型输入参数与输出的不确定性。研究结果表明，这些方法显著提升了基于智能体模型的鲁棒性、精确度与预测能力。