We propose a novel approach to the statistical analysis of stochastic simulation models and, especially, agent-based models (ABMs). Our main goal is to provide fully automated, model-independent and tool-supported techniques and algorithms to inspect simulations and perform counterfactual analysis. Our approach: (i) is easy-to-use by the modeller, (ii) improves reproducibility of results, (iii) optimizes running time given the modeller's machine, (iv) automatically chooses the number of required simulations and simulation steps to reach user-specified statistical confidence, and (v) automates a variety of statistical tests. In particular, our techniques are designed to distinguish the transient dynamics of the model from its steady-state behaviour (if any), estimate properties in both 'phases', and provide indications on the (non-)ergodic nature of the simulated processes - which, in turn, allows one to gauge the reliability of a steady-state analysis. Estimates are equipped with statistical guarantees, allowing for robust comparisons across computational experiments. To demonstrate the effectiveness of our approach, we apply it to two models from the literature: a large-scale macro-financial ABM and a small scale prediction market model. Compared to prior analyses of these models, we obtain new insights and we are able to identify and fix some erroneous conclusions.

翻译：我们提出了一种新颖的统计分析方法，用于分析随机仿真模型，尤其是主体基模型（ABMs）。主要目标是提供完全自动化、独立于模型且具有工具支持的技术与算法，用于检查仿真并执行反事实分析。我们的方法：（i）便于建模者使用，（ii）提高结果的可复现性，（iii）根据建模者机器优化运行时间，（iv）自动选择所需仿真次数与仿真步数，以达到用户指定的统计置信度，以及（v）自动化多种统计检验。特别是，我们的技术旨在区分模型的瞬态动力学与稳态行为（若有），估计两个“阶段”中的属性，并提供关于仿真过程（非）遍历性的指示——这反过来允许人们评估稳态分析的可靠性。估计结果附有统计保证，支持跨计算实验的稳健比较。为展示方法的有效性，我们将其应用于文献中的两个模型：一个大规模宏观金融ABM和一个规模较小的预测市场模型。与先前对这些模型的分析相比，我们获得了新的见解，并能够识别并修正部分错误结论。