Agent-based modelling (ABM) is a widespread approach to simulate complex systems. Advancements in computational processing and storage have facilitated the adoption of ABMs across many fields; however, ABMs face challenges that limit their use as decision-support tools. A significant issue is parameter estimation in large-scale ABMs, particularly due to computational constraints on exploring the parameter space. This study evaluates a state-of-the-art simulation-based inference (SBI) framework that uses neural networks (NN) for parameter estimation. This framework is applied to an established labour market ABM based on job transition networks. The ABM is initiated with synthetic datasets and the real U.S. labour market. Next, we compare the effectiveness of summary statistics derived from a list of statistical measures with that learned by an embedded NN. The results demonstrate that the NN-based approach recovers the original parameters when evaluating posterior distributions across various dataset scales and improves efficiency compared to traditional Bayesian methods.

翻译：智能体建模（ABM）是一种广泛应用的复杂系统仿真方法。计算处理与存储能力的进步促进了ABM在众多领域的应用，然而ABM仍面临制约其作为决策支持工具使用的挑战。大规模ABM的参数估计是一个关键难题，主要源于参数空间探索所受到的计算限制。本研究评估了一种采用神经网络（NN）进行参数估计的先进仿真推理（SBI）框架。该框架被应用于基于职业转换网络构建的成熟劳动力市场ABM中。ABM的初始化分别采用合成数据集和真实美国劳动力市场数据。随后，我们比较了从一系列统计度量中提取的摘要统计量与嵌入式神经网络学习特征的效能。结果表明：在不同规模数据集的后验分布评估中，基于神经网络的方法能够有效还原原始参数，且相较于传统贝叶斯方法显著提升了计算效率。