用神经网络加速劳动力市场模型参数估计，提升效率与精度。
Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model
- 用神经网络替代传统方法进行仿真推断，自动学习参数匹配特征。
- 在不同规模数据下均能准确恢复原始参数，效率显著高于贝叶斯方法。
- 适合需要快速校准复杂代理模型的研究者，尤其适用于大规模社会系统建模。
基于代理的建模（ABM）是模拟复杂系统的常用方法。尽管计算能力与存储技术的进步推动了其广泛应用，但大规模ABM在参数估计方面仍面临挑战，主要受限于参数空间探索的计算开销。本文评估了一种先进的基于仿真的推断（SBI）框架，该框架利用神经网络（NN）实现参数估计。研究将该框架应用于一个基于工作转换网络的成熟劳动力市场ABM，分别使用合成数据和真实美国劳动力市场数据启动模型。进一步比较了由一组统计指标生成的摘要统计量与嵌入式神经网络自动学习的摘要统计量的有效性。结果表明，基于神经网络的方法在不同数据规模下均能准确恢复原始参数，并在效率上优于传统贝叶斯方法。
原文摘要 · Abstract (English)
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.
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