Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model
This addresses computational bottlenecks in ABM parameter estimation for decision-support in fields like economics, though it is incremental as it builds on existing SBI methods.
The study tackled parameter estimation in large-scale agent-based models (ABMs) by applying a neural network-based simulation-based inference framework to a labour market ABM, showing that it recovers original parameters and improves efficiency over traditional Bayesian methods.
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.