Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model

TL;DR

This paper evaluates a neural network-based simulation inference framework for estimating parameters in large-scale agent-based labour market models, demonstrating improved efficiency and accuracy over traditional methods.

Contribution

It introduces a neural network-based approach for parameter estimation in agent-based models, addressing computational challenges and enhancing accuracy.

Findings

Neural network approach effectively recovers original parameters.

Method improves efficiency over traditional Bayesian techniques.

Applicable to both synthetic and real-world datasets.

Abstract

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.