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

2026-02-17

AI Summary

This study addresses the computational challenges of parameter estimation in large-scale agent-based models (ABMs) of labor markets, which hinder their practical use for decision support. To overcome this limitation, the authors propose an end-to-end parameter estimation framework based on simulation-based inference (SBI). The approach leverages neural networks to automatically learn summary statistics, replacing handcrafted indicators, and integrates neural embeddings with Bayesian posterior inference. Evaluated on both synthetic data and real-world U.S. labor market data, the method demonstrates superior accuracy and efficiency in recovering model parameters across multiple scales, significantly outperforming conventional Bayesian approaches while maintaining high precision and substantially improving computational efficiency.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: State Estimation
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

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.

Problem

• parameter estimation
• agent-based model
• labour market
• computational constraints
• simulation-based inference

Innovation

• neural network
• simulation-based inference
• agent-based model
• parameter estimation
• labour market