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[2602.15572] Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model
Summary
This paper presents a neural network-based framework for parameter estimation in agent-based models (ABMs) of the labor market, demonstrating improved efficiency over traditional methods.
Why It Matters
The study addresses a critical challenge in agent-based modeling—parameter estimation—by applying advanced neural network techniques. This approach enhances the utility of ABMs in decision-making processes across various fields, particularly in labor market analysis, making it relevant for researchers and practitioners alike.
Key Takeaways
- Neural networks can effectively estimate parameters in labor market ABMs.
- The proposed framework outperforms traditional Bayesian methods in efficiency.
- Synthetic datasets and real labor market data were used for validation.
- The study highlights the importance of summary statistics in model accuracy.
- Improved parameter recovery enhances the decision-support capabilities of ABMs.
Computer Science > Machine Learning arXiv:2602.15572 (cs) [Submitted on 17 Feb 2026] Title:Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model Authors:M Lopes Alves, Joel Dyer, Doyne Farmer, Michael Wooldridge, Anisoara Calinescu View a PDF of the paper titled Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model, by M Lopes Alves and 4 other authors View PDF HTML (experimental) 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...
