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Neural Network-Based Parameter Estimation for a Labour Market ABM

US & West · 17 Aug

This study applies a state-of-the-art simulation-based inference framework with neural networks to estimate parameters of a labour market agent-based model. The model is based on job transition networks and is initialized with synthetic and real U.S. labour market data. The approach addresses computational constraints in exploring the parameter space.

Why it matters

The method is important for practical use of agent-based models in decision support, as it enables calibration of complex models on real data.

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

Read the original: arXiv — Multiagent Systems (cs.MA)

Translation and summary are machine-generated from the source. The full article is not republished; its rights belong to the source.