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Neural Network-Based Parameter Estimation for a Labour Market ABM
US & West ·
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
A generic formalism is proposed to represent and control large-scale systems of systems through agent-based simulation. Organizational aspects use the Agent-Group-Role model, functional aspects a goal specification, and multilevel aspects the IRM4MLS meta-model, covering both static and dynamic properties.
The Hybrid Coevolutionary Opinion Game H-COG combines cost-minimizing Friedkin-Johnsen agents with Phi-4 language agents in a dynamically rewired network. Using 50 agents seeded with opinions from 5,199 Reddit comments, it studies convergence and collective efficiency.
Conditions are identified to define the most typical network state in grand canonical ensembles and their random mixtures. A deterministic construction is proposed that converges to this typical state in the thermodynamic limit, addressing how well a real network matches a chosen model.
