Neural networks enhance parameter estimation for agent-based models

A research paper explores the application of neural networks for parameter estimation in agent-based models (ABMs), specifically within a labor market simulation. The study evaluates a simulation-based inference framework using neural networks to address the computational challenges of parameter estimation in large-scale ABMs. Results indicate that the neural network approach effectively recovers original parameters and offers improved efficiency compared to traditional Bayesian methods. AI

IMPACT This research could lead to more efficient and accurate simulations in fields relying on agent-based modeling.

RANK_REASON This is a research paper detailing a novel application of neural networks to a specific modeling technique. [lever_c_demoted from research: ic=1 ai=1.0]

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• M Lopes Alves
• U.S.

• paper
• other

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