Using machine learning as a surrogate model for agent-based simulations
This study compared nine machine-learning methods for creating surrogate models of an agent-based model (ABM) of social care provision in the UK. Neural networks outperformed other methods in accuracy, especially with larger datasets, but required longer training times. Gradient-boosted trees and non-linear SVM also demonstrated good performance with faster runtimes, suggesting machine learning can be a viable approach for efficient surrogate modeling of ABMs.