New Framework Automates Analysis of Complex Agent-Based Models Using ML Surrogates

Researchers developed a two-stage pipeline combining systematic screening and machine learning to efficiently explore high-dimensional stochastic agent-based models, automatically identifying unstable regions driven by nonlinear interactions. The approach addresses computational bottlenecks limiting ABM exploration and enables more rigorous policy testing for complex systems.