StressLab combines deterministic simulation, adversarial search, theory extraction, and reporting into one workflow. The studies on this site were produced by running that pipeline repeatedly across large synthetic system families and then validating the strongest claim with a controlled follow-up.
StressLab can load authored YAML systems or generate large synthetic families such as random queue, scale-free, small-world, supply-network, and market-style graphs.
Each system is simulated under baseline conditions, minimum-shock failure search, and worst-case stress search so collapse metrics can be compared systematically.
StressLab computes structural features, collapse metrics, early-warning signals, candidate laws, and phase-transition summaries across the resulting dataset.
Research reports, curated figures, case studies, and public-facing narrative assets are built automatically so the same engine supports both science and communication.
The point is not just to produce pretty plots. StressLab emphasizes deterministic, artifact-rich runs so results can be replayed, compared, and explained. That is why it works as both a research tool and a decision-support platform.
Many simulation tools stop at scenario playback. StressLab treats scenario generation, search, theory analysis, and narrative packaging as one continuous workflow. That is what made the flagship studies possible.