Explore StressLab without a backend.
This version runs entirely on precomputed StressLab scenarios, so it can live on GitHub Pages for free. The controls below snap to the nearest real scenario in a curated library instead of starting a live Python run.
Preset
Choose a domain story, then adjust the same knobs used in the live demo.
Controls
The result snaps to the nearest precomputed scenario in the library.
Scenario result
A research-backed preview of collapse risk, failure margin, bottlenecks, and intervention leverage.
Representative figure
Interpretation
Top bottlenecks
Critical edges
Why this works on GitHub Pages
The static demo packages a small library of real StressLab runs for each preset and uses nearest-scenario matching in the browser. That keeps the experience free, fast, and reproducible while staying grounded in actual search and optimization outputs.
If you want the fully live version later, the same presets can still be served through the Python API. This page is the zero-cost publishing path.
What to look for
- Push utilization upward and watch collapse risk accelerate faster than the other knobs move it.
- Increase coupling and note that the failure margin usually shrinks faster than the threshold moves.
- Reduce slack to make the same utilization level feel more brittle.
- Use the healthcare preset for the clearest story and the market preset for the sharpest “margin disappears first” pattern.
Coupling compresses margin
The controlled follow-up found that coupling mostly narrows shock margin rather than relocating the threshold much.
Concrete case study
The healthcare preset connects the abstract result to an intuitive overload and intervention story.
Cross-domain context
The static demo is anchored to the same cross-domain finding shown in the flagship studies.