01 / THE PRACTICAL QUESTION
A decision, before a model.
An institution appears resilient on its own but is exposed to other institutions that can fail together.
Test whether graph learning predicts simulated contagion more accurately than a structural rule.
02 / DATA & COMPARISON
The idea in plain language.
A graph model passes information between connected institutions. A structural baseline explicitly approximates how losses propagate through the same network.
- Brier score
- The average squared difference between predicted probabilities and observed outcomes.
Data. Seeded financial networks with simulated default cascades.
Baseline. Degree and exposure-based structural scores.
03 / THE EXPERIMENT
What the saved experiment shows.
The graph convolution model has a higher Brier score than the one-round structural baseline in all three held-out seeds. Lower is better: the simpler mechanism-aware baseline wins this saved comparison.
Inspect the supporting resultEvidence record: learning-benchmarks.json#projects/10
Explore the related lab04 / RESULTS & LIMITATIONS
Evidence with its boundaries attached.
The related lab is a cross-project demonstration. Read this investigation’s evidence and limits before transferring its conclusions.
Inspect numerical checks and validation records
Loading validation evidence…
Loading across-seed evidence…
Constructed network labels are conditional on the chosen cascade mechanism.
05 / REPRODUCE
Reproduce and challenge the result.
Code, configuration, and reproduction
The project contains its implementation, configuration, tests, and walkthrough. Download the lab configuration to record the exact parameters used in an interactive run.
Project code and walkthrough