What this is. A Python, stdlib-only synthetic companion that generates a discovery rate Rd from a label-blind interface searcher on a Highly Optimized Tolerance (HOT) rule graph. The patch-gap G = Rd/Rp and the stocks Sknown, Sactive are outputs, not fitted legal measurements. Paper this illustrates (separate Zenodo concept): concept 10.5281/zenodo.21910874; this PDF 10.5281/zenodo.21910875 (v1.0). That PDF defines G as a definition, not a dynamical model; the section 4.4 powers of ten are heuristics. This software exists so those qualitative distinctions can be run. Reproduce. Python 3.10+ (3.8 likely fine). No third-party packages. python verify_patchgap_claims.py — exit 0 iff three toy claims hold. Optional: python -m simulation.figures writes labeled SVGs under figures/. Contents. HOT vs uniform-null generators; spill-ranked interface searcher; two-stock dynamics; claim-manifest.json (cd-claim-gate/v1); executed gate evidence; five Tufte-leaning figures (HOT vs null, G and S, searcher rule, actors, limiters). No paper PDF. No real legal corpus. Non-claims. Not a measurement of legal G, Rd, or Rp. Not support for the paper heuristic exponents. Not a test that any statute is HOT. Not SocioHack, A1/VERITE, Rice/FLP, or an evaluation of any live bill. This version (v0.1.1). Labeled figures replace unlabeled snapshots: seed strips with axes; stacked G(t) and S(t) (no dual y-axis); spill vs degree-greedy; actor-class execution stocks; limiter 2×2. Dynamics and gated claims unchanged from v0.1.0 (10.5281/zenodo.21918092).
AI governance · open texture · reward hacking · regulatory arbitrage · highly optimized tolerance · governance patch-gap · legal exploit discovery · computational claim gate · synthetic rule graph