This project develops and stress-tests a concrete governance instrument for frontier AI: risk-weighted tradable compute permits with enforceable compliance under imperfect monitoring. The core idea is to regulate training-relevant compute as a scarce, auditable input while allowing trade to reduce compliance cost and improve feasibility. “Risk-weighted” means the permits required per unit of compute depend on verifiable risk indicators, especially evaluation outcomes, so higher-risk training runs face tighter effective caps and higher marginal cost.

The project has three workstreams.

(1) Formal model: a regulator sets an aggregate cap, a risk-weighting rule, and an enforcement policy; developers choose compute use, reporting, and evasion effort under imperfect monitoring. We derive implementable conditions for truthful reporting and deterrence of under-reporting or hidden training.

(2) Minimal simulation: we implement a lightweight Monte Carlo or agent-based simulation comparing enforcement regimes such as random audits, risk-based targeting, convex penalties, and escalation rules, documenting tradeoffs between compliance, expected harm reduction, and administrative burden.

(3) Policy translation: we convert the design into an EU-relevant blueprint specifying institutional roles, evidentiary standards, audit triggers, and interfaces with evaluations and incident reporting.

Expected outputs by the end of the main phase are:

(a) a public preprint with a clear proposition spine and robustness checks,

(b) an open-source repository with simulation code and reproducible figures, and

(c) a 6 to 10 page policy brief describing an EU-implementable pathway.

If the project extends, we will polish for submission and stakeholder feedback.