A Closed, Non-Adaptive Framework for Auditable Decision-Making

This work presents a closed, deterministic decision framework focused on auditability rather than optimization.
The system is non-adaptive: no learning, no targets, no automation.
Reasoning is constrained through fixed sequential states, invariant checks, and deterministic evaluation.
The objective is formal inspectability and accountability, not performance gains.
Repository (OSF, DOI assigned):
https://osf.io/ub5f4/
Feedback from formal methods, decision systems, and AI governance is welcome.