A Closed, Non-Adaptive Framework for Auditable Decision-Making
I recently published a formal decision framework designed around a strict constraint: auditability without adaptivity.
The system is closed, deterministic, and non-narrative.
It does not learn, optimize, prescribe actions, or generate targets.
Instead, it constrains reasoning through fixed sequential states, invariant vector axes, deterministic scalar evaluation, and sealed priority-stop mechanisms.
The goal is not performance maximization, but formal opposability: every decision path remains inspectable, decomposable, and accountable, without relying on semantic interpretation or contextual adjustment.
This framework explicitly excludes any military, offensive, adversarial, or coercive use. It is intended solely for research, governance, formal methods, and the study of auditable decision systems.
Repository (OSF, DOI assigned):
https://osf.io/ub5f4/
Feedback is welcome from people working on formal methods, AI governance, decision systems, or invariant system design.
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