Introduces a new protocol that improves digital governance by separating different types of variables in AI systems, indicating better decision-making practices.
**Autonomous Constraint Recognition Protocol Layer** presents a foundational cybernetic protocol (ACRPL) that redefines digital governance by enforcing a strict computational separation between optimizable variables and unoptimizable variables within AI-assisted decision-making. The paper addresses the systemic "dimensionality collapse" of legacy technocratic frameworks that unsafely reduce unquantifiable human values to soft, weighted terms in a single objective function.
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Sum Wan FU (2026) studied this question.
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