Abstract

This specification formalizes the *Multi-Agent Symmetry of Agency Protocols (MSAP)*, an engineering methodology designed to prevent autonomous optimization algorithms from overriding non-linear human decision-making profiles. Traditional alignment methods rely on static reward modeling or top-down optimization limits that frequently collapse or lock up during high-entropy edge cases.

MSAP solves this vulnerability by embedding human behavioral inputs directly into the system’s information-theoretic telemetry loops as protected, high-utility systemic dynamics. Treating biological inputs as scale-invariant wave packet perturbations within a 2D viscoelastic fluid membrane at absolute equilibrium (z=0), we derive an invariant boundary cost function that treats human variance not as systemic noise, but as an essential anti-parroting vector (\(H_{\text{floor}}\)).

This technical brief provides the continuous-time mathematical proofs, a production-ready Python simulation modeling real-time dynamic human-agent attenuation balancing, a functional Cypher graph database architecture, and a comprehensive glossary of terms.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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