Empirical Implementation and Validation Architecture for Dyadic Calibration Systems - DSS VI

This preprint provides the empirical implementation, simulation, and validation architecture for the Dyadic Systems Series. The paper consolidates the theoretical constructs developed in DSS-I through DSS-III, the quantitative and qualitative operationalisations specified in DSS-IVa and DSS-IVb, and the cross-modal synthesis established in DSS-V. It does not introduce additional theoretical mechanisms. Instead, it specifies how the existing framework may be implemented empirically while preserving its system-level ontology and inference boundaries. The architecture separates dyadic state variables, regulatory parameters, and meta-regulatory processes across distinct analytic layers and time scales. It provides measurement primitives, models of inter-domain coupling, minimal study-design templates, and implementation skeletons for cross-sectional, longitudinal, APIM, EMA, and dynamic parameter-estimation approaches. Simulation is treated as an analytic support tool for examining structurally admissible trajectories, parameter-drift regimes, gating-dependent stability, and regime boundaries. It is not presented as a prediction engine, optimisation procedure, or source of intervention recommendations. The paper specifies four complementary forms of validity: structural validity, dynamic validity, modal validity, and negative validity. Negative validity concerns whether empirical use respects the framework’s explicit exclusions, including the rejection of imposed symmetry, fairness scoring, intent diagnosis, and prescriptive outcome optimisation. DSS-VI closes the series as a coherent empirical architecture: operational, testable, and extensible through data, while remaining descriptive, normatively neutral, and bounded to the explanation of dyadic system dynamics. Mathematics and simulation in the Dyadic Systems Series are representational rather than foundational. They are used to expose assumptions, dependencies, dynamic constraints, and admissible trajectories. They do not imply that dyads perform numerical optimisation, nor do simulated trajectories constitute predictions of individual relationships.