Synthetic Reality Analysis (SRA) II: Beneath the Methodological Surface-Operational Protocols for Human–AI Synthetic Data Worlds

Synthetic Reality Analysis (SRA) offers a structured method for generating and examininghypothetical datasets anchored in a publicly declared Descriptor (DC-1) and Constraint Sheet (CS-1). While synthetic data is often treated as a proxy for missing observations, SRA positions it as aprimary site of inquiry: a deliberately constructed world whose coherence is judged againsttransparent design rules rather than external ground truth. This paper addresses a keymethodological gap by formalizing the operational workflow—including descriptor design,constraint encoding, dataset generation, and internal coherence testing—and by introducing theHuman–AI Hypothetical Data Comparison Instrument (H-AI HDCI), a one-time calibration protocolthat aligns AI-generated datasets with human interpretive standards. The resulting ResonanceIndex, Interpretive Depth Score, and Divergence Profile establish whether the synthetic worldbehaves plausibly within its declared boundaries. After calibration, subsequent studies canproceed with AI-generated synthetic datasets (SRA-Solo), supported by reproducibledocumentation practices such as SRA Cards and constraint versioning. The paper outlines theseprocedures and provides a compact education example to demonstrate how SRA enablesstructured, transparent exploration of plausible scenarios where empirical data are limited, costly,or ethically constrained