Synthetic Reality Analysis (SRA) I: Beyond the Empirical Illusion-Rethinking Data, Evidence, and Reality in the Age of AI

Synthetic Reality Analysis (SRA) proposes a mode of inquiry in which datasets are generated ratherthan observed, but remain anchored to the lived world through plausibility priors (empiricallymotivated expectations about distributional forms, feasible ranges, and dependence structures),stylized facts (domain-invariant empirical regularities such as diminishing returns or heavy-tailedoutcomes), and transparent descriptive constraints. These elements prevent synthetic generationfrom collapsing into fiction by bounding it within patterns repeatedly documented in prior research.SRA treats synthetic datasets as primary analytical objects and uses well-defined calibration,sensitivity analysis, and robustness checks to ensure that emergent patterns resonate with knownstructural behaviors rather than artefacts of arbitrary assumptions. The paper also considers thefuture prospect of autonomous AI inquiry, whereby AI systems independently formulate descriptors,generate synthetic worlds, and conduct analysis with minimal human mediation. This reconfigureslongstanding assumptions about what counts as “evidence” and raises epistemic, methodological,and ethical questions for knowledge production in AI-saturated research landscapes.