A Structural-Bayesian Framework for Evaluating Scientific Hypotheses: Integrating Epistemic Constraints with Probabilistic Inference

This paper introduces a novel Structural-Bayesian framework for evaluating scientific hypotheses that bridges the gap between qualitative epistemic assessment and quantitative Bayesian inference. The methodology addresses a persistent problem in philosophy of science: standard evaluation criteria assign identical scores to radically different theories—from fundamental physical laws to trivial methodological truisms—creating a "Plateau of Perfection" that obscures genuine epistemic differences. The Framework The approach decomposes theory evaluation into three orthogonal layers: Internal Integrity (I): Logical consistency, conceptual clarity, reproducibility, absence of ad hoc elements, domain specification (0–5 scale) Constraint Power (C): Whether the theory excludes genuine possibilities beyond definitions and tautologies (0–3 scale) Ontological Load (O): Whether claims are world-directed with modal constraint (0–2 scale) A continuous Non-Triviality Multiplier N = 0.4 + 0.2×C penalizes theories that satisfy formal criteria without excluding anything substantive. The Structural Admissibility Score S(T) bounds rational Bayesian prior assignment: P(T) ≤ S(T). Key Results Benchmark evaluations demonstrate discriminative power: General Relativity: S = 100% (complete physical theory) Quantum Mechanics: S = 78% (empirically powerful, foundationally incomplete) String Theory: S = 12% (mathematically rich, empirically unconstrained) Practical Applications The paper includes: A standardized evaluation protocol for reproducible assessment LLM-assisted self-evaluation workflow for researchers Domain-specific weight calibration guidelines Comprehensive literature review positioning the framework within Bayesian epistemology Target Audience Philosophers of science, theoretical physicists, statisticians, and researchers seeking principled methods for theory comparison in data-sparse domains. Bayesian inference; theory evaluation; epistemic assessment; structural constraints; prior specification; model comparison; philosophy of science; scientific methodology