General Probabilistic Computational Framework Applied to Drake–Fermi–Brin Models of Technological Civilizations

Many scientific and engineering domains rely on simple models whose usefulness is limited by uncertain parameters and incompatible formulations. We present a computational framework that converts deterministic, semi-empirical, and heuristic equations into stochastic model families by using bounded parameter representations and Monte Carlo sampling. An explicit semantic layer preserves differences in variable meaning, enabling comparison without forcing structural equivalence. The framework also supports supermodels—weighted mixtures of heterogeneous model families evaluated in a shared diagnostic space. The method is implemented as a reproducible pipeline for five structurally distinct Drake–Fermi–Brin models, with joint and marginal distribution analysis, exploratory clustering, parameter-importance diagnostics, and layered uncertainty decomposition. Results show that model structure and epistemic parameterization materially shape the induced distributions. Comparisons therefore remain conditional on the declared parameter bounds, sampling families, semantic bridges, and model-family weights. Nevertheless, ensemble integration can reveal behavior not visible within individual models. The space of possible supermodel configurations exceeds 1027, illustrating both the scale of the problem and the value of a structured probabilistic workflow. The framework provides an extensible basis for uncertainty propagation and comparison across incompatible models.