Strategic Abductive Forecasting (SAF) v1.0: A Unified Probabilistic Framework for Forecasting Systems with Strategic Agents – Compendium of Specification, Theory, Forecasts, and Validation

Abstract

This compendium presents the complete specification, mathematical theory, prospective forecasts, and synthetic validation results for Strategic Abductive Forecasting (SAF) v1.0. SAF is a unified hierarchical Bayesian framework that integrates Information Theory (MDL), Bounded-Rationality Game Theory (Quantal Response Equilibrium), and Causal Abductive Inference to forecast complex systems where agents react to each other and to the forecasts themselves (reflexivity). Contents: Part I: Formal Specification: The core generative model, signal extraction filters, and epistemic integrity constraints. Part II: Formal Theory: Mathematical proofs for regret bounds, QRE uniqueness, reflexive fixed points, and anytime-valid misfit detection. Part III: Forecast I (2026–27 El Niño): A prospective forecast of the strategic and socio-economic transmission of the 2026–27 El Niño event, including food prices, export policies, and regional impacts. Part IV: Forecast II (World in 2036): A scenario-based forecast of global demographics, climate, energy, growth, and conflict under deep uncertainty. Part V: Validation (V0): Synthetic recovery tests demonstrating the correctness of the information filter, rationality coefficient recovery, and reflexive iteration. Part VI: Appendices: Full Python source code, raw simulation data, and SHA-256 hashes for reproducibility. Status: SAF v1.0 is a formal specification. The included forecasts (Parts III & IV) are registered prospective forecasts (Level V2) to be scored upon resolution, not validated historical retrodictions. No claim of superior predictive accuracy is made until the full validation protocol (V0–V3) is completed. License: CC-BY-SA 4.0