Ahuraic Framework – Phase 3 Core Validation Codes (V31, V32, V34)

This repository contains the three core computational experiments of Phase 3 of the Ahuraic Framework numerical validation, supporting the paper: "Informational Structural Cost (CIS): Derivation, Thermodynamic Correspondence, and Predictive Power of the Ahuraic Framework's Core Organizational Quantity" These experiments test the behavior of the canonical CIS functional, I(X) = ln(|S_ρ(pre(X))| / |X|) + κ·ρ(X) with κ = 9.93, in an abstract agent‑based environment. Scripts included: - V31 (cis_simulation_v31_thermodynamic_bridge.py): Baseline structural accumulation over 500 generations in 10 independent worlds. No capacity constraint is imposed. CIS grows from initial values ~9–24 to a mean of 516.2, while internal prediction error declines below 0.002. - V32 (cis_simulation_v32_blind_prediction_engine.py): Blind forecasting of the final organizational state using only the initial CIS. The mean prediction error is 95.8%, showing that CIS alone is not a universal predictive law. - V34 (cis_simulation_v34_capacity_aware_prediction_engine.py): Capacity‑constrained prediction using a logistic growth model I(t) = C_max(1 – e^{–rt}). After estimating C_max from the baseline, the blind prediction accuracy is restored to 99.87% (mean error 0.13%). Key findings:(1) CIS shows consistent structural accumulation and self‑predictability.(2) Blind extrapolation from initial CIS fails dramatically.(3) Incorporating finite capacity constraints restores near‑perfect prediction, confirming that CIS is a state descriptor governed by organizational capacity. Each script outputs a corresponding CSV file with full per‑world results. Requirements: Python 3.7+, NumPy, SciPy, Matplotlib.