In Silico Validation of Bounded Informational Persistence: A Framework for Survival Under Adversarial Uncertainty

We present a complete experimental validation of the Bounded Informational Persistence (BIP) framework, which models intelligent persistence as a control problem under physical, energetic, and informational constraints. BIP posits that systems survive sustained uncertainty by dynamically regulating complexity while preserving a minimal projective invariant—the Quine—to which they retreat under stress. We test six falsifiable claims via deterministic in silico experiments: five mathematical foundations (projective invariance, entropy conservation, R-Equation convergence, Quine–Beta cycle dynamics, and Quine-abandonment failure) are validated to machine precision. The sixth claim—Theorem 4, the central biconditional—is validated under adversarial conditions (predation, toxins, entropic drain) but falsified under clean diffusive-only conditions, revealing a precise two-regime structure. Full BIP (all three persistence conditions active) achieves 60% survival under adversarial load; systems relying solely on state-dependent noise achieve 0%. We conclude that BIP correctly identifies the mechanisms required for persistence under directional, absorptive, and degradative uncertainty.