The Inadequacy of Classical and Quantum Frameworks for Predictive Autonomous Agents in Non-Stationary Environments

Classical and quantum physical frameworksincluding Newtonian mechanics, general relativity, quantum mechanics, and quantum information theoryhave long provided the foundational language for describing dynamical systems. However, their applicability to predictive, autonomous agents operating in highly non-stationary environments remains largely unexplored. We designed a thought experiment centered on an advanced cyber-physical agent (JERPAT-9) operating under extreme non-stationary conditions. Using a hybrid methodology combining analytical modeling, numerical simulation, and proof-by-contradiction, we evaluated the predictive power of both isolated and combined physical theories. Simulations were implemented in Python using differential equation solvers and quantum circuit emulators. All tested models-whether classical, quantum, or hybridfailed to account for key observed behaviors such as anticipatory trajectory adjustment, local entropy reduction, and non-local coherence. Structural incompatibilities (e.g., between quantum superposition and classical spacetime) and computational limitations were systematically identified. Existing physical theories are insufficient to model intentional, adaptive agents in non-stationary environments. This inadequacy necessitates a new theoretical framework, tentatively termed Molimambic, incorporating non-local, information-driven, and intentional dynamics.