A Two-Phase Stochastic–Bayesian Dynamical Framework for Decision Making in Open Dissipative Systems

We present an abstract dynamical framework for decision making basedon stochastic processes in open dissipative systems. The model exhibits atwo-phase structure consisting of stochastic hypothesis expansion followed bydissipative attractor selection. A Bayesian interpretation is provided via freeenergy minimization. The framework is formulated as an effective theoryand does not assume any specific biological, physical, or quantum-mechanicalsubstrate. Numerical illustrations demonstrate internal consistency and qualitative behavior. This project is now under legal monitoring. All commercial implementations must contact the author.