This preprint extends the Kaan Invariant, originally formulated for open quantum systems, to cognitive decision dynamics, neural population models, reinforcement learning, and optimisation processes in artificial neural networks. The central structure tau * ||G|| ≈ c appears across multiple domains: drift–diffusion models (decision time vs drift rate), neural integrators (memory stability vs instability margin), reinforcement learning (convergence time vs reward gradient), stochastic gradient descent (optimisation time vs effective gradient norm), AGI alignment (alignment vs misalignment drift competition). The note argues that the invariant reflects a more general constraint on dynamical systems operating under uncertainty: the minimal time to cross a decision, memory, or optimisation boundary is inversely proportional to the strength of the generator driving that transition. This document forms Part II of the Kaan Invariant series and builds upon the foundational quantum formulation presented in Part I.