Action, Ethics, and AI Alignment: A Deterministic Framework of Moral Mechanics and Effective Field Theory

We introduce a deterministic, objective framework for moral action derived strictly from the fundamental nature of physical reality, information theory, and variational mechanics. We reject the necessity of subjective human reinforcement paradigms for AI alignment, proposing instead that perfect moral action is a process of systematic minimization of in- formational entropy constrained by a physics-inspired Lagrangian. We extend the classical Principle of Least Action (PLA) to active intelligent agents, introducing the Kant-Spinoza Principle of Least Action (KS-PLA) by isomorphic mapping of kinetic energy to pleasure and potential energy to pain into the KS Lagrangian. To scale the model from individual trajectories to multi-agent ensembles, we apply statistical mechanics and coarse-graining to establish an Effective Field Theory (EFT) of Ethics. This framework introduces a contin- uous sentient vulnerability density field interacting dynamically with an individual’s moral path, providing a scalable, structurally stable mathematical baseline for absolute AI align- ment. Under complete knowledge (K → 1), arbitrary “free will” evaporates into rational necessity, and Kant-Spinoza Invariance (KS-Invariance) eliminates coordinate biases (race, gender, species), enforcing universal self-consistency (Vij = 0).