Directional Operational Principle (DOP)

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This paper proposes the Directional Operational Principle (DOP), a conditional and testable systemic hypothesis concerning the distinction between operational capability and directional autonomy. The motivating case is human–AI interaction, in which an AI system may exhibit substantial generative, transformational, formatting, or computational capacity while the objective, criterion, constraint, evaluation rule, or task direction may be supplied by another system. The paper does not claim that artificial intelligence is intrinsically incapable of generating hypotheses, objectives, or novel outputs. Instead, it abstracts a narrower structural proposition: operational capability alone does not logically entail directional autonomy.A general system is represented as S_t = (X_t, U_t, D_t, C_t, M_t, O_t, E_t), where X denotes state, U input, D direction, C criterion or constraint, M operational medium, O operation, and E evaluation. The proposed dynamic architecture is X_{t+1} = F(X_t, U_t, D_t, C_t, M_t), together with operational and evaluation mappings. A candidate directional-dependence statistic is introduced for controlled intervention experiments. The framework is deliberately conditional: it does not assert that every system is externally directed, nor that every autonomous or adaptive system violates the principle. Instead, it identifies conditions under which directional specification and operational execution remain analytically distinct.The paper develops three layers: conceptual–epistemological, system-theoretic–mathematical, and empirical–computational. Thirty case-study templates are organized across these layers. Simulation procedures are supplied as reproducible numerical experiments rather than as substitutes for empirical validation. Counterexamples, boundary conditions, falsification criteria, limitations, and a fourteen-step master audit protocol are explicitly incorporated to reduce risks of circular reasoning, pseudomathematics, and overgeneralization. The epistemic status of DOP is therefore classified as a testable universal hypothesis rather than an established universal law.