The Structural Conditions of Consciousness VI — Final Volume — — Structural Knot Theory — Toward a Structural Decision Tool: Determining the Presence and Degree of Subjectivity (Repositioning State-Based Theories of Consciousness: IIT, GNW, HOT, Predictive Processing, RPT, and Epiphenomenalism)

Qualia? “What cannot be directly spoken of must remain unsaid — but it can still be structurally surrounded.” This is the guiding spirit of the present series. This paper presents a minimal structural framework for determining the presence and degree of consciousness and subjectivity. Existing theories of consciousness typically characterize subjectivity in terms of internal states, including information integration, global availability, higher-order representation, and predictive processing. When subjectivity is defined in this way, its evaluation is necessarily formulated over state spaces. As system complexity increases, these state spaces expand correspondingly, and the determination of subjectivity becomes a problem of evaluating high-dimensional structures. In this case, the identification of subjectivity becomes tied to the tractability of such evaluations. This structural limitation has been examined in detail in Volume III. The present framework takes a different approach. Rather than locating subjectivity within internal states, it is identified in the structure of irreversible decision, in which multiple internally generated possibilities are reduced to a single realized outcome under viability constraints. If this account is correct, subjectivity is not to be found in internal states at all, but only in the structure of irreversible decision. From this perspective, subjectivity is not inferred from internal complexity, but determined through structural conditions. Accordingly, the presence of subjectivity is defined as a binary condition. Subjectivity exists if and only if all five structural conditions are satisfied: (1) autonomous world-modifying action, (2) viability constraint, (3) multiple internally generated action candidates, (4) exclusive and irreversible decision, and (5) the formation of a single irreversible history. If any one of these conditions is absent, subjectivity is not structurally required (see Volumes I and II for strong inferential support). This formulation allows for direct structural inspection without exhaustive computation, enabling clear and consistent classification. The framework further defines the degree of subjectivity as a structural quantity associated with the contraction of possible futures during exclusive decision processes. This quantity is determined by the number of action candidates, the strength of viability constraints, and the rate at which reachable futures are reduced. In this sense, subjective intensity corresponds to the density with which possible futures are irreversibly resolved. This formulation is derived in Volume V. The scope of the framework is not limited to particular cases such as dreaming, sleep, anesthesia, biological variation, or artificial systems, including artificial intelligence. Rather, it applies to all systems operating under causal constraints. The classifications presented here are illustrative and do not restrict the scope of the theory. Within this framework, subjectivity is determined solely by structural conditions, independently of intelligence, complexity, or physical substrate. This work is presented as a structural prototype. Its validity depends on resistance to counterexamples. If subjectivity is demonstrated in the absence of the proposed conditions, or if the formulation of subjective intensity fails to correspond to observed variation, the framework must be revised or abandoned. In this sense, subjectivity is treated not as something to be inferred from internal states, but as a structurally identifiable and testable feature of causally constrained systems. This paper can be read independently as a minimal operational formulation of subjectivity. The framework is therefore proposed as a structurally testable account of subjectivity. This manuscript was written with the assistance of generative AI. The author i