An attempt at defining aspects of consciousness using algorithmic complexity theory

This paper presents a formal, information-theoretic model of consciousness as the alignment between an observer’s true beliefs and an object’s objective description. Objects are defined as sets of statements (O(x)–Q(y)), and consciousness is measured as the ratio of the complexity of true beliefs to the total descriptive complexity. Three belief states are distinguished: Consciousness (true beliefs), Schizo-Consciousness (misbeliefs), Unconsciousness (unheld beliefs). These are modeled as vectors tied to specific parts of an object’s description. The brain is treated as a belief-updating system responding to stimuli, and complexity is measured via binary encoding, programming representations, or entropy-based methods. Visual metaphors and coordinate-based diagrams allow comparisons of belief structures between observers. The model has applications in AI, epistemology, and cognitive science, and suggests potential extensions based on atomic qualities and exclusivity constraints.