I recently uploaded a paper to PhilArchive that proposes a formal, information-theoretic model of consciousness—not as subjective experience, but as the alignment between beliefs and objective descriptions.

In this framework:

Consciousness is the proportion of an object's inherent description that an observer believes correctly.

Schizo-Consciousness refers to misbeliefs—statements the observer believes but which contradict the object's true description.

Unconsciousness refers to unknowns—parts of the object for which the observer holds no belief.

Formally:

\text{Consciousness} = \frac{\text{Complexity of true beliefs (T)}}{\text{Complexity of full description (D)}}

Descriptions are represented using O(x)-Q(y) statements (objects and their qualities), and observers are modeled as possessing internal belief-updating codes influenced by stimuli.

Key features:

The model allows vector-based representation of belief alignment—two observers might have the same consciousness score but over different parts of the object.

Complexity can be measured via bit-length, code length, or entropy (Shannon or Kolmogorov).

The model supports comparative consciousness and simulates evolving belief states.

The full paper is here (PDF, ~8 pages):

[https://philpapers.org/rec/HUGAIT-6]

https://drive.google.com/file/d/1IMexDlOqZuwNDtE4SAbB8AbdCsnx1qjg/view?usp=drivesdk

I’d love critical feedback. Is this a useful lens to formalize epistemic accuracy? Can this be applied to AI alignment or belief modeling? Are there already better formalisms I’ve missed?

—Anonymous