Hi everyone,

I’m sharing a theoretical model I’ve developed that attempts to formalize consciousness using belief accuracy and information theory, and I’d love your thoughts, critique, or suggestions—especially from those working in epistemology, AI alignment, cognitive modeling, or philosophy of mind.

🧠 Core Idea:

The model treats consciousness not as subjective experience per se, but as a structural alignment between an observer’s beliefs and the objective description of an object.

It introduces three belief states:

Consciousness: the observer holds a true belief about some quality of an object.

Schizo-Consciousness: the observer holds a false belief (mislabeling or distortion).

Unconsciousness: the observer holds no belief (a blind spot).

📏 The Formal Definition:

\text{Consciousness} = \frac{\text{Complexity of true beliefs}}{\text{Complexity of total object description}}

Descriptions are expressed as O(x)–Q(y) pairs (objects and qualities), and belief states are modeled as labels (true/false/none) on these statements. Complexity can be measured using entropy, bit-length, or code length.

The model also includes a dynamic belief-updating mechanism (inspired by cognition), and it visualizes belief content as vectors in an informational space, allowing for comparisons between observers or tracking evolution over time.

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🌍 Applications & Relevance:

I believe this model might have relevance to several areas:

AI alignment: modeling misbeliefs or epistemic divergence in agents.

Interpretability: quantifying and tracing which beliefs an agent/system holds and why.

Human epistemology: simulating how true and false beliefs form and evolve in complex domains.

Value learning: separating ignorance from misalignment at a formal level.

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📄 Paper:

I’ve uploaded a PDF draft here:

👉 [https://philpapers.org/rec/VOGAAA-5]

If anyone is interested in reviewing or workshopping the model—or pointing me toward relevant critiques or similar formalisms—I’d deeply appreciate it.

Thank you,

Cameron Vogler