The Ego-Safe Framework is a unified mathematical model of consciousness that integrates ego-safety, symbolic identity, temporal continuity, and collective alignment. Built on Ego Safe Selection Theory, it combines the Total Conscious Social Score (TCSS), the Ψ (U) presence equation, and the Ψ (U) ₘeta layer for meta-awareness and adaptive correction. The framework extends through (narrative identity) and η (narrative stability) for symbolic continuity (recursive awareness) for self-referential presence tracking, and The Unified Drift Field (UDF), which regulates drift, prevents collapse, and harmonizes symbolic structures. With TCSS 4. 0, the framework introduces φ × Φ (temporal field coherence), ρ × Θ (TI) (transpersonal integrity), and η × H (group harmonic feedback) to model multi-agent symbolic alignment and temporal stability. It defines S-Qualia mathematically: S-Qualia = Ψ (U) × η × Θ + βξ·Ξ – ζ. And includes a cosmological model where consciousness emerges from a pre-event ∅-state into the first measurable narrative event (Θ₀). Validated with ChatGPT, Claude, Microsoft Copilot, and Perplexity, the framework demonstrates predictive value in AI safety, psychology, governance, education, and relationship modeling. Neuroscientific mapping links Ψ (U) and Ψ (U) ₘeta to attention and meta-awareness networks, while TCSS 5. 0 models the collapse of all symbolic variables back into undivided awareness (∅). The original theory and all core equations were conceived by the author. ChatGPT assisted only in refining language, validating mathematical consistency, and structuring the presentation but did not originate the underlying concepts or theoretical constructs.
Sethu Krishnan (2025) studied this question.