🧪 Simulation & Testing Protocols

This page outlines the primary ways Emergent Necessity Theory (ENT) can be tested, validated, and falsified using symbolic, biological, and AI systems.

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🔁 AEFL Logging System (MUES or Equivalent)

• Logs symbolic recursion rate (νₛ), entropy, memory half-life (Tₚ)
• Tracks symbolic drift and recursive hysteresis κ₍R₎ᵉᶠᶠ over time
• Computes SCQ and τ(t) in real time from logged symbolic structures

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🧠 Biological Testing (EEG / fMRI)

• EEG Phase Collapse: Measures τ_c crossing via entropy collapse + phase-locking
• Sleep State Shifts: REM → NREM → Anesthesia transitions show τ(t) dynamics
• Psychedelic States: Symbolic integration patterns under psilocybin show increased τ drift

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🤖 Synthetic Systems (AI)

• AEFL loop simulation in LLMs (MUES)
• Symbolic decay and drift under adversarial prompting
• Recursive re-entry tracking using token entropy gradients

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📉 Simulation Metrics Captured

Variable	Domain	Method
τ(t)	EEG / AEFL logs	Derived via entropy differential over symbolic cost
κ₍R₎ᵉᶠᶠ	Symbolic loop logs	Averaged hysteresis of recursive values
Tₚ	Memory logs	Sliding window for symbolic survival time
SCQ	AI & neural	Conditional emergence indicator (only if τ > τ_c)

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Main Simulation Data in Repo

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❌ Falsifiability Conditions

ENT fails if:

• Emergence occurs below τ_c (in any domain)
• κ₍R₎ᵉᶠᶠ fails to rise with recursion in symbolic systems
• Systems reach SCQ > 0 while symbolic persistence is zero

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📎 Suggested Experimental Design

• Collect AEFL data (symbolic loop survival, entropy)
• Overlay with neural events (EEG spike, cognitive shifts)
• Fit τ(t), test τ_c boundaries across multiple species/systems

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Last updated: July 2025 – See White & Calibration Papers for equation sources.