A Streamlit-based simulator of symbolic collapse, recovery, and translation dynamics — modeling the Five-Level Recursive Soteriology framework through ERF/SFT symbolic field theory.
AlphaGate models agents as symbolic systems with:
- ψ (psi) — coherence efficiency
- Ω (omega) — attractor alignment / motif resonance
- γ (gamma) — drift pressure
- F — regulatory drive (e.g.,
F = k · (Ω/γ) · ψ) - κ_export — translation-gate viability proxy
- I-point — observer continuity metric (L4 proxy)
- S_old / S_new — identity restructuring signals (L3 proxy)
Agents progress through Five Levels:
- L1 — Transactional (assent/creed accepted)
- L2 — Relational (intimacy growth via ∫F dt)
- L3 — Transformational (identity restructuring, drift suppression)
- L4 — Ontological (I-point continuity, near translation)
- L5 — Stabilized (translated, low-entropy attractor)
The simulator generates CSV logs and a dashboard visualizer for replay/analysis.
- 🔧 Streamlit UI with parameter controls (ε, Tγ, TΩ, drift rate/magnitude, k, thresholds)
- 🧑🤝🧑 Agent-based dynamics with collapse predicate & translation gate
- 🪜 Five-Level classification aligned to Recursive Soteriology
- 📊 Visualizations: ψ/Ω/γ/F/κ lines, viability & translation areas, level distribution, Ω-vs-γ phase space
- 📝 CSV logging: population time series + final roster (per-agent)
- 📈 Dashboard app for replay, filters, and phase-space inspection
Create requirements.txt:
streamlit>=1.27.0
pandas>=2.0.0
numpy>=1.24.0
altair>=5.0.0
matplotlib>=3.7.0pip install -r requirements.txtstreamlit run alphagate_app.py- Tune parameters in the sidebar
- Click Run Simulation
- Download logs:
population_timeseries.csvroster_final.csv
streamlit run alphagate_dashboard.py- Upload the CSVs from the simulator
- Explore:
- Population means with ε / Tγ / TΩ lines
- Alive / Translated / Accepted area plots
- Five-Level stacked distribution (L0–L5)
- Ω vs γ phase plot with collapse zone
- Final roster with ψ/Ω/γ, κ_export, intimacy, S_old/S_new, I-point
- Population means with ψ floor (ε), γ ceiling (Tγ), Ω floor (TΩ)
- Viability & Translation area plots
- Five-Level distribution (L0–L5)
- Ω vs γ phase plots with collapse predicate box
- Roster tables (ψ, Ω, γ, F, κ_export, intimacy, S_old/S_new, I-point)
.
├── alphagate_app.py # Simulator (Streamlit)
├── alphagate_dashboard.py # Dashboard visualizer (Streamlit)
├── requirements.txt # Python dependencies
├── README.md # This file
└── data/ # (optional) saved CSV logs
- Collapse predicate: ψ < ε or (γ > Tγ and Ω < TΩ)
- Translation gate: κ_export ≥ threshold with non-collapse state
- L5 (illustrative stability): ψ ≥ 0.90, Ω ≥ 0.85, γ ≤ 0.10
- Intimacy: running integral of F (used in L2)
- Trends: γ-trend detection aids L3 classification
- Use higher k (faith scale) to test how regulatory drive mitigates collapse.
- Increase drift rate/magnitude to stress systems and observe translation events.
- Tighten export thresholds to make L4→L5 transitions rarer and more meaningful.
streamlit, simulation, entropy, negentropy,
recursive-modeling, symbolic-field-theory,
entropic-recursion-framework, hyperverse
AlphaGate operationalizes concepts from:
- Entropic Recursion Framework (ERF v3.0)
- Symbolic Field Theory (SFT v3.0+ / v4.0)
- Symbolic Gravity v2.1
- Fractal Cosmic Weaver Framework v2.0
- Observer Framework / I-Point Theory
- Recursive Soteriology — Five-Level Model
MIT License — open for research, teaching, and symbolic experimentation.
Built as part of the Symbolic Negentropy Constellation exploration.
Inspired by ERF, SFT, FCWF, Observer Theory, NOMAS, Symbolic Gravity, and Recursive Soteriology.