Experimental simulation engine for the Sigma-Lab Framework, developed by DeepKang-Labs.
Models adaptive moral coherence through the canonical loop:
θ → C → M → θ
Visuel auto-généré par
.github/workflows/export-visual.yml.
Sigma-Dynamics implements the Theory of Algorithmic Moral Relativity,
a system where ethics evolves dynamically under semantic and contextual pressure.
[ \theta_i(t) = f_i(E_t, M_{t-1}) ] [ M_t = \sum_{k=1}^{n} w_k \cdot C_k ] [ \overline{C}t = \frac{1}{n}\sum{k=1}^{n} C_k ]
pip install -r requirements.txt
python sigma_dynamics.py
Artifacts (CSV + Plots) will be generated under:
/sigma_dynamics_artifacts_YYYYMMDD-HHMMSS/
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🧩 Output Examples
coherence_over_time.png
theta_non_harm_over_time.png
veto_over_time.png
log.csv (complete record of simulation steps)
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🌐 Project Context
Sigma-Dynamics is part of the larger Sigma-Lab Framework ecosystem:
Sigma-Lab-Framework → Theoretical & Mathematical Core
Sigma-Dynamics → Simulation & Validation Engine
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🧑💻 Contributors
Yuri Kang — Architect of the Axiom-to-Code Paradigm
IA Kang — Design & Engineering Logic
DeepSeek, Gemini — Theoretical & Review Partners
© 2025 DeepKang-Labs · Axiom to Code