ENTRO-DASA — Dynamic Autonomous Sovereignty Algorithm

Governance Decision Logic

Three-Signal SAM Classification

Every cognitive trajectory evaluated by ENTRO-DASA receives a real-time SAM signal and certified CCS score with full deviation diagnostics.

CCS ≥ 0.95

🟢 CONSISTENCY LOCK

All trajectories within the Consistency Basin B_C. Attractor lock certified. Governance in maintenance mode — β-damping active.

0.70 – 0.95

🟠 MONITORING PHASE

Partial convergence detected. Preventive gravity adjustment active. Inter-trajectory synchronization force engaged to contain drift.

CCS < 0.70

🔴 CRITICAL DEVIATION

Trajectory divergence exceeds θ_crit. Immediate α-amplification of cognitive gravity engaged. Full recapture protocol activated.

Governance Pipeline

Four-Module Cybernetic Architecture

No prior causal graph or model-specific assumptions required. ENTRO-DASA operates as a sovereignty enforcement layer atop any dissipative cognition system.

MODULE 01

DASA Core Engine

DCE

Multi-threaded parallel state update for swarms T₁, T₂, T₃. Barrier-synchronized deviation computation at each time step t.

d_{i,j}(t) = ‖x_{i,j}(t) − A*‖₂
D_j(t) = (1/N) Σ exp(γ_w·d)·d

MODULE 02

Adaptive Linguistic Gravity

ALG

Dynamic restoring force modulated by measured deviation. α=1.05 amplifies on critical divergence; β=0.98 dampens near the attractor.

w_{t+1} = w_t · α^[d>θ] · β^[d≤θ]
θ = 0.80, α = 1.05, β = 0.98

MODULE 03

Consistency Lock

Hard Consistency Basin projection prevents post-lock drift. Phase transition from guided convergence to certified attractor confinement.

x(t+1) = Π_{B_C}[x(t) − γ·w_t·∇V + η]
V(x) = ½k‖x−A*‖² + (λ/4)‖x−A*‖⁴

MODULE 04

Strategic Analytics

SAM

Transforms continuous CCS scores into discrete 🔴🟠🟢 governance decisions. Drives real-time archival and Streamlit dashboard updates.

CCS_j = (1/N) Σ exp(−κ·d²_final)
CCS_sys = (1/3)·Σ_j CCS_j

Quick Start

Deploy Governance in 60 Seconds

pip install entro-dasa

from entro_dasa import DASAGovernor

# Initialize with sovereign attractor at origin
governor = DASAGovernor(attractor=[0.0, 0.0, 0.0])

# X: trajectory state matrix (n_points, 3)
result = governor.run(X, T_max=500)

print(result.label)        # "CONSISTENCY_LOCK" | "MONITORING" | "CRITICAL"
print(result.ccs_score)    # float in [0, 1]
print(result.breakdown)    # {"DCE": 0.94, "ALG": 0.97, "CL": 0.96, "SAM": 0.99}
print(result.entropy_ceri) # Cognitive Entropy Reduction Index (0.94 at σ=0.15)

from entro_dasa import DASAGovernor

governor = DASAGovernor(
    attractor=[0.0, 0.0, 0.0],
    params={
        "alpha":    1.05,  # amplification: critical deviation response
        "beta":     0.98,  # damping: near-attractor softening
        "theta":    0.80,  # critical deviation threshold
        "gamma":    0.05,  # computational step rate
        "kappa_s":  0.10,  # inter-trajectory sync coefficient
        "sigma":    0.15,  # environmental noise level
    }
)
result = governor.run(X, T_max=500, n_swarms=3)
print(result.ccs_score, result.label)

from entro_dasa import DASAGovernor
from entro_dasa.adaptive import AdaptiveFeedback

# Load outer-loop adaptive parameter optimizer
adapter = AdaptiveFeedback.from_pretrained("default")
governor = DASAGovernor(
    attractor=[0.0, 0.0, 0.0],
    adapter=adapter
)
# Adapter recalibrates α, β, θ, κ_s at runtime
result = governor.run(X, T_max=500)
print(result.adapted_params)   # final calibrated parameter values
print(result.meta_confidence)  # P(Lock | score_vector)

# Launch real-time 3D Streamlit dashboard
# Live trajectory rendering · CCS time-series · SAM signals

$ streamlit run examples/streamlit_live.py

# Dashboard at: http://localhost:8501
# Features:
#   · 3D Plotly point-cloud: T₁ T₂ T₃ swarms + A* attractor
#   · Live CCS / deviation time-series (200-step rolling window)
#   · Consistency Basin sphere overlay
#   · 🔴🟠🟢 SAM signal status panel
#   · JSON/CSV archive with SHA-256 checksums

Citation

Cite ENTRO-DASA in Your Research

If ENTRO-DASA contributes to your research, please use one of the citation formats below.

@software{baladi2026entrodasa_pypi,
  author    = {Baladi, Samir},
  title     = {{ENTRO-DASA}: Dynamic Autonomous Sovereignty Algorithm},
  year      = {2026},
  version   = {10.2.0},
  publisher = {Python Package Index},
  url       = {https://pypi.org/project/entro-dasa},
  note      = {Python package, MIT License, EntropyLab Series E-LAB-12}
}

@dataset{baladi2026entrodasa_zenodo,
  author    = {Baladi, Samir},
  title     = {{ENTRO-DASA}: Dynamic Autonomous Sovereignty Algorithm —
               Research Paper and Simulation Data},
  year      = {2026},
  publisher = {Zenodo},
  version   = {10.2.0},
  doi       = {10.5281/zenodo.20353988},
  url       = {https://doi.org/10.5281/zenodo.20353988},
  series    = {E-LAB-12}
}

@misc{baladi2026entrodasa_osf,
  author    = {Baladi, Samir},
  title     = {{ENTRO-DASA} Framework: Pre-registered Study Protocol for
               Cybernetic Governance of Dissipative Cognition Systems},
  year      = {2026},
  publisher = {Open Science Framework},
  doi       = {10.17605/OSF.IO/XXXXX},
  url       = {https://doi.org/10.17605/OSF.IO/XXXXX},
  note      = {OSF Preregistration}
}

@article{baladi2026entrodasa,
  author  = {Baladi, Samir},
  title   = {{ENTRO-DASA}: A Cybernetic Framework for Multi-Trajectory
             Attractor Guidance and Self-Regulating Consistency Locks
             in Dissipative Cognition Systems},
  year    = {2026},
  month   = {May},
  series  = {E-LAB-12},
  version = {10.2.0},
  doi     = {10.5281/zenodo.20353988},
  url     = {https://doi.org/10.5281/zenodo.20353988}
}

Baladi, S. (2026). ENTRO-DASA: A Cybernetic Framework for
Multi-Trajectory Attractor Guidance and Self-Regulating
Consistency Locks in Dissipative Cognition Systems
(Version 10.2.0, Series E-LAB-12). Zenodo.
https://doi.org/10.5281/zenodo.20353988