Endogenously-Constrained AI (ECA)

Endogenously-Constrained AI Toward Structurally Stable and Auditable Artificial Intelligen.

Télécharger • 1.94MB

B_Endogenously-Constrained AI (ECA) and Crowd-Based Dynamics (CBD).pdf

Télécharger PDF • 406KB

C_Unified Governability Framework for Human and Artificial Collective Systems.pdf

Télécharger PDF • 3.73MB

D_Applied RAG-RES Operational Framework.pdf

Télécharger PDF • 2.16MB

E_Dialogic Dynamics, Mimetic Entropy, and Governability (RAG-RES).pdf

Télécharger PDF • 3.23MB

F_ Foundational Structural Integration of Endogenously-Constrained Artificial Intelligence.

Télécharger • 811KB

G_ Toward a Governable Artificial Intelligence (ECA).pdf

Télécharger PDF • 1.40MB

Endogenously-Constrained AI (ECA) proposes a new paradigm for designing stable and governable artificial intelligence systems. It shifts the focus from performance optimization to internal structural coherence and constraint-based stability. ECA models AI systems as complex adaptive systems subject to endogenous limits. The framework emphasizes that intelligence must remain bounded to avoid instability and drift. It introduces constraint operators that regulate system behavior from within rather than through external control. This approach aligns with CBD by integrating saturation, thresholds, and governability into AI dynamics. ECA highlights the role of memory, internal feedback loops, and structural consistency. It addresses risks such as uncontrolled amplification, misalignment, and loss of coherence. The framework proposes that stability emerges from internal constraint rather than imposed regulation. It also explores the limits of scalability and the cost of maintaining system coherence. ECA connects AI behavior with collective dynamics and systemic stability principles.Overall, it defines a path toward auditable, stable, and structurally bounded artificial intelligence systems.