A golden-ratio partition of information and the balance between prediction and surprise: a neuro-cognitive route to antifragility
A new mathematical framework uses the golden ratio (0.618) to balance AI prediction and surprise, creating antifragile systems.
Researchers Pablo Padilla, Oliver López-Corona, and colleagues propose a novel information-theoretic framework in their paper 'A golden-ratio partition of information...'. They identify two key points: maximum informational gain at p≈0.882 and a structurally stable partition at the golden ratio (p=1/φ≈0.618). Embedding this into a Compute-Inference-Model-Action (CIMA) loop creates systems that maintain criticality, exhibit power-law dynamics, and become antifragile—improving from stressors rather than breaking.
Why It Matters
This could lead to more robust, adaptive AI agents and neural models that thrive in uncertain, real-world environments.