This is a conceptual prototype of the Onion Fractal Model. Mode creation, linking, and adequacy calculation are simulated for demonstration.
Onion Fractal Model
Visualize recursive knowledge through causal-ideational adequacy in real-time. Navigate the fractal structure of understanding.
About the Onion Fractal Model
Theoretical Foundation
The Onion Fractal Model represents "God as the Living Fractal" - a formal ontological system of recursive substance. Each mode (node) represents an idea-of-idea structure, where understanding builds through layered, self-similar patterns of causality and ideation.
Core Concepts
- Modes: Individual nodes representing ideas, concepts, or understandings
- Causal Links: Cause→effect relationships showing how ideas influence each other
- Ideational Links: Concept→concept relationships showing abstract connections
- Adequacy (ΔA): Measure of understanding clarity (0-10) based on depth and connections
How It Works
The fractal operates through recursive adequacy scoring. Each mode's understanding is enhanced by its connections to other modes, creating a self-reinforcing network of knowledge. Deeper modes (higher depth) represent more refined understanding, while connections create pathways for knowledge flow.
Practical Applications
- Knowledge Mapping: Visualize complex understanding networks
- Learning Optimization: Identify gaps in understanding for focused study
- Philosophical Inquiry: Explore causal-ideational relationships in complex topics
- Research Planning: Structure investigation through recursive adequacy building
Implementation Roadmap
1. Fractal Mode Graph API
Scalable backend using graph databases (Neo4j/Dgraph) with Python/Node.js APIs for creating modes, linking causes and ideas, and tagging nodes with metadata (ΔA, depth, timestamp, context).
2. Vector Assignment Logic
Language model embeddings (OpenAI, HuggingFace) to convert reflections into high-dimensional vectors for similarity search and recursive idea-of-idea structure representation.
3. Reflective Agent Layer
SpiñO Logic integration for traversing causality and ideation, computing ΔA for any node, and suggesting next reflective focus for attention or correction.
4. Metaphysical Reasoning SDK
Modular developer toolkit with REST/GraphQL APIs, UI components (graph viewer, reflection console), and export capabilities for AI agents, education tools, and research visualizations.
Recursive layers