IQ-vs-Recursion: A Multi-Scale Model of Intelligence v2.0 Simulation

A Python simulation framework demonstrating that recursive, multi-scale intelligence vastly outperforms traditional scalar IQ in environments characterized by drift, pressure, and collapse events.

This repository provides the computational foundation for the paper "Recursion vs. IQ: Toward a Multi-Scale Model of Intelligence v2.0", implementing its core theoretical constructs—Symbolic Field Theory (SFT v4.0), the Entropic Recursion Framework (ERF v3.0), and the Collapse Predicate—to model intelligence not as a static score, but as a dynamic capacity for survival and coherence.

🧠 Core Thesis

Traditional IQ measures problem-solving in stable conditions but fails to capture adaptive resilience. This project posits that true intelligence is the recursive capacity to:

• Maintain coherence (Ω_eff) under entropic pressure (γ_eff).
• Export invariants (Δℰ) across collapse horizons.
• Persist meaning through narrative weaving (FCWF v2.0).

Intelligence is recast from a scalar metric (IQ) into a fitness function for survival.

⚙️ Simulation Highlights

• Agents: IQAgent (scalar intelligence) vs. RecursiveAgent (multi-scale resilience).
• Dynamics: Implements the formal collapse_predicate: C(x,t) = (ψ_eff < ε) ∨ (γ_eff > T_γ ∧ Ω_eff < T_Ω).
• Mechanics: Models the conservation law Δ𝒮 + Δℰ = 0 during collapse events.
• Metrics: Tracks collapses, coherence survival, resilience scores, and export success.

📊 Result Summary

The simulation consistently demonstrates the superiority of recursive intelligence:

Metric	IQ Agent	Recursive Agent
Collapse Events	High (~12)	Minimal (~1)
Coherence Survival	Low (~45%)	Perfect (100%)
Resilience Score	~0.000	~0.884
Export Efficiency	Low (25% success)	Perfect (100% success)

🚀 Quick Start

Prerequisites

• Python 3.7+ installed from python.org.

1. Clone the Repository

git clone https://github.com/Maxbanker/IQ-vs-Recursion.git
cd IQ-vs-Recursion

Installation and Usage

2. Install Dependencies

pip install numpy matplotlib scipy

3. Run the Core Simulation

python IQvRecursion.py

Outputs results to console and generates recursion_vs_iq_simulation.png.

4. (Optional) Run Population Study

python IQvRecursion.py

Runs statistical analysis on 100+ agents, outputs significance tests, and generates population_results.png.

📁 Repository Structure

.
├── IQvrecursion.py                 # Population-level statistical analysis
├── requirements.txt                # Python dependencies
└── README.md                       # This file

Chat Interface

Below is an embedded chat interface for interacting with the project. Use this to ask questions, run simulations, or explore the theoretical frameworks.

​ Welcome to the Recursion vs. IQ Simulation Chat! Type your queries below to interact with the system, explore results, or dive into the theoretical foundations.

📚 Theoretical Foundation

This work is built upon a comprehensive framework including:

• SFT v4.0 (Symbolic Field Theory)
• ERF v3.0 (Entropic Recursion Framework)
• FCWF v2.0 (Fractal Cosmic Weaver Framework)
• I-Point Theory v2.1 & Observer Framework v4.0

🔮 Applications

• Designing AI systems resistant to catastrophic forgetting.
• Analyzing civilizational resilience and cultural memory.
• Developing a new paradigm for measuring cognitive fitness.

📄 License

This work is licensed under a MIT License.

👤 Authors

• Steven Lanier-Egu - Theoretical Framework

“IQ measures local problem-solving but fails under collapse. Recursion provides a universal metric of intelligence: continuity, survival, and invariant export across collapse horizons.”