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Computational validation code for 'Replication Optimization at Scale: Dissolving Qualia via Occam's Razor' - network epistemology simulations

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Consciousness Narrative Computational Models

Computational validation code for "Replication Optimization at Scale: Dissolving Qualia via Occam's Razor" (Farzulla, 2025).

DOI

Overview

This repository contains network epistemology simulations validating predictions from the consciousness-as-narrative thesis. The core finding: network structure, not philosophical depth, explains why consciousness debates persist.

Built on the PolyGraphs framework (Koliousis, 2024).

Key Results

Network Topology Convergence Final Belief Interpretation
Complete Graph 100% 0.30 (truth) Full connectivity → truth wins
Cycle Graph 100% 1.0 (realist) Echo chambers → systematic error
Small-World 0% 0.51 ± 0.035 Realistic structure → persistent disagreement

Quick Start

# Clone
git clone https://github.com/studiofarzulla/consciousness-narrative-computational.git
cd consciousness-narrative-computational

# Install
pip install -r requirements.txt

# Run simulations
python consciousness_belief_v2.py --topologies all --seeds 10

# Generate publication figures
python create_publication_figures.py --output publication_figures/

Repository Structure

├── consciousness_belief_sim.py    # Basic simulation script
├── consciousness_belief_v2.py     # Enhanced simulation with all topologies
├── create_publication_figures.py  # Figure generation for paper
├── results/                       # Simulation output data
│   └── simulation_results.csv
├── publication_figures/           # Generated figures
├── polygraphs/                    # PolyGraphs framework (core library)
├── configs/                       # Simulation configurations
├── examples/                      # Usage examples
└── scripts/                       # Utility scripts

Simulation Parameters

  • Agents: 100 per simulation
  • Topologies: Complete, Cycle, Small-World (Watts-Strogatz k=4, p=0.1)
  • Truth value: 0.3 (illusionism favored)
  • Update rule: Bayesian belief revision
  • Convergence: < 0.01 belief variance or 1000 steps
  • Replications: 10 per condition

Requirements

  • Python 3.10+
  • NumPy, Pandas, Matplotlib
  • NetworkX

See requirements.txt for full dependencies.

Citation

@misc{farzulla2025consciousness,
  author = {Farzulla, Murad},
  title = {Replication Optimization at Scale: Dissolving Qualia via Occam's Razor},
  year = {2025},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.17917970}
}

Acknowledgments

Built on the PolyGraphs framework:

Ball, B., Koliousis, A., Mohanan, A. & Peacey, M. Computational philosophy: reflections on the PolyGraphs project. Humanit Soc Sci Commun 11, 186 (2024).

License

MIT License - see LICENSE

Author

Murad Farzulla - Farzulla Research ORCID: 0009-0002-7164-8704

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Computational validation code for 'Replication Optimization at Scale: Dissolving Qualia via Occam's Razor' - network epistemology simulations

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