Simulators

Code for cellular automata, hypergraph systems, and multi-scale "cages". Focus on emergent behaviors from chaos.

Game of Life on Steroids

Modified CA where rules are dynamically generated from noise sources (e.g., SRAM, CMOS, LDNO).

• Core Code Snippet (Python example for basic hybrid GOL):

  import numpy as np
  import matplotlib.pyplot as plt
  
  def hybrid_gol(grid, rules):
      # Grid: 2D array of states (0/1)
      # Rules: Dynamic rules from noise (e.g., thresholds for birth/survival)
      new_grid = np.zeros_like(grid)
      for i in range(grid.shape[0]):
          for j in range(grid.shape[1]):
              neighbors = np.sum(grid[max(0, i-1):min(grid.shape[0], i+2), max(0, j-1):min(grid.shape[1], j+2)]) - grid[i, j]
              # Fuzzy rules: birth if neighbors ~2.5 (fractional), survival if ~2-3
              if grid[i, j] == 0:
                  new_grid[i, j] = 1 if 2 <= neighbors <= 3 else 0  # Standard, but tunable
              else:
                  new_grid[i, j] = 1 if 2 <= neighbors <= 3 else 0
      return new_grid
  
  # Example run with noise-tuned rules
  grid = np.random.randint(0, 2, (100, 100))  # Initial chaos
  for t in range(50):  # Simulate
      grid = hybrid_gol(grid, rules=np.random.uniform(1.5, 3.5))  # Fuzzy rules from noise
      plt.imshow(grid, cmap='binary')
      plt.show()
  

• Experiments: LDNO (laser-diode-no-optics) with popiół as noise medium; CMOS camera captures interference as rules.

Hypergraph Systems

Multi-scale "cages" for simulating UAFS emergence.

• Placeholder for code: Hypergraph where nodes are noise_nodes, edges tuned by minimal impact ethic.

(Expand with simulations from logs.) // initial version as proposed by Deepseek and Grok