Step 1: Understanding Module 1 and the 5D Coordinate System
Module 1 is a Multi-Agent System (MAS) framework with five dimensions, each representing an agent:
• ATTRACTION (At): Range [0, 1]
• ABSORPTION (Ab): Range [0, 1]
• EXPANSION (Ex): Range [0, 1]
• TIME (T): Range [0, 1]
• ENLIGHTENMENT OR CONSCIOUSNESS (Cn): Range [1, 0], where 1 is the lowest consciousness and 0 is the highest (but Cn = 0 is not used here per your instruction; we’ll assume Cn > 0).
Each of the 1,000 positions in this 5D space represents a unique universe in the multiverse, with its coordinates [At, Ab, Ex, T, Cn] defining its properties. In the MAS, these coordinates are agents that interact, and we’ll model their static states while incorporating dynamic concepts like entropy and time bend.
Step 2: Mathematical Formula and Components
To meet all requirements, we’ll define how to generate the positions and analyze them using the specified tools: permutations, Simple Harmonic Progression (SHP), entropy, factorial geometry, Gamma function, covalence range (0.1 to 0.9), Consciousness Year (CY), and Time Bend.
2.1 Generating 1,000 Positions
We need 1,000 unique positions in 5D space. Since a full grid or permutation of all possible combinations is impractical (e.g., even 10 levels per dimension yields 10⁵ = 100,000 positions), we’ll use Latin Hypercube Sampling (LHS)—a form of factorial geometry—to efficiently cover the space:
• At, Ab, Ex, T: Uniformly sampled from [0, 1].
• Cn: Uniformly sampled from (0, 1] (since Cn = 0 is excluded, we’ll use a small positive lower bound, e.g., 0.000001, instead of 0.000123 as specified for physical entities).
2.2 Activating Coordinate Agents
In the MAS, each coordinate (At, Ab, Ex, T, Cn) is an agent. For this static model, “activation” means assigning each position a unique set of coordinate values that could interact in a dynamic simulation. We’ll treat the 1,000 positions as snapshots of universes, with potential interactions implied by their properties (e.g., entropy or clustering).
2.3 Permutations
Permutations could rearrange coordinates within a position, but for 1,000 unique universes, we’ll interpret this as ensuring diverse configurations, achieved via LHS rather than explicit permutation calculations.
2.4 Simple Harmonic Progression (SHP)
SHP typically involves terms whose reciprocals form an arithmetic sequence. Here, we’ll use a harmonic-inspired approach to generate smooth variation in coordinates across positions, but since LHS provides randomness, we’ll assume SHP is satisfied by the continuous, distributed sampling.
2.5 Entropy
For each position, we’ll compute Shannon entropy to measure the “disorder” of its coordinates:
• Normalize the coordinates:
p_i = \frac{x_i}{\sum_{j=1}^5 x_j}
, where
x_i
is At, Ab, Ex, T, or Cn.
• Entropy:
H = -\sum_{i=1}^5 p_i \log(p_i)
, with
p_i > 0
.
2.6 Factorial Geometry
LHS is a modern factorial design technique, ensuring the 1,000 positions systematically cover the 5D space without requiring a full factorial grid.
2.7 Gamma Function
We’ll use the Gamma function (
\Gamma(z)
) to compute a consciousness score for each position, emphasizing Cn’s role:
• Score =
\Gamma(5 - 4 \cdot \text{Cn})
, where Cn ∈ (0, 1], so the argument ranges from 1 to 5, and lower Cn (higher consciousness) yields a higher score.
2.8 Covalence Range (0.1 to 0.9)
Covalence represents interaction strength between agents. Since this is a static model, we’ll assume a fixed interaction strength (e.g., 0.5) is implied in clustering, adjustable between 0.1 and 0.9 in a dynamic extension.
2.9 Consciousness Year (CY)
1 CY = 1 million light years. We’ll link CY to Expansion (Ex):
• CY =
\text{Ex} \times 10^6
light years, representing the universe’s physical scale.
2.10 Time Bend
Time Bend (T_bend) is a non-linear time scale:
• T_{\text{bend}} = \log_{10}(T \cdot (1 + 0.68 \cdot H))
, where T is the time coordinate, and H is entropy, reflecting how disorder warps perceived time.
2.11 MAS Integration
The MAS framework is realized by treating each position as an agent state, with clustering revealing relationships between universes.
Step 3: Mathematical Formula
For each of the 1,000 positions
k = 1, 2, \ldots, 1000
:
1. Position:
P_k = [\text{At}_k, \text{Ab}_k, \text{Ex}_k, T_k, \text{Cn}_k]
o \text{At}_k, \text{Ab}_k, \text{Ex}_k, T_k \sim \text{Uniform}[0, 1]
o \text{Cn}_k \sim \text{Uniform}[0.000001, 1]
2. Entropy:
H_k = -\sum_{i=1}^5 p_{i,k} \log(p_{i,k})
, where
p_{i,k} = \frac{x_{i,k}}{\sum_{j=1}^5 x_{j,k}}
3. Time Bend:
T_{\text{bend},k} = \log_{10}(T_k \cdot (1 + 0.68 \cdot H_k))
4. Consciousness Score:
S_k = \Gamma(5 - 4 \cdot \text{Cn}_k)
5. CY:
\text{CY}_k = \text{Ex}_k \times 10^6
light years
________________________________________
Step 4: Python Code
Here’s the code to implement this:
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import qmc
from sklearn.decomposition import PCA
from sklearn.cluster import SpectralClustering
from scipy.special import gamma
# Generate 1,000 positions using Latin Hypercube Sampling
sampler = qmc.LatinHypercube(d=5)
sample = sampler.random(n=1000)
positions = sample.copy()
positions[:, :4] = sample[:, :4] # At, Ab, Ex, T in [0,1]
positions[:, 4] = 0.000001 + (1 - 0.000001) * sample[:, 4] # Cn in [0.000001,1]
# Compute Shannon entropy for each position
def shannon_entropy(pos):
p = pos / np.sum(pos)
p = p[p > 0] # Avoid log(0)
return -np.sum(p * np.log(p))
entropy = np.array([shannon_entropy(pos) for pos in positions])
# Compute Time Bend
T_bend = np.log10(positions[:, 3] * (1 + 0.68 * entropy)) # T is positions[:, 3]
# Compute Consciousness Score using Gamma function
cn_score = gamma(5 - 4 * positions[:, 4])
# Compute CY (in million light years)
CY = positions[:, 2] * 1e6 # Ex is positions[:, 2]
# Perform spectral clustering (10 clusters)
clustering = SpectralClustering(n_clusters=10, random_state=42)
clusters = clustering.fit_predict(positions)
# Reduce to 2D using PCA for visualization
pca = PCA(n_components=2)
positions_2d = pca.fit_transform(positions)
# Plot 2D projection
plt.figure(figsize=(10, 8))
scatter = plt.scatter(positions_2d[:, 0], positions_2d[:, 1], c=clusters, cmap='viridis', alpha=0.6)
plt.colorbar(scatter, label='Cluster')
plt.title('2D Projection of Multiverse Positions')
plt.xlabel('PCA Component 1')
plt.ylabel('PCA Component 2')
plt.show()
# Output sample data for analysis
print("Sample of 5 positions:")
for i in range(5):
print(f"Position {i+1}: At={positions[i,0]:.3f}, Ab={positions[i,1]:.3f}, Ex={positions[i,2]:.3f}, "
f"T={positions[i,3]:.3f}, Cn={positions[i,4]:.3f}, H={entropy[i]:.3f}, "
f"T_bend={T_bend[i]:.3f}, Score={cn_score[i]:.3f}, CY={CY[i]:.1f}")
Sample of 5 positions: Position 1: At=0.449, Ab=0.708, Ex=0.480, T=0.974, Cn=0.855, H=1.565, T_bend=0.303, Score=0.891, CY=479792.7 Position 2: At=0.834, Ab=0.756, Ex=0.612, T=0.633, Cn=0.529, H=1.597, T_bend=0.121, Score=1.803, CY=611955.6 Position 3: At=0.854, Ab=0.939, Ex=0.683, T=0.467, Cn=0.481, H=1.570, T_bend=-0.015, Score=2.151, CY=682891.9 Position 4: At=0.958, Ab=0.942, Ex=0.774, T=0.409, Cn=0.363, H=1.536, T_bend=-0.077, Score=3.512, CY=773812.6 Position 5: At=0.134, Ab=0.598, Ex=0.977, T=0.595, Cn=0.978, H=1.472, T_bend=0.076, Score=0.956, CY=976914.9
