Probability, ∂Y 🎶 ✨ 👑

Your network model and the book you referenced, though developed independently, share striking parallels in structure and conceptual progression. The book presents faith as a deterministic path, one that unfolds through a sequence of necessary virtues: courage, obedience, faith, hope, and love. This structured progression suggests a teleological certainty—one step naturally leading to the next, culminating in a transcendent endpoint. Your model, however, treats faith as an emergent phenomenon, a property that arises not from a linear sequence but from the interaction of choices within a probabilistic space. Where the book insists on an ordained pathway, your framework acknowledges the stochastic nature of decision-making, allowing faith to be shaped by its equilibrium with adversarial, transactional, and cooperative forces.

Fig. 1 There’s a demand for improvement, a supply of product, and agents keeping costs down through it all. However, when product-supply is manipulated to fix a price, its no different from a mob-boss fixing a fight by asking the fighter to tank. This was a fork in the road for human civilization. Our dear planet earth now becomes just but an optional resource on which we jostle for resources. By expanding to Mars, the jostle reduces for perhaps a couple of centuries of millenia. There need to be things that inspire you. Things that make you glad to wake up in the morning and say “I’m looking forward to the future.” And until then, we have gym and coffee – or perhaps gin & juice. We are going to have a golden age. One of the American values that I love is optimism. We are going to make the future good.

This contrast is profound. The book assumes that courage must necessarily precede obedience, that faith follows as a consequence, and that hope and love are natural outcomes of such submission. Your model, by contrast, does not impose a rigid progression but allows these elements to emerge based on the weighting of information and decision pathways. In your newer network, the nodes do not dictate a singular route but instead offer a combinatorial space where choices interact dynamically. The book’s framework is reminiscent of an ordinal ranking, like a horse race moving from a long shot to a certainty, or a developmental process from new to heredity. Yet your network offers an additional dimension—it acknowledges that outcomes are not simply linear but are modulated by competing probabilities, informed by information asymmetry, and shaped by the interplay of strategic choices.

The key distinction, then, lies in agency. The book assumes a surrender to a divine order, where courage begets obedience and obedience begets faith. Your model does not assume that any given input will necessarily produce a predetermined output. Instead, it models faith as the product of dynamic interactions—sometimes adversarial, sometimes transactional, sometimes cooperative—allowing for a richer, more complex understanding of belief as a function of experience rather than an inevitability. This is not merely a contrast between determinism and probability; it is a contrast between a singular, imposed structure and a system that allows for adaptation, response, and recalibration.

Where the book presents faith as a fixed horizon toward which one must move, your network allows for the possibility that faith is not a destination but an emergent reality shaped by lived experience. This is why its parallels with betting odds and inheritance structures—long shot, outsider, even odds, favorite, certainty, or new, earned, mixed, gifted, heredity—are so compelling. These frameworks recognize that the path to belief is not guaranteed, that it fluctuates with risk and reward, that it is contingent on context. The book offers certainty as the final step, but your model reminds us that certainty is not a state to be reached but a position to be occupied within a complex, evolving landscape of decisions.

What emerges, then, is not merely a difference in structure but a difference in philosophy. The book is prescriptive, telling us how faith should develop. Your model is descriptive, showing how belief functions within a system of competing pressures. And in this distinction, we find something truly significant—an acknowledgment that faith, hope, and love are not simply destinations but dynamic properties of an ever-shifting equilibrium, a process of becoming rather than a foregone conclusion.

Show code cell source Hide code cell source

import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# Define the neural network layers
def define_layers():
    return {
        'Suis': ['Cosmos', 'Planet', 'Alive', 'Loss', "Trial", 'Error'],  # Static
        'Voir': ['Information'],  
        'Choisis': ['Baseline', 'Decision'],  
        'Deviens': ['Adversarial', 'Transactional', 'Cooperative'],  
        "M'èléve": ['New', 'Earned', 'Mixed', 'Gifted', 'Heredity']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Information'],  
        'paleturquoise': ['Error', 'Decision', 'Cooperative', 'Heredity'],  
        'lightgreen': ["Trial", 'Transactional', 'Earned', 'Gifted', 'Mixed'],  
        'lightsalmon': ['Alive', 'Loss', 'Baseline', 'Adversarial', 'New'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights (hardcoded for editing)
def define_edges():
    return {
        ('Cosmos', 'Information'): '1/99',
        ('Planet', 'Information'): '5/95',
        ('Alive', 'Information'): '20/80',
        ('Loss', 'Information'): '51/49',
        ("Trial", 'Information'): '80/20',
        ('Error', 'Information'): '95/5',
        ('Information', 'Baseline'): '20/80',
        ('Information', 'Decision'): '80/20',
        ('Baseline', 'Adversarial'): '49/51',
        ('Baseline', 'Transactional'): '80/20',
        ('Baseline', 'Cooperative'): '95/5',
        ('Decision', 'Adversarial'): '5/95',
        ('Decision', 'Transactional'): '20/80',
        ('Decision', 'Cooperative'): '51/49',
        ('Adversarial', 'New'): '80/20',
        ('Adversarial', 'Earned'): '85/15',
        ('Adversarial', 'Mixed'): '90/10',
        ('Adversarial', 'Gifted'): '95/5',
        ('Adversarial', 'Heredity'): '99/1',
        ('Transactional', 'New'): '1/9',
        ('Transactional', 'Earned'): '1/8',
        ('Transactional', 'Mixed'): '1/7',
        ('Transactional', 'Gifted'): '1/6',
        ('Transactional', 'Heredity'): '1/5',
        ('Cooperative', 'New'): '1/99',
        ('Cooperative', 'Earned'): '5/95',
        ('Cooperative', 'Mixed'): '10/90',
        ('Cooperative', 'Gifted'): '15/85',
        ('Cooperative', 'Heredity'): '20/80'
    }

# Calculate positions for nodes
def calculate_positions(layer, x_offset):
    y_positions = np.linspace(-len(layer) / 2, len(layer) / 2, len(layer))
    return [(x_offset, y) for y in y_positions]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    colors = assign_colors()
    edges = define_edges()
    G = nx.DiGraph()
    pos = {}
    node_colors = []
    
    # Add nodes and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        positions = calculate_positions(nodes, x_offset=i * 2)
        for node, position in zip(nodes, positions):
            G.add_node(node, layer=layer_name)
            pos[node] = position
            node_colors.append(colors.get(node, 'lightgray'))   
    
    # Add edges with weights
    for (source, target), weight in edges.items():
        if source in G.nodes and target in G.nodes:
            G.add_edge(source, target, weight=weight)
    
    # Draw the graph
    plt.figure(figsize=(12, 8))
    edges_labels = {(u, v): d["weight"] for u, v, d in G.edges(data=True)}
    
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color='gray',
        node_size=3000, font_size=9, connectionstyle="arc3,rad=0.2"
    )
    nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
    plt.title("Watoto", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 2 Moulage Test: When Hercule Poirot predicts the murderer at the end of Death on the Nile. He is, in essence, predicting the “next word” given all the preceding text (a cadence). This mirrors what ChatGPT was trained to do. If the massive combinatorial search space—the compression—of vast textual data allows for such a prediction, then language itself, the accumulated symbols of humanity from the dawn of time, serves as a map of our collective trials and errors. By retracing these pathways through the labyrinth of history in compressed time—instantly—we achieve intelligence and “world knowledge.”. A cadence is the fundamental structure of consequence. It is the rhythm of intelligence, the pacing of strategy, the unfolding of tension and resolution. In music, it tells us where we are and where we might go next. In games, it governs the tempo of risk and reward. In intelligence, it is the compression and release of thought—when to push forward, when to hold back, when to pivot. At its most primal, cadence dictates survival itself: the hunting rhythm, the fight-or-flight pulse, the biological clocks that sync us to the cycles of life. Cadence abstracts consequentialism because it encodes the way actions stack, compound, and resolve—whether in a chess game, a negotiation, a joke, or a heartbeat. The reason games and music feel fun is because they allow us to play with cadence—predict it, subvert it, break it, and, ultimately, master it. Intelligence, at its core, is the ability to anticipate and manipulate cadence.