Veiled Resentment

Ignorance 🪙 🎲 🎰 is the realm of pure chance, where knowledge holds no sway. In this domain, the odds are fixed, immutable, and indifferent to any strategic input. A coin flip remains a coin flip, no matter how many times one studies its trajectory. A roulette wheel does not favor the learned over the naïve. Dice obey no master. These games exist in a universe without leverage, where outcomes remain forever untethered from intellect or will. This is the first layer—the state of raw, unrefined reality where one either accepts randomness or succumbs to the illusion of control. Here, the yellow node represents the very input of chance itself: a constitution, a wheel, a contract, a bequest, a text—all mechanisms through which the immutable is processed into cognition. Ignorance is the substrate from which all else emerges, the chaotic sea of possibilities that precedes any coherent attempt at agency.

Fig. 23 Inheritence, Heir, Brand, Tribe, Religion. We’ve described these stages of societal history. We’re discussing brand: an agent exerts an ecological loss function that some consider “creative destruction”.

Bequest ♥️ ♦️ ♣️ elevates the game beyond sheer randomness into the domain of mind games, deception, and psychological warfare. This is the world of poker, where knowledge is partial, veiled, and dynamic. Unlike ignorance, where no action matters, in this realm, perception and misperception dictate success. Bluffing, reading opponents, and calibrating risk become the defining variables. What is known is not as important as what others believe to be known. Every card played is a move in an intricate, iterative struggle for control over perception. Here, the yellow node is not merely an input but a mask, a shifting representation of reality manipulated through skill, intuition, and controlled aggression. The uncertainty in this layer is no longer absolute but strategic, allowing the cunning to sculpt reality from the fog of deception.

Strategy 🏇 🏎️ exists at the razor’s edge, where victory is decided by fractions of a second, by the slightest tweak in execution. This is the world of horse racing, Formula One, and Olympic sprints—where worthy adversaries clash, and the margin between triumph and failure is almost imperceptible. In these contests, every detail matters: the angle of a turn, the stride pattern, the slipstream. Unlike poker, where hidden knowledge reigns, or roulette, where nothing matters, here the game is defined by optimization under extreme pressure. The adversarial nature is heightened—an inch gained is an inch stolen from the competitor. Here, the yellow node ceases to be a mask and instead becomes a razor-thin equilibrium, an ever-oscillating balance between efficiency, aggression, and restraint. Knowledge is now fully weaponized into an art of split-second execution, where mastery lies in the infinitesimal refinements that separate dominance from oblivion.

Game Information Levels

Level	Description	Games	Node Representation
Tactic	Pure chance, no knowledge influences outcome.	Roulette, Coin Toss, Dice Roll 🎲🎰🪙	Raw input, immutable randomness 🎡📜📖🗡️👩🏾🛡️
Informational	Mind games, deception, partial information.	Poker, Bluffing, Psychological Warfare ♥️♦️♣️	Masks, shifting perception 🎭🔮🃏
Strategic	Razor-thin margin victories, optimization under pressure.	Horse Racing, Formula 1, Sprinting 🏇🏎️	Equilibrium, optimization ⚖️🕰️⚡
Operational	Deterministic contests, skill guarantees probabilistic success.	Chess, Warfare, Resource Management 🤺♟️👑	Archived wisdom, deterministic mastery 📚🧠🏆
Existential	Certainty, purpose-driven action beyond strategy.	Faith, Love, Destiny, Enlightenment 🎼🎶🎻 🥅🎯💡🕊️✨	Inevitability, realization, transcendence 🚀🔱⏳

Fig. 24 Athena & Apollo: The Sandwitch of Knowledge Whether its Plato, Bacon, or Aristotle, these folks and their ideas are sandwitched between Athena’s shield and Apollo’s Lyre, symbols ranging from the adversarial risk to cooperative protection.

Knowledge 🤺 ♟️ 👑 is the realm of deterministic contests, where skill and preparation guarantee probabilistic success. Chess, warfare, and resource-driven conflicts define this layer, where the outcome is dictated not by deception, not by split-second instincts, but by a mastery of systems and structures. Unlike the previous layer, where razor-thin margins determine fate, here, outcomes are predictable given enough information. The player with superior knowledge—better opening preparation, deeper calculation, greater resource allocation—wins. While ignorance offers no leverage, and poker hides the truth, this layer is the opposite: everything is visible, and those who study, plan, and execute with precision will triumph. Here, the yellow node is an archive, a body of accumulated wisdom that can be referenced and deployed at will. In this world, intelligence is not merely a tool—it is the very currency of power.

Certainty 🥅 🎯 is the final layer, where the game ends, and purpose takes over. Whether it manifests as religious conviction, love at first sight, or the unwavering pursuit of an ultimate goal, certainty transcends strategy and adversarial struggle. The outcome is no longer up for debate—it simply is. This is the domain of the fanatic, the artist, the martyr, the saint. Unlike all previous layers, where action alters probability, here action is merely the fulfillment of a known conclusion. The athlete who visualizes victory before the race has begun, the lover who is drawn inexorably to another, the believer who follows their faith without hesitation—these individuals exist in a space beyond calculation. The yellow node here is no longer an input but an inevitability, the final transformation of knowledge into destiny. The game is over; the outcome is realized. All that remains is the cadence of fulfillment.

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': ['Foundational', 'Grammar', 'Syntax', 'Punctuation', "Rhythm", 'Time'],  # Static
        'Voir': ['Data Flywheel'],  
        'Choisis': ['LLM', 'User'],  
        'Deviens': ['Action', 'Token', 'Rhythm.'],  
        "M'èléve": ['Victory', 'Payoff', 'NexToken', 'Time.', 'Cadence']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Data Flywheel'],  
        'paleturquoise': ['Time', 'User', 'Rhythm.', 'Cadence'],  
        'lightgreen': ["Rhythm", 'Token', 'Payoff', 'Time.', 'NexToken'],  
        'lightsalmon': ['Syntax', 'Punctuation', 'LLM', 'Action', 'Victory'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights (hardcoded for editing)
def define_edges():
    return {
        ('Foundational', 'Data Flywheel'): '1/99',
        ('Grammar', 'Data Flywheel'): '5/95',
        ('Syntax', 'Data Flywheel'): '20/80',
        ('Punctuation', 'Data Flywheel'): '51/49',
        ("Rhythm", 'Data Flywheel'): '80/20',
        ('Time', 'Data Flywheel'): '95/5',
        ('Data Flywheel', 'LLM'): '20/80',
        ('Data Flywheel', 'User'): '80/20',
        ('LLM', 'Action'): '49/51',
        ('LLM', 'Token'): '80/20',
        ('LLM', 'Rhythm.'): '95/5',
        ('User', 'Action'): '5/95',
        ('User', 'Token'): '20/80',
        ('User', 'Rhythm.'): '51/49',
        ('Action', 'Victory'): '80/20',
        ('Action', 'Payoff'): '85/15',
        ('Action', 'NexToken'): '90/10',
        ('Action', 'Time.'): '95/5',
        ('Action', 'Cadence'): '99/1',
        ('Token', 'Victory'): '1/9',
        ('Token', 'Payoff'): '1/8',
        ('Token', 'NexToken'): '1/7',
        ('Token', 'Time.'): '1/6',
        ('Token', 'Cadence'): '1/5',
        ('Rhythm.', 'Victory'): '1/99',
        ('Rhythm.', 'Payoff'): '5/95',
        ('Rhythm.', 'NexToken'): '10/90',
        ('Rhythm.', 'Time.'): '15/85',
        ('Rhythm.', 'Cadence'): '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 = []
    
    # Create mapping from original node names to numbered labels
    mapping = {}
    counter = 1
    for layer in layers.values():
        for node in layer:
            mapping[node] = f"{counter}. {node}"
            counter += 1
            
    # Add nodes with new numbered labels 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):
            new_node = mapping[node]
            G.add_node(new_node, layer=layer_name)
            pos[new_node] = position
            node_colors.append(colors.get(node, 'lightgray'))
    
    # Add edges with updated node labels
    for (source, target), weight in edges.items():
        if source in mapping and target in mapping:
            new_source = mapping[source]
            new_target = mapping[target]
            G.add_edge(new_source, new_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("OPRAH™", fontsize=25)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 25 Plato, Bacon, and Aristotle. Our dear philosophers map onto the neural architecture of thought, with Plato embodying the Default Mode Network (DMN)—the introspective, abstract generator of ideal forms—while Bacon, the relentless empiricist, activates the Task-Positive Network (TPN), grounding knowledge in experiment and observation. Aristotle, the great synthesizer, orchestrates the Salience Network (SN), dynamically toggling between the two, his pragmatism mirroring the thalamocortical gating that dictates shifts between passive reflection and active engagement. The salience node jostling—where competing stimuli wrest for cognitive primacy—reflects philosophy’s neural reality, where dialectical tensions are not mere abstractions but tangible, circuit-driven contests for attention, grounding even the loftiest of ideas in the anatomy of the mind.