Failure

The eternal recurrence of the same presents a cosmos untethered from moral imperatives or teleological conclusions, its infinite loops echoing the indifference of a universe unconcerned with human narratives. Nietzsche’s vision forces us to confront the unsettling prospect of repetition without purpose, an architecture where each iteration begins again without judgment, without meaning, without end. In such a framework, art, like the cosmos itself, emerges as a phenomenon immune to the constraints of moral purpose. Its beauty lies not in delivering conclusions but in existing as an expression of the infinite, a snapshot of a system in perpetual motion. To demand purpose from it—moral or otherwise—is to reduce it to a function, a cog in a machine designed to please rather than reflect the boundless complexity of life.

Fig. 22 The Next Time Your Horse is Behaving Well, Sell it. The numbers in private equity don’t add up because its very much like a betting in a horse race. Too many entrants and exits for anyone to have a reliable dataset with which to estimate odds for any horse-jokey vs. the others for quinella, trifecta, superfecta. The eternal recurrence of the same, as Nietzsche framed it, unfolds a cosmos devoid of beginning or end, an architecture where every output node circles back as input to an identical iteration, endlessly. In such a framework, the demand for moral purpose dissolves into absurdity. The cosmos, infinite in time and space, operates beyond binaries of good and evil, refusing to submit its vastness to human conceits of narrative closure. Yet Goethe’s defiance of moral demands, while celebrating the autonomy of the artist, rings hollow when confronted with the persistent itch for a conclusion, as Anthony Capella’s narrator discovers. To demand purpose from art, perhaps, is not to ruin it, but to humanize it, to tether infinite loops of meaning to a finite self yearning for understanding. In rejecting moral purpose, we may resist simplifying complexity, but we also risk severing art from its audience—an audience that, like Capella’s Victorian memoirist, craves resolution, even if only to satisfy itself. And so, while Nietzsche’s cosmos tumbles through its eternal recurrence, it must tolerate those who insist on finding lessons, a phenomenon less a contradiction than an inevitable consequence of human architecture mirroring the infinite. What have we learned? Perhaps only that the output of one iteration can be both conclusion and renewal, endlessly oscillating between the finite and the infinite.

And yet, the human mind resists this detachment. Anthony Capella’s narrator, reaching the end of his memoir, finds himself at odds with the austere purity of Goethe’s dictum. The Victorian in him, and perhaps in all of us, demands resolution, a lesson, a moral to tie the threads together. There is a hunger for the finite, for a sense of completion in a world that seems otherwise governed by entropy and endless flux. This tension, between art as infinite recurrence and art as moral deliverance, captures the essence of human contradiction. We are creatures bound to the finite yet haunted by the infinite, forever attempting to impose our small conclusions upon the vastness of existence.

Perhaps this is why art must occupy a space between these poles. While the cosmos spins on in its eternal recurrence, art allows us to pause, to momentarily make sense of the senseless, to find patterns where none objectively exist. It is not the moral purpose that ruins the artist’s work, but the insistence that art must only reflect the infinite, untainted by human need. To create, after all, is a human act, and humans are moral creatures—not because morality defines the cosmos, but because it defines us. Art, then, is not a repudiation of moral purpose but an arena where morality and meaning collide with chaos, forging something ephemeral yet profound.

What have we learned? Perhaps that the output of one iteration, be it art or life itself, is never the end but always the beginning of another. And yet, in each return, we find a chance to pause, to interpret, to impose a momentary meaning. That meaning may not satisfy the cosmos, but it satisfies the self—and that, despite Nietzsche, may be the only conclusion we truly need.

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

# Define the neural network fractal
def define_layers():
    return {
        'World': ['Cosmos-Entropy', 'Planet-Tempered', 'Life-Needs', 'Ecosystem-Costs', 'Generative-Means', 'Cartel-Ends', ], ## Cosmos, Planet
        'Perception': ['Perception-Ledger'], # Life
        'Agency': ['Open-Nomiddle', 'Closed-Trusted'], # Ecosystem (Beyond Principal-Agent-Other)
        'Generative': ['Ratio-Seya', 'Competition-Blockchain', 'Odds-Dons'], # Generative
        'Physical': ['Volatile-Distributed', 'Unknown-Players',  'Freedom-Crypto', 'Known-Transactions', 'Stable-Central'] # Physical
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Perception-Ledger'],
        'paleturquoise': ['Cartel-Ends', 'Closed-Trusted', 'Odds-Dons', 'Stable-Central'],
        'lightgreen': ['Generative-Means', 'Competition-Blockchain', 'Known-Transactions', 'Freedom-Crypto', 'Unknown-Players'],
        'lightsalmon': [
            'Life-Needs', 'Ecosystem-Costs', 'Open-Nomiddle', # Ecosystem = Red Queen = Prometheus = Sacrifice
            'Ratio-Seya', 'Volatile-Distributed'
        ],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# 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()
    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'))  # Default color fallback

    # Add edges (automated for consecutive layers)
    layer_names = list(layers.keys())
    for i in range(len(layer_names) - 1):
        source_layer, target_layer = layer_names[i], layer_names[i + 1]
        for source in layers[source_layer]:
            for target in layers[target_layer]:
                G.add_edge(source, target)

    # Draw the graph
    plt.figure(figsize=(12, 8))
    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"
    )
    plt.title("Crypto Inspired App", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 23 Vet Advised When to Sell Horse. Right after an outstanding performance. Because you want to control how its perceived, the narrative backstory, and how its future performance might be imputed. Otherwise, reality manifests with point and interval estimates. This is the subject matter of the opening dialogue in Miller’s Crossing.