Failure

The Coen brothers’ films, with their relentless ambiguity and refusal to resolve into satisfying conclusions, resemble a regression model that fails to converge. Their narratives are built on open functions, systems that defy closure, leaving the audience with a sense of perpetual uncertainty. In contrast, the Victorian literary model operates like a closed function—a system designed to converge after a set number of iterations, offering resolution and harmony that align with the moral and social frameworks of its time. This fundamental difference between the Coens and the Victorians reflects not only their storytelling styles but their underlying philosophies about life, meaning, and the role of narrative in shaping human experience.

In a Victorian novel, the closed function is evident in the progression of the narrative arc. The iterations—the trials and tribulations of the protagonist—are calculated steps toward a known endpoint. Dickens, Eliot, and the Brontës all follow this formula, ensuring that the system resolves into a stable state by the final chapter. The narrative regression may begin with chaos, like Pip’s uncertain parentage or Jane Eyre’s difficult childhood, but each iteration reweights the system toward moral clarity, social stability, and individual growth. The closed function is deterministic: it ensures that the story converges on a resolution that aligns with the Victorian ethos of order, duty, and progress. It reflects a belief in the capacity of human agency and social structures to bring about a harmonious conclusion.

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.

The Coen brothers, by contrast, embrace an open function that resists convergence. Their films are built on systems of randomness and chaos, where each iteration introduces new volatility rather than reducing it. Consider No Country for Old Men, where the narrative cycles through violence, fate, and moral ambiguity without ever resolving into a stable state. The regression model they employ remains open, unbounded by the constraints of traditional storytelling. There is no clear endpoint, no assurance that the system will settle into equilibrium. Instead, the audience is left grappling with a model that continues to iterate indefinitely, offering no conclusions and exposing the instability inherent in the world they portray.

This open function is not a flaw but a deliberate choice, one that reflects the Coens’ worldview. Life, as they see it, is not a closed system with a predetermined resolution but an open, chaotic process where meaning is elusive and outcomes are often arbitrary. In The Big Lebowski, this philosophy is distilled into the character of The Dude, whose lack of ambition and passive approach to life mirror the film’s refusal to converge on a traditional narrative arc. The Dude drifts through a series of iterations—kidnappings, mistaken identities, and bowling tournaments—without ever arriving at a definitive resolution. The Coens’ regression model leaves the function open, prioritizing the process over the endpoint.

The Victorian model, in contrast, reflects a time and culture that valued convergence. The closed function of their narratives mirrors the societal belief in progress, morality, and the capacity for human growth. Even when Victorian novels acknowledge the imperfections of their characters or the flaws of society, they ultimately resolve into a state of harmony. This convergence is not just a narrative choice but a reflection of a broader cultural confidence in the possibility of resolution. Victorian literature operates as a system that always finds its way back to stability, even if the iterations are painful and protracted.

The divergence between these two models—the open function of the Coens and the closed function of the Victorians—reveals much about their respective views of human existence. The Victorian regression model assumes that life, like a well-crafted narrative, can be optimized for resolution. Each iteration brings the system closer to its goal, whether that is moral redemption, social integration, or personal fulfillment. The Coens’ open function, however, rejects this notion, presenting life as a system that may never converge, one in which each iteration introduces new complexities and uncertainties rather than eliminating them.

Ultimately, the Victorian model reassures, while the Coen model unsettles. The closed function offers the comfort of resolution, the assurance that chaos can be tamed and meaning restored. The open function, by contrast, forces us to confront the instability and unpredictability of existence, leaving us with a system that iterates endlessly without ever finding a stable state. These contrasting approaches—one deterministic, the other probabilistic—speak to the broader evolution of storytelling and the human desire to make sense of a world that often resists convergence. Whether through the harmonious closure of Victorian novels or the perpetual uncertainty of Coen brothers’ films, these models invite us to reflect on the limits of narrative and the nature of meaning itself.

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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.