Apollo & Dionysus

The ethos of true heroes stands in stark contrast to the ethos embodied by figures like Donald Trump or Jules Winnfield. In the former category, we find a discipline forged in hardship, a devotion to a collective ideal that transcends individual ambition. The warriors of Sparta, the disciplined ranks of Shaka Zulu, and the band of brothers rallying behind Henry V all operated under an ethic of sacrifice, honor, and duty. Their heroism was not measured by their capacity to dominate others in a transactional game of power but by their willingness to subsume themselves into a larger cause, knowing that their mortality was the price of their ideals.

By contrast, the ethos of Trump and Jules Winnfield is a spectacle of individual survival, manipulation, and self-interest. Jules, a hitman from Pulp Fiction, is a man entangled in his own contradictions—on the one hand, he is a ruthless executor of violence, and on the other, he fancies himself a seeker of personal transformation after a perceived divine intervention. His ‘redemption’ is introspective, detached from a greater calling. It is a personal project, disconnected from the collective ethos that defines true heroism. Trump, by comparison, is the distillation of unyielding self-preservation, a figure who bends reality to his own narrative. His survival and success are predicated on spectacle rather than sacrifice, on an ability to mold perception rather than to lead men into battle or endure hardship for a cause greater than himself.

Fig. 17 Dionysian ethos vs. Apollonian surface. Let’s call it for what it is: Jules’ is a performative art as is Trumps. But the warrior ethos is a horse of an entirely different color.

What differentiates the two categories most profoundly is the treatment of suffering and sacrifice. The Spartan warrior was conditioned to see pain as an inevitability of duty, not as something to be avoided but as proof of commitment to the polis. Henry V’s men, on the eve of Agincourt, understood their diminished numbers not as a disadvantage but as the foundation of greater glory—“We few, we happy few, we band of brothers.” This is a mindset of acceptance, a willingness to face death in service of a principle. The adversarial life is not chosen for its rewards but because it is the only way to remain true to a sense of higher purpose.

Jules and Trump, by contrast, embody a different kind of adversarial existence. They engage in a world where conflict is necessary, but not in service of a grand ideal—rather, it is a means of personal survival, of maintaining dominance over others. Jules’ transformation does not lead him to join a higher cause but merely to walk away from his previous existence. He opts out rather than stepping up. Trump, similarly, navigates power through chaos and disruption, but his battles are performative. They are fought in media cycles, in courtrooms, in the domain of rhetoric and branding. There is no true sacrifice, no confrontation with mortality in the way a warrior must inevitably face it.

The great compression of history reveals this duality in sharp relief. The heroes of antiquity did not become great because they sought greatness but because they pursued duty with such intensity that history was forced to acknowledge them. Sparta’s warriors were not individuals seeking to brand themselves as warriors; they were bred into an ethic where the survival of the city-state was paramount. The Zulu impis fought not to elevate their own names but because Shaka had created a military machine in which the individual was secondary to the whole. Henry V’s men fought for their king, their land, and their God, not for personal glory. The collective ethos always superseded personal ambition.

Jules and Trump represent the opposite impulse: they are products of a world where personal narrative overrides collective duty. Their transformations, whether real or imagined, are acts of self-mythology rather than acts of service. They belong to the era of spectacle, where power is no longer a function of sacrifice but of perception. True heroes fought, bled, and died for something beyond themselves. Those who play at heroism in the modern age wield influence, manipulate media, and survive, but they never truly serve.

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

# Define the neural network layers
def define_layers():
    return {
        'Suis': ['Fewer Men', 'Greater Honor', 'And If To Live', 'Country Loss', "If We Are Mark’d", 'To Die, We Enough'],  # Static
        'Voir': ['Information'],  
        'Choisis': ['Baseline', 'Decision'],  
        'Deviens': ['Adverse Event Markers', 'Comorbidity/ICD Code', 'Temporal Changes'],  
        "M'èléve": ['Mortality Rate', 'Organ Failure', 'Hospitalization', 'Dependency', 'Physical Frailty']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Information'],  
        'paleturquoise': ['To Die, We Enough', 'Decision', 'Temporal Changes', 'Physical Frailty'],  
        'lightgreen': ["If We Are Mark’d", 'Comorbidity/ICD Code', 'Organ Failure', 'Dependency', 'Hospitalization'],  
        'lightsalmon': ['And If To Live', 'Country Loss', 'Baseline', 'Adverse Event Markers', 'Mortality Rate'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights (hardcoded for editing)
def define_edges():
    return {
        ('Fewer Men', 'Information'): '1/99',
        ('Greater Honor', 'Information'): '5/95',
        ('And If To Live', 'Information'): '20/80',
        ('Country Loss', 'Information'): '51/49',
        ("If We Are Mark’d", 'Information'): '80/20',
        ('To Die, We Enough', 'Information'): '95/5',
        ('Information', 'Baseline'): '20/80',
        ('Information', 'Decision'): '80/20',
        ('Baseline', 'Adverse Event Markers'): '49/51',
        ('Baseline', 'Comorbidity/ICD Code'): '80/20',
        ('Baseline', 'Temporal Changes'): '95/5',
        ('Decision', 'Adverse Event Markers'): '5/95',
        ('Decision', 'Comorbidity/ICD Code'): '20/80',
        ('Decision', 'Temporal Changes'): '51/49',
        ('Adverse Event Markers', 'Mortality Rate'): '80/20',
        ('Adverse Event Markers', 'Organ Failure'): '85/15',
        ('Adverse Event Markers', 'Hospitalization'): '90/10',
        ('Adverse Event Markers', 'Dependency'): '95/5',
        ('Adverse Event Markers', 'Physical Frailty'): '99/1',
        ('Comorbidity/ICD Code', 'Mortality Rate'): '1/9',
        ('Comorbidity/ICD Code', 'Organ Failure'): '1/8',
        ('Comorbidity/ICD Code', 'Hospitalization'): '1/7',
        ('Comorbidity/ICD Code', 'Dependency'): '1/6',
        ('Comorbidity/ICD Code', 'Physical Frailty'): '1/5',
        ('Temporal Changes', 'Mortality Rate'): '1/99',
        ('Temporal Changes', 'Organ Failure'): '5/95',
        ('Temporal Changes', 'Hospitalization'): '10/90',
        ('Temporal Changes', 'Dependency'): '15/85',
        ('Temporal Changes', 'Physical Frailty'): '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=15, connectionstyle="arc3,rad=0.2"
    )
    nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
    plt.title("Indicator, Influencer, Impacted", fontsize=23)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 18 Change of Guards. In Grand Illusion, Renoir was dealing the final blow to the Ancién Régime. And in Rules of the Game, he was hinting at another change of guards, from agentic mankind to one in a mutualistic bind with machines (unsupervised pianos & supervised airplanes). How priscient!