Tactical

In a world increasingly dominated by data-driven efficiency, Kingsley Coman’s lament about the joy being drained from football mirrors the emergence of the green node in modern networks: the transactional node. This node, “Convenienza,” lies at the heart of a paradigm shift in sports, society, and human agency, emphasizing utility and outcomes over spontaneity and passion. The green node exemplifies the iterative strategy: a ceaseless optimization that sacrifices playfulness for results, replacing serendipity with sterile predictability.

Kids used to play in the streets, in parks, with no coaches… freely. These new régime of players all join academies from young, any dribbling/flair ability is coached out of them.
– Prov 1:20

Football, as Coman suggests, is no longer a dance but a spreadsheet. Coaches obsess over xG (expected goals), players are instructed to press relentlessly in choreographed patterns, and fans are inundated with metrics that dissect every microsecond of action. This transactional ethos, embodied by the green node, forces players and spectators alike into a rigid framework where the beauty of unpredictability is diminished. The artistry of a Zidane pirouette or Ronaldinho’s samba-like dribbles is supplanted by the econometrics of heat maps and sprint distances. The green node calculates endlessly, but it does not dream.

Fig. 1 Kingsley Coman: Why Efficiency is Taking the Joy Out of the Game. Whatever happend to imagination, joy, frenzy? The full range of our monoamines have gotta be excited not just dopamine! A recent computational investigation of genetic origins shows that the earliest development of monoamines occurred 650 million years ago and that the appearance of these chemicals, necessary for active or participatory awareness and engagement with the environment, coincides with the emergence of bilaterian or “mirror” body in the midst of (or perhaps in some sense catalytic of?) the Cambrian Explosion. It would appear that not only do these molecules play a role in bilaterian phenomena in the physical, but also in the metaphysical. The shadow self emerges particularly with indoleamines. Source: Wikipedia

The neural network’s architecture provides a lens through which to critique this phenomenon. In Coman’s critique, the joyous nodes—the “Sympatico” (connections), “Ostilità” (rivalry), and even the “Shadow” (unexpected impulses)—are subdued by the cold logic of “Convenienza.” Transactionalism asserts itself as a middle layer, bridging physicality and generativity but stifling the upward trajectory toward creativity and transcendence. The green node may stabilize and iterate, but it cannot soar.

This is not to deny the utility of the green node; efficiency has its place. Yet its unchecked dominance results in a network that stagnates. Football becomes not a living cosmos but a parallel system of cost-benefit calculations. Here, the joy of life—what Nietzsche might call the Dionysian spirit—is subsumed by the Apollonian desire for order. The shadow of this transactional approach stretches far beyond football, reflecting societal trends where algorithms dictate human behavior, from personalized ads to corporate decision-making. We live in a world of green nodes, where “Convenienza” has metastasized into every aspect of existence.

See also

Anchor ⚓️ -

• Histamine (adversarial)

• Acetylcholine (iterative)

• Adenosine (cooperative)

  • Serotonin (cooperative)

  • Dopamine (iterative)

  • Noradernaline (adversarial)

Yet there is hope in rebellion. Coman’s dissatisfaction hints at the network’s inherent tension, a call for reweighting the layers. The neural network’s combinatorial space, its hidden layer of possibilities, must be reactivated. Football, and society at large, must rediscover its shadow, its capacity for unpredictability and joy. Let the green node serve as a supporting actor, not the star. Only then can the game—and life—regain its magic.

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 structure
def define_layers():
    return {
        'World': ['Cosmos', 'Earth', 'Life', 'Cost', 'Parallel', 'Time', ],
        'Perception': ['Monoamines'],
        'Agency': ['Shadow', 'Bilateria'],
        'Generativity': ['Ostilità', 'Convenienza', 'Sympatico'],
        'Physicality': ['Offense', 'Lethality',  'Retreat', 'Immunity', 'Defense']
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Monoamines'],
        'paleturquoise': ['Time', 'Bilateria', 'Sympatico', 'Defense'],
        'lightgreen': ['Parallel', 'Convenienza', 'Immunity', 'Retreat', 'Lethality'],
        'lightsalmon': [
            'Cost', 'Life', 'Shadow',
            'Ostilità', 'Offense'
        ],
    }
    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("Alitalia", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 2 Kynurenine. Now that’s a molecule that asks you to look into the abyss. Looks like Plato had his cave, the Wachowski’s had their matrix, and our Feline has the abyss. Ain’t that somethin’? Meanwhile, the teenage Kevin only has time for life’s little games – not yet as deep, contemplative as a grownup Feline!