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Ah, the symbiosis of birds perched on a bison! It’s a scene straight out of nature’s most intricate choreography, where utility meets quiet elegance. The birds—often oxpeckers—aren’t just freeloaders enjoying a ride; they’re opportunists with a knack for cleaning parasites off their massive hosts. This relationship, however, isn’t as purely harmonious as it appears.

Fig. 4 Strategic Ambiguity. The Red Queen presents rather fascinating scenarios to us.

On one hand, it’s a mutualistic dynamic: the bison gets some relief from ticks and other pests, while the birds enjoy a reliable source of food. On the other hand, oxpeckers have been known to widen wounds to keep the blood flowing, feeding off it like opportunistic vampires disguised as benefactors. It’s a microcosm of how nature thrives in ambiguous spaces, where the lines between help and harm blur.

What I find captivating about these images is the juxtaposition: a hulking, lumbering bison carrying tiny, fragile creatures. It’s a metaphor for coexistence—power hosting fragility, with the fragile wielding unexpected influence. It reflects something primal about our own networks, both biological and societal, where symbiotic relationships often disguise their transactional undercurrents.

In short, it’s a scene of deceptive peace layered with the quiet chaos of survival. Beautiful, yet utterly pragmatic.

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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', 'Sacrifice', 'Means', 'Ends', ],
        'Perception': ['Perception'],
        'Agency': ['Instinct', 'Deliberation'],
        'Generativity': ['Parasitism', 'Mutualism', 'Commensalism'],
        'Physicality': ['Offense', 'Lethality',  'Retreat', 'Immunity', 'Defense']
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Perception'],
        'paleturquoise': ['Ends', 'Deliberation', 'Commensalism', 'Defense'],
        'lightgreen': ['Means', 'Mutualism', 'Immunity', 'Retreat', 'Lethality'],
        'lightsalmon': [
            'Life', 'Sacrifice', 'Instinct',
            'Parasitism', '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("Symbiosis", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 5 How now, how now? What say the citizens? Now, by the holy mother of our Lord, The citizens are mum, say not a word.