Risk

Broligarchy

Fig. 15 Getting Something Created (masculine) vs. The Optics and Performative Empathy (feminine). After appearing as a hunky contestant on the first ever series on Love Island, Chris Williamson’s life’s purpose came into focus; to create content that changed people’s lives. Since appearing on the ITV reality show, Williamson has launched the ‘Modern Wisdom’ podcast and his own YouTube channel; boasting an impressive base of three million subscribers.

Making achievement great again

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

# Define the neural network structure
layers = {
    'Input': ['Resourcefulness', 'Resources'],
    'Hidden': [
        'Identity (Self, Family, Community, Tribe)',
        'Tokenization/Commodification', 
        'Adversary Networks (Biological)', 
    ],
    'Output': ['Joy', 'Freude', 'Kapital', 'Schaden', 'Ecosystem']
}

# Adjacency matrix defining the weight connections
weights = {
    'Input-Hidden': np.array([[0.8, 0.4, 0.1], [0.9, 0.7, 0.2]]),
    'Hidden-Output': np.array([
        [0.2, 0.8, 0.1, 0.05, 0.2],
        [0.1, 0.9, 0.05, 0.05, 0.1],
        [0.05, 0.6, 0.2, 0.1, 0.05]
    ])
}

# Visualizing the Neural Network
def visualize_nn(layers, weights):
    G = nx.DiGraph()
    pos = {}
    node_colors = []

    # Add input layer nodes
    for i, node in enumerate(layers['Input']):
        G.add_node(node, layer=0)
        pos[node] = (0, -i)
        node_colors.append('lightgray')

    # Add hidden layer nodes
    for i, node in enumerate(layers['Hidden']):
        G.add_node(node, layer=1)
        pos[node] = (1, -i)
        if node == 'Identity (Self, Family, Community, Tribe)':
            node_colors.append('paleturquoise')
        elif node == 'Tokenization/Commodification':
            node_colors.append('lightgreen')
        elif node == 'Adversary Networks (Biological)':
            node_colors.append('lightsalmon')

    # Add output layer nodes
    for i, node in enumerate(layers['Output']):
        G.add_node(node, layer=2)
        pos[node] = (2, -i)
        if node == 'Joy':
            node_colors.append('paleturquoise')
        elif node in ['Freude', 'Kapital', 'Schaden']:
            node_colors.append('lightgreen')
        elif node == 'Ecosystem':
            node_colors.append('lightsalmon')

    # Add edges based on weights
    for i, in_node in enumerate(layers['Input']):
        for j, hid_node in enumerate(layers['Hidden']):
            G.add_edge(in_node, hid_node, weight=weights['Input-Hidden'][i, j])

    for i, hid_node in enumerate(layers['Hidden']):
        for j, out_node in enumerate(layers['Output']):
            # Adjust thickness for specific edges
            if hid_node == "Identity (Self, Family, Community, Tribe)" and out_node == "Kapital":
                width = 6
            elif hid_node == "Tokenization/Commodification" and out_node == "Kapital":
                width = 6
            elif hid_node == "Adversary Networks (Biological)" and out_node == "Kapital":
                width = 6
            else:
                width = 1
            G.add_edge(hid_node, out_node, weight=weights['Hidden-Output'][i, j], width=width)

    # Draw the graph
    plt.figure(figsize=(12, 8))
    edge_labels = nx.get_edge_attributes(G, 'weight')
    widths = [G[u][v]['width'] if 'width' in G[u][v] else 1 for u, v in G.edges()]
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color='gray', 
        node_size=3000, font_size=10, width=widths
    )
    nx.draw_networkx_edge_labels(G, pos, edge_labels={k: f'{v:.2f}' for k, v in edge_labels.items()})
    plt.title("Greateful (Static) vs. Pushing (Dynamic)")
    plt.show()

visualize_nn(layers, weights)

Fig. 16 Balance between ambition & smelling the roses.