Woo’d 🗡️❤️💰

Mel Gibson’s idea that “art is simulation” aligns beautifully with the concept of neural networks, where exploration of a “labyrinth” (or combinatorial space) gives rise to the illusion of free will and choice. The protagonist navigating this space mirrors how neural networks test and refine connections, iteratively seeking optimized outcomes.

In your network, Art occupies the metaphysical layer, the yellow node that bridges Physics and Decisions, embodying this simulation concept. It allows exploration beyond raw inputs like Means and Ends (and means are the causal rules of biology… and society) in the Physics layer, moving through decisions like Good and Bad, ultimately shaping emergent outcomes such as Wisdom or Ithaca.

What stands out here is the role of Art as both a mediator and a creator of possibility—inviting the individual (or the neural system) to simulate paths, experience their emotional and logical consequences, and arrive at resolutions (like Happiness or Ithaca). Art becomes the stage on which Ids are explored and sublimated, offering a deliberate alternative to raw instinct, much like a protagonist delves into their aspirations through narrative or visual mediums.

The idea of art as a “vast combinatorial space” also ties into how it confronts and refines instincts. Your model captures this with connections that traverse from Ends to Art, and from Art to layers that demand choices, reflecting art’s power to transform raw experience into refined understanding.

In essence, this model isn’t just about mapping choices; it’s a profound structure for understanding how simulation (through art) crafts the paths we take and shapes the output of happiness, wisdom, or even tragedy.

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

def define_layers():
    return {
        'Physics': ['Cosmos', 'Earth', 'Life', 'Sacrifice', 'Means', 'Ends'],
        'Metaphysics': ['Art'],
        'Decisions': ['Bad', 'Good'],
        'Games': ['Id', 'Journey', 'Happiness'],
        'Outcomes': ['Troy', 'Imposthume', 'Priestly', 'Wisdom', 'Ithaca']
    }

def assign_colors(node, layer):
    if node == 'Art': return 'yellow'
    if layer == 'Physics' and node in ['Ends']: return 'paleturquoise'
    if layer == 'Physics' and node in ['Means']: return 'lightgreen'
    if layer == 'Decisions' and node == 'Good': return 'paleturquoise'
    if layer == 'Games':
        if node == 'Happiness': return 'paleturquoise'
        if node == 'Journey': return 'lightgreen'
        if node == 'Id': return 'lightsalmon'
    if layer == 'Outcomes':
        if node == 'Ithaca': return 'paleturquoise'
        if node in ['Wisdom', 'Priestly', 'Imposthume']: return 'lightgreen'
        if node == 'Troy': return 'lightsalmon'
    return 'lightsalmon'

def calculate_positions(layer, center_x, offset):
    layer_size = len(layer)
    start_y = -(layer_size - 1) / 2
    return [(center_x + offset, start_y + i) for i in range(layer_size)]

def visualize_nn():
    layers = define_layers()
    G = nx.DiGraph()
    pos = {}
    node_colors = []
    center_x = 0

    for i, (layer_name, nodes) in enumerate(layers.items()):
        y_positions = calculate_positions(nodes, center_x, offset=-len(layers) + i + 1)
        for node, position in zip(nodes, y_positions):
            G.add_node(node, layer=layer_name)
            pos[node] = position
            node_colors.append(assign_colors(node, layer_name))

    for layer_pair in [
        ('Physics', 'Metaphysics'), ('Metaphysics', 'Decisions'), ('Decisions', 'Games'), ('Games', 'Outcomes')
    ]:
        source_layer, target_layer = layer_pair
        for source in layers[source_layer]:
            for target in layers[target_layer]:
                if source == "Means" and target == "Instinct":
                    G.add_edge(target, source)
                else:
                    G.add_edge(source, target)

    G.add_edge('Ends', 'Art')

    plt.figure(figsize=(14, 10))
    for u, v in G.edges:
        style = "arc3,rad=0.1"
        color = 'gray'
        width = 1

        if (u, v) == ('Instinct', 'Means'):
            style = "arc3,rad=-0.1"
            color = 'lightgreen'
            width = 1
        elif (u, v) == ('Ends', 'Instinct'):
            style = "arc3,rad=0.2"
            color = 'paleturquoise'
            width = 2

        nx.draw_networkx_edges(
            G, pos, edgelist=[(u, v)], width=width,
            edge_color=color, connectionstyle=style,
            arrows=True, arrowstyle='-|>', arrowsize=25
        )
    nx.draw_networkx_nodes(G, pos, node_color=node_colors, node_size=2000)
    nx.draw_networkx_labels(G, pos, font_size=10)

    plt.axis('off')
    plt.show()

visualize_nn()

Fig. 7 Cambridge University Graduates. They transplanted their notions of the sound means of tradition, the justified tactics of change within stability, and firm and resolute commitment to share ideals of humanity. And setup the stage for an aburd scene in Athens – A Midsummer Nights Dream