Heir, y 95/5

Hamlet is a massive combinatorial search space—an open-ended, recursive exploration of possibilities, where every choice spawns more choices, and every interpretation fractures into further ambiguities. The play is structured like a vast search algorithm, attempting to navigate an infinite game tree of moral, political, and existential decisions. Hamlet himself embodies this process, constantly revising his understanding of reality—recomputing whether to act, whether to trust the ghost, whether Claudius is truly guilty, whether Ophelia is sincere, whether life itself is worth enduring. He iterates through soliloquies like a machine learning model updating its weights with each new input, but never fully converging. His mind is an adversarial loop, generating and rejecting strategies in a combinatorial explosion of potentialities. This is why Hamlet remains inexhaustible—it is a play designed to resist finality, to be an endless process of inquiry.

Macbeth, by contrast, is the cadence—the driving rhythm that hurtles toward inevitable resolution. Where Hamlet is an iterative search without clear stopping criteria, Macbeth is an optimization problem with a preloaded terminal state: damnation. The moment Macbeth steps onto his path, the weight updates are over; he has chosen the function, and now it simply executes. The witches provide an algorithmic constraint—prophecy as destiny—so that each step follows with metronomic precision. The language reflects this shift from search to cadence: Hamlet is filled with hesitations, recursive loops, and interrogative syntax; Macbeth is percussive, brutal, declarative—“If it were done when ’tis done, then ’twere well / It were done quickly.” No combinatorial expansion here—just a tightening spiral, an inexorable collapse.

Fig. 1 Establish Your Credentials. Only if you wish for the audience to switch-off their critical faculties, which begs the question.

Now, gods, stand up for bastards!
– Edmund

And then there’s King Lear, which completes the structure. If Hamlet is search space and Macbeth is cadence, King Lear is entropy. It is the dissolution of the search, the breaking apart of all structured motion, the scattering of meaning into chaos. Lear begins in the illusion of control, assigning his kingdom as if partitioning a dataset, assuming an orderly relationship between word and world. But the map disintegrates, language collapses (“never, never, never, never, never”), and even cadence is lost. The storm sequences aren’t a march toward doom, as in Macbeth—they are dissolution itself, a collapse of all coherent structure, where even the identity function fails. By the end, no restoration is possible; there is no resolution, no satisfying terminal state—only the irreversible fragmentation of everything.

So:

• Hamlet is the search space, a massive combinatorial expansion of possibilities.

• Macbeth is the cadence, the inevitability of execution once parameters are set.

• King Lear is entropy, the final unraveling when all structure breaks down.

Each play, then, isn’t just a psychological or thematic study—it represents a fundamental mode of processing reality. Shakespeare, in this triad, doesn’t just write about kings and their fates; he encodes different models of decision, action, and collapse, as if sketching out a cognitive architecture that humanity runs on.

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

# Define the neural network fractal
def define_layers():
    return {
        'World': ['Electro', 'Magnetic', 'Pulse', 'Cost', 'Trial', 'Error', ], # Veni; 95/5
        'Mode': ['Reflexive'], # Vidi; 80/20
        'Agent': ['Ascending', 'Descending'], # Vici; Veni; 51/49
        'Space': ['Sympathetic', 'Empathetic', 'Parasympathetic'], # Vidi; 20/80
        'Time': ['Hardcoded', 'Posteriori',  'Meaning', 'Likelihood', 'A Priori'] # Vici; 5/95
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Reflexive'],  
        'paleturquoise': ['Error', 'Descending', 'Parasympathetic', 'A Priori'],  
        'lightgreen': ['Trial', 'Empathetic', 'Likelihood', 'Meaning', 'Posteriori'],  
        'lightsalmon': [
            'Pulse', 'Cost', 'Ascending',  
            'Sympathetic', 'Hardcoded'
        ],
    }
    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'))   

    # 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("Intelligence", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 2 If reflexive node corresponds to Lear, and the algorithmic & surgical precision of top-down vs. bottom-up dichotomy corresponds to Macbeth, then the massive combinatorial search space falls to Hamlet.