Tactical

Let’s march toward a generative framework, as we emerge phoenix-like from the ashes of perspectivism in the principal-agent-other theory (hereby rechristened monarchy, oligarchy, anarchy). The world is teeming with enterprise data to show the depth of the matter. But we also have simulations, fantasy-games, machine-learning and art to allow us to search the massive combinatorial spaces of our existences for a sure-footed way to go about maximizing a “common goal”, if at all that isn’t fantasy.

To address real-world agency problems, we must move beyond the closed systems of games and embrace a networked perspective. This means acknowledging the multiplicity of perspectives, the fluidity of goals, and the interdependence of agents within a generative, evolving landscape. Unlike chess, where every move is calculable, or roulette, where outcomes are random, real-world systems require adaptive strategies that balance optimization with creativity.

In this light, games serve not as blueprints for solving agency problems but as metaphors that reveal their limitations. The challenge lies not in simulating a perfect system but in navigating the imperfect, fragmented realities of human and ecological networks. Success, in this context, is less about achieving a singular metric and more about fostering resilience, innovation, and generativity within the vast combinatorial search space of the real world.

Having rechristened these ideas as monarchy, oligarchy, anarchy (ends, means, cost - 🩸 typically human sacrifice ), then capitalist, marxist, feminist, nostalgic, MAGA, nationalist, ancién regime, and perhaps any movement in modern history can seamlessly fit the narrative – because it offers a perspective it wishes to fight for.

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; modified to align with "Aprés Moi, Le Déluge" (i.e. Je suis AlexNet)
def define_layers():
    return {
        'World': ['Birds, Song & Life', 'Materials on Earth', 'Particles & Cosmos', 'Harmonic Series', 'Equal Temperament', 'Cadences & Patterns'],
        'Perception': ['Surveillance, Imagination'],
        'Agency': ['Surprise & Resolution', 'Genre & Expectation'],
        'Generativity': ['Individual Melody', 'Rhythm & Pocket', 'Chords, One Accord'],
        'Physicality': ['Solo Performance', 'Theme & Variation', 'Maestro or Conductor', 'Call & Answer', 'Military Cadence']
    }

# Assign colors to nodes
def assign_colors(node, layer):
    if node == 'Surveillance, Imagination':
        return 'yellow'
    if layer == 'World' and node in [ 'Cadences & Patterns']:
        return 'paleturquoise'
    if layer == 'World' and node in [ 'Equal Temperament']:
        return 'lightgreen'
    elif layer == 'Agency' and node == 'Genre & Expectation':
        return 'paleturquoise'
    elif layer == 'Generativity':
        if node == 'Chords, One Accord':
            return 'paleturquoise'
        elif node == 'Rhythm & Pocket':
            return 'lightgreen'
        elif node == 'Individual Melody':
            return 'lightsalmon'
    elif layer == 'Physicality':
        if node == 'Military Cadence':
            return 'paleturquoise'
        elif node in ['Call & Answer', 'Maestro or Conductor', 'Theme & Variation']:
            return 'lightgreen'
        elif node == 'Solo Performance':
            return 'lightsalmon'
    return 'lightsalmon'  # Default color

# Calculate positions for nodes
def calculate_positions(layer, center_x, offset):
    layer_size = len(layer)
    start_y = -(layer_size - 1) / 2  # Center the layer vertically
    return [(center_x + offset, start_y + i) for i in range(layer_size)]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    G = nx.DiGraph()
    pos = {}
    node_colors = []
    center_x = 0  # Align nodes horizontally

    # Add nodes and assign positions
    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))

    # Add edges (without weights)
    for layer_pair in [
        ('World', 'Perception'), ('Perception', 'Agency'), ('Agency', 'Generativity'), ('Generativity', 'Physicality')
    ]:
        source_layer, target_layer = layer_pair
        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=10, connectionstyle="arc3,rad=0.1"
    )
    plt.title("Perspectivisim: Archimedes vs. Nietzsche", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 1 Can We Agree On The Objective Function to Optimize? When we think of one, digitization could ostensibly fit the bill to resolve such conflicts by enforcing a standard—perhaps through a metronome or AI-driven interpretative model—but this would flatten the very human tensions that define classical music. Process quality in this context is not merely about error minimization; it is about fostering an environment where divergent visions can coexist and challenge each other, yielding generative outcomes.