Tactical

This book is a symphony of abstraction, where the themes of life, governance, perception, and existence are woven into a conceptual neural network. It represents not just a model but a philosophy—a map of thought that condenses diverse disciplines into a layered structure, each with its own color-coded significance and symbolic resonance.

At its core, this work posits that human understanding and agency are structured in layers, much like a neural network. Each layer reflects a progression from the cosmic to the tangible, from the divine to the societal. Through this structure, we aim to articulate a coherent vision of how ideas interconnect, evolve, and manifest in the physical world.

The Architecture of Thought

1. World Layer: This foundational layer captures the vast themes of cosmos, earth, life, and cost. It explores existential realities—how humanity relates to its divine origins, the biological cycles of the Red Queen Hypothesis, and the relentless machine of time and parallel processes. These nodes form the bedrock upon which higher abstractions rest.

2. Perception Layer: At the heart of this layer is the solitary node of Perspectivism. It represents a compression of all viewpoints into a singular lens—a unifying vantage point through which the chaos of the world begins to make sense. Perspectivism is the linchpin, the bridge between raw experience and higher meaning.

3. Agency Layer: Here, the duality of Surprise and Optimism embodies humanity’s navigation through life’s uncertainties. Surprise captures the unpredictability of existence, while Optimism charts a forward momentum, underscoring the resilience and creativity of the human spirit.

4. Generativity Layer: This layer encodes pathways of order and innovation through the nodes of Anarchy, Oligarchy, and Monarchy. It reflects the dynamics of creation, governance, and the tension between chaos and structure. These systems are not merely political; they are metaphors for generative processes in thought, society, and beyond.

5. Physicality Layer: Grounding the abstract, this layer connects to societal structures and movements through Dynamic, Static, Partisan, Non-Partisan, and Common Wealth nodes. These elements contextualize the system within tangible realities, offering a framework for interpreting action and stability in the real world.

The Symbolism of Color

Color coding infuses the structure with emotional and thematic depth:

• Paleturquoise: Represents stability, hope, and forward-looking nodes, such as Time, Optimism, Monarchy, and Static.

• Lightgreen: Encodes cooperation, iteration, and groundedness, as seen in Parallel, Non-Partisan, and Common Wealth.

• Lightsalmon: Reflects transformation and adversarial dynamics, embodied by Dynamic and Anarchy.

This vibrant palette highlights the interplay of harmony and tension, encapsulating the essence of human and systemic evolution.

The Visualization of Ideas

The visualization of this network is both artistic and analytical. By arranging nodes within layers and connecting them through relationships, the graph becomes a living document of thought. Each edge signifies an interaction—a flow of ideas from the cosmic to the societal, from perception to action.

Through this framework, we invite readers to explore the intersections of philosophy, science, and humanity. It is a journey into the architecture of understanding, where every node and edge speaks to the complexities of existence and the elegance of order.

Our Philosophy

This work champions the compression of time (wall-e), the power of parallel processing (art of fugue), and the sacrifices that underpin progress (there’ll be blood). From the ancestral rituals of blood and toil to the modern trade-offs of energy and innovation (gray nodes), it examines how humanity’s relentless pursuit of meaning ripples across ecosystems, societies, and the cosmos itself.

Yet, it is not a deterministic vision. The inclusion of Perspectivism and Optimism reflects a belief in agency—that amidst the constraints of nature and history, there is room for creativity, surprise, and the reimagining of pathways.

As you embark on this exploration, we hope you find resonance in the layers of this neural network. Whether as a lens for interpreting the world or a framework for generating new ideas, may it serve as both a map and a mirror, reflecting the intricate dance of order and chaos, and the boundless potential of human thought.

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
def define_layers():
    return {
        'World': ['Cosmos', 'Earth', 'Life', 'Cost', 'Parallel', 'Time'], # Divine: Cosmos-Earth; Red Queen: Life-Cost; Machine: Parallel-Time
        'Perception': ['Perspectivism'],
        'Agency': ['Surprise', 'Optimism'],
        'Generativity': ['Anarchy', 'Oligarchy', 'Monarchy'],
        'Physicality': ['Dynamic', 'Partisan', 'Common Wealth', 'Non-Partisan', 'Static']
    }

# Assign colors to nodes
def assign_colors(node, layer):
    if node == 'Perspectivism':
        return 'yellow'
    if layer == 'World' and node in [ 'Time']:
        return 'paleturquoise'
    if layer == 'World' and node in [ 'Parallel']:
        return 'lightgreen'
    if layer == 'World' and node in [ 'Cosmos', 'Earth']:
        return 'lightgray'
    elif layer == 'Agency' and node == 'Optimism':
        return 'paleturquoise'
    elif layer == 'Generativity':
        if node == 'Monarchy':
            return 'paleturquoise'
        elif node == 'Oligarchy':
            return 'lightgreen'
        elif node == 'Anarchy':
            return 'lightsalmon'
    elif layer == 'Physicality':
        if node == 'Static':
            return 'paleturquoise'
        elif node in ['Non-Partisan', 'Common Wealth', 'Partisan']:
            return 'lightgreen'
        elif node == 'Dynamic':
            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("Snails Pace vs. Compressed Time", fontsize=15)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 1 Can We Agree On The Objective Function to Optimize? The Physical (Output) Layer of this neural network shows somewhat mutually exclusive nodes for optimization. We’ve elevated the principal-agent theory to handle at least five categories rather than a simplistic binary. This allows us to digest capitalism, marxism, feminism, racism, colonialism, tribalism, terrorism, religion, atheism, mental illness, music, art, philosophy, science, movements, mythology, and algorithms