Transvaluation

Niccolò Machiavelli’s The Prince is a treatise on the mechanics of power—how it is acquired, stabilized, and lost. Within the framework of this neural network, Machiavelli’s arguments map onto a layered system of inputs (Suis), compression (Voir, Choisis), and emergent decision pathways (Deviens and M’élève). Power, in this model, is not a static entity but an iterative function shaped by the adversarial, transactional, and cooperative forces that determine a prince’s rise and fall.

Fig. 21 What Exactly is Identity. A node may represent goats (in general) and another sheep (in general). But the identity of any specific animal (not its species) is a network. For this reason we might have a “black sheep”, distinct in certain ways – perhaps more like a goat than other sheep. But that’s all dull stuff. Mistaken identity is often the fuel of comedy, usually when the adversarial is mistaken for the cooperative or even the transactional.

At the foundation, Suis represents the primordial inputs—Cosmos, Planet, Alive, Loss, Trial, and Error. These correspond to the raw materials that shape governance: the physical world, biological imperatives, existential threats, and the iterative nature of adaptation. Machiavelli’s opening chapters align with this base layer. Chapter I, which classifies different types of principalities, mirrors the tension between Cosmos (an overarching system) and Loss (the ever-present risk of failure). Just as a neural network begins with a structured but uncertain input space, a prince’s origin is defined by the constraints of geography, historical circumstance, and human nature. Chapter II, concerning hereditary principalities, is rooted in Planet—the stability of dynastic rule, where continuity outweighs disruption. In contrast, Chapter III, on mixed principalities, introduces Trial and Error—where a ruler must navigate the turbulence of new acquisitions through experimentation, learning which methods consolidate power and which invite rebellion.

The next layer, Voir, consists of a singular node: Information. This is the prince’s processing unit, where all historical lessons and contemporary observations are weighed. It is the repository of both theoretical and practical knowledge, akin to Machiavelli’s own project of codifying political wisdom into a single accessible doctrine. The weight distribution within this node varies—some principalities rely heavily on precedent (hereditary states), while others demand constant reassessment (new conquests). Chapters IV and V embody this tension. The stability of the Persian Empire after Alexander’s conquest (Chapter IV) is a case of high information retention—once the structure of governance was absorbed, it persisted with minimal resistance. Conversely, Chapter V, which addresses the challenge of governing previously free cities, represents an instance where Loss dominates Information—a failure to account for the entrenched political culture of a region invites rebellion.

How flatterers should be avoided
– Chapter XXIII

Once processed, information moves into Choisis, the decision layer, which contains Baseline and Decision. These nodes determine the prince’s strategic approach. Baseline represents the default assumptions—tradition, historical precedent, and conventional wisdom—while Decision represents the moments of conscious deviation from these norms. Chapters VI and VII illustrate this divide. A prince who acquires power through his own arms and abilities (Chapter VI) is operating from a refined Decision node, actively shaping events rather than passively inheriting them. By contrast, those who gain power through fortune or the efforts of others (Chapter VII) are leaning on a weak Decision weight, relying instead on the stochastic distributions of Baseline.

From here, the pathways diverge into Deviens, which houses the three equilibria of power: Adversarial, Transactional, and Cooperative. These represent different modes of statecraft. Chapter VIII, concerning those who gain power through wickedness, sits firmly within Adversarial—a raw application of force that maximizes immediate gains but risks long-term instability. Chapter IX, on civil principalities, belongs to Transactional, where power is brokered through negotiation and shifting alliances. Meanwhile, Chapter X, on measuring a principality’s strength, bridges Adversarial and Cooperative, as rulers must determine whether security is best achieved through fear (hard power) or civic trust (soft power). Ecclesiastical states (Chapter XI) exist in a realm where Cooperative is artificially sustained by ideology, reducing the role of transactional and adversarial mechanisms.

Chapters XII–XIV shift focus to military power, reinforcing the weights between Adversarial and Transactional. Machiavelli’s distaste for mercenaries (Chapter XII) and auxiliaries (Chapter XIII) stems from their unreliability—systems that outsource security are fragile, as they depend on forces whose interests are not aligned with the ruler’s. This is a case of misallocated Transactional weight; the prince is paying for control but not receiving loyalty. The ideal military force, according to Chapter XIV, is one that internalizes war as a necessity—an Adversarial stance permanently embedded in governance.

Chapters XV–XIX focus on reputation management, oscillating between Adversarial, Transactional, and Cooperative strategies. Chapter XVII’s famous question—whether it is better to be feared than loved—maps onto the weight distribution between Adversarial and Cooperative. Machiavelli’s answer, that fear is more reliable, suggests a bias toward adversarial strategies, though he acknowledges that extreme cruelty destabilizes power. Chapter XVIII, on keeping faith, reinforces this; the prince must appear virtuous while remaining flexible in practice, adapting Transactional principles as needed.

Finally, the highest layer, M’élève, encapsulates the different forms of ascendancy: New, Earned, Mixed, Gifted, and Heredity. These classifications correspond directly to Machiavelli’s typology of rulership. The self-made prince (Chapter VI) represents Earned, where power is built through effort and merit. The fortunate ruler (Chapter VII) corresponds to Gifted, where power is conferred by external forces. Mixed principalities (Chapter III) align with Mixed, where a ruler must synthesize multiple strategies. Hereditary rulers (Chapter II) naturally fall under Heredity, where the weight of tradition dictates governance. The adversarial pathway, where power is seized violently (Chapter VIII), maps to New, where innovation in power structures often emerges from destruction.

The concluding chapters (XX–XXVI) reinforce the network’s dynamics. The collapse of Italy’s rulers (Chapter XXIV) illustrates a systemic failure to adjust weights appropriately—an overreliance on Transactional and Cooperative strategies in a period demanding Adversarial recalibration. Chapter XXV, on fortune, directly ties into the probabilistic nature of decision-making: rulers who anticipate uncertainty and adjust dynamically (high Decision weight) outperform those who assume stability. Finally, Chapter XXVI, Machiavelli’s exhortation to liberate Italy, aligns with the prince’s highest possible function—ascending beyond personal rule to shape history itself.

Machiavelli’s The Prince is thus not just a guidebook for rulers but a neural algorithm for power—an interplay of structured inputs, decision-based compression, and weighted outputs that determine the survival of a principality. The prince who masters this system does not merely rule; he becomes a self-perpetuating force, adapting to historical contingencies with the fluidity of a living neural network.

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 layers
def define_layers():
    return {
        'Suis': ['Cosmos', 'Planet', 'Alive', 'Loss', "Trial", 'Error'],  # Static
        'Voir': ['Information'],  
        'Choisis': ['Baseline', 'Decision'],  
        'Deviens': ['Adversarial', 'Transactional', 'Cooperative'],  
        "M'èléve": ['New', 'Earned', 'Mixed', 'Gifted', 'Heredity']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Information'],  
        'paleturquoise': ['Error', 'Decision', 'Cooperative', 'Heredity'],  
        'lightgreen': ["Trial", 'Transactional', 'Earned', 'Gifted', 'Mixed'],  
        'lightsalmon': ['Alive', 'Loss', 'Baseline', 'Adversarial', 'New'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights (hardcoded for editing)
def define_edges():
    return {
        ('Cosmos', 'Information'): '1/99',
        ('Planet', 'Information'): '5/95',
        ('Alive', 'Information'): '20/80',
        ('Loss', 'Information'): '51/49',
        ("Trial", 'Information'): '80/20',
        ('Error', 'Information'): '95/5',
        ('Information', 'Baseline'): '20/80',
        ('Information', 'Decision'): '80/20',
        ('Baseline', 'Adversarial'): '49/51',
        ('Baseline', 'Transactional'): '80/20',
        ('Baseline', 'Cooperative'): '95/5',
        ('Decision', 'Adversarial'): '5/95',
        ('Decision', 'Transactional'): '20/80',
        ('Decision', 'Cooperative'): '51/49',
        ('Adversarial', 'New'): '80/20',
        ('Adversarial', 'Earned'): '85/15',
        ('Adversarial', 'Mixed'): '90/10',
        ('Adversarial', 'Gifted'): '95/5',
        ('Adversarial', 'Heredity'): '99/1',
        ('Transactional', 'New'): '1/9',
        ('Transactional', 'Earned'): '1/8',
        ('Transactional', 'Mixed'): '1/7',
        ('Transactional', 'Gifted'): '1/6',
        ('Transactional', 'Heredity'): '1/5',
        ('Cooperative', 'New'): '1/99',
        ('Cooperative', 'Earned'): '5/95',
        ('Cooperative', 'Mixed'): '10/90',
        ('Cooperative', 'Gifted'): '15/85',
        ('Cooperative', 'Heredity'): '20/80'
    }

# 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()
    edges = define_edges()
    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 with weights
    for (source, target), weight in edges.items():
        if source in G.nodes and target in G.nodes:
            G.add_edge(source, target, weight=weight)
    
    # Draw the graph
    plt.figure(figsize=(12, 8))
    edges_labels = {(u, v): d["weight"] for u, v, d in G.edges(data=True)}
    
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color='gray',
        node_size=3000, font_size=12, connectionstyle="arc3,rad=0.2"
    )
    nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
    plt.title("The Prince", fontsize=23)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 22 Nvidia vs. Music. APIs between Nvidias CUDA & their clients (yellowstone node: G1 & G2) are here replaced by the ear-drum & vestibular apparatus. The chief enterprise in music is listening and responding (N1, N2, N3) as well as coordination and syncronization with others too (N4 & N5). Whether its classical or improvisational and participatory, a massive and infinite combinatorial landscape is available for composer, maestro, performer, audience. And who are we to say what exactly music optimizes?