Freedom in Fetters#
A shepherd stands as both guardian and guide, maintaining the delicate equilibrium of the ecosystem in which his flock exists. In the broadest sense, the shepherd represents the force that sustains life, ensuring harmony between nature and those who depend upon it. The shepherd is, in many ways, akin to the sun—an unceasing presence that offers light, warmth, and direction. Without the shepherd’s vigilance, the ecosystem falls into disorder, much as a world without the sun descends into darkness. In the Isobantu framework, this balance is not merely about survival but about the deep interconnection between being and the environment. The sheep that follow the shepherd find sustenance in green pastures and still waters, but they also remain protected from the lurking threats of wolves and other enemies that seek to disrupt the order of their world.
Fig. 27 The Next Time Your Horse is Behaving Well, Sell it. The numbers in private equity don’t add up because its very much like a betting in a horse race. Too many entrants and exits for anyone to have a reliable dataset with which to estimate odds for any horse-jokey vs. the others for quinella, trifecta, superfecta#
Vision, or ukubona/kuhura, emerges as the defining trait of the obedient. Those who follow the shepherd do not do so blindly; rather, they recognize the light that illuminates their path and trust in its guidance. This obedience is not submission to arbitrary rule, but an attunement to the higher order that governs their existence. Light allows the sheep to see, to navigate safely through the landscape, avoiding the perils that darkness conceals. Those who reject the shepherd’s light, who stray into the shadows, risk becoming lost—prey for the wolves that thrive in the absence of order.
The contrast between light and darkness is a fundamental one, represented by the distinction between the sheep and the goats. The sheep move toward the light, following the shepherd’s call, while the goats, symbolic of chaos and defiance, wander into obscurity. Where the sheep seek green pastures, the goats are drawn to barren lands, where their stubbornness isolates them from the abundance that obedience provides. Kubanana—the ability to live in unity, to see and be seen in the fullness of existence—defines the sheep. Kutaribaniza, on the other hand, is the fragmentation of vision, the refusal to harmonize, leading to an existence of discord and alienation.
Yet, the journey is not without opposition. Wolves—those who seek to disrupt and prey upon the vulnerable—exist in every ecosystem. They are the adversaries, the lurking dangers that test the resilience of the flock. They thrive where obedience falters, where darkness takes root. Enemies, however, are not always external; sometimes, they emerge within the flock itself, disguised among the sheep, eroding trust from within. The shepherd must be ever watchful, ensuring that the true path remains clear, that the call to the green pastures and still waters is not drowned out by fear or deception.
The payoffs of obedience are evident in the flourishing of the ecosystem. The sheep that remain in the shepherd’s care find safety, provision, and continuity. They are nourished by the land, their steps guided by the light. Still waters—symbolic of peace and sustenance—await those who remain steadfast in their vision. Darkness may threaten, and wolves may prowl at the edges of the pasture, but the shepherd’s presence ensures that the balance is maintained. In the end, the ecosystem thrives not through mere survival, but through the recognition of light, the discipline of obedience, and the wisdom to distinguish between the guiding call of the shepherd and the isolating pull of the wild.
Show 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': ['Improbability', 'Planet', 'Alive', 'Loss', "Trial", 'Error'], # Static
'Voir': ['Witness'],
'Choisis': ['Faith', 'Decision'],
'Deviens': ['Adversarial', 'Transactional', 'Hope'],
"M'èléve": ['Victory', 'Payoff', 'Loyalty', 'Charity', 'Love']
}
# Assign colors to nodes
def assign_colors():
color_map = {
'yellow': ['Witness'],
'paleturquoise': ['Error', 'Decision', 'Hope', 'Love'],
'lightgreen': ["Trial", 'Transactional', 'Payoff', 'Charity', 'Loyalty'],
'lightsalmon': ['Alive', 'Loss', 'Faith', 'Adversarial', 'Victory'],
}
return {node: color for color, nodes in color_map.items() for node in nodes}
# Define edge weights (hardcoded for editing)
def define_edges():
return {
('Improbability', 'Witness'): '1/99',
('Planet', 'Witness'): '5/95',
('Alive', 'Witness'): '20/80',
('Loss', 'Witness'): '51/49',
("Trial", 'Witness'): '80/20',
('Error', 'Witness'): '95/5',
('Witness', 'Faith'): '20/80',
('Witness', 'Decision'): '80/20',
('Faith', 'Adversarial'): '49/51',
('Faith', 'Transactional'): '80/20',
('Faith', 'Hope'): '95/5',
('Decision', 'Adversarial'): '5/95',
('Decision', 'Transactional'): '20/80',
('Decision', 'Hope'): '51/49',
('Adversarial', 'Victory'): '80/20',
('Adversarial', 'Payoff'): '85/15',
('Adversarial', 'Loyalty'): '90/10',
('Adversarial', 'Charity'): '95/5',
('Adversarial', 'Love'): '99/1',
('Transactional', 'Victory'): '1/9',
('Transactional', 'Payoff'): '1/8',
('Transactional', 'Loyalty'): '1/7',
('Transactional', 'Charity'): '1/6',
('Transactional', 'Love'): '1/5',
('Hope', 'Victory'): '1/99',
('Hope', 'Payoff'): '5/95',
('Hope', 'Loyalty'): '10/90',
('Hope', 'Charity'): '15/85',
('Hope', 'Love'): '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=9, connectionstyle="arc3,rad=0.2"
)
nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
plt.title("Heritage, Resources, Static (y) vs. Adaptation, Resourcefulness, Dynamic (x)", fontsize=15)
plt.show()
# Run the visualization
visualize_nn()
#
Fig. 28 From a Pianist View. Left hand voices the mode and defines harmony. Right hand voice leads freely extend and alter modal landscapes. In R&B that typically manifests as 9ths, 11ths, 13ths. Passing chords and leading notes are often chromatic in the genre. Music is evocative because it transmits information that traverses through a primeval pattern-recognizing architecture that demands a classification of what you confront as the sort for feeding & breeding or fight & flight. It’s thus a very high-risk, high-error business if successful. We may engage in pattern recognition in literature too: concluding by inspection but erroneously that his silent companion was engaged in mental composition he reflected on the pleasures derived from literature of instruction rather than of amusement as he himself had applied to the works of William Shakespeare more than once for the solution of difficult problems in imaginary or real life. Source: Ulysses#