Ecosystem, ∂Y 🎶 ✨ 👑#
Human emotions are not infinite in number, but rather infinite in expression. They can be collapsed into a small, fundamental set—nodes of raw affect—while the richness of human experience arises from the sheer number of edges connecting them. This compression is not a simplification, but a deep structure, akin to how the vast library of human language emerges from a finite set of phonemes or how music, with its boundless expressive possibilities, is governed by a limited set of pitches and harmonies. The essence of our emotional lives is not in the sheer number of unique emotions we can name, but in the complexity of the pathways between them, the cadences that shape their interplay.
Consider how a single moment—one shimmering instance of perception—can be folded into a node of experience and unfurled again into the poetic. Take Maugham’s description of Isabel in The Razor’s Edge: she is not merely joyful, but joy in motion, joy refracted through the warm hues of life itself. The observation does not stop at her features; it leaps into metaphor, transmuting a moment of human beauty into something ripe, golden, and luscious. But the metaphor does not stand alone. It emerges from the interplay of edges, from the way desire glances off admiration, how vitality pulses against transience. The image of Isabel as a pear exists not as a singular metaphor but as a node embedded in an invisible web, one that links attraction to ephemerality, youth to temptation, and the sensuous world to the passage of time. If emotions were independent entities, isolated in their own domains, the comparison would fail—it would be a static thing, lifeless and inert. But because emotions are networked, because they compress and expand in endless permutations, such an image can strike us as not only true, but inevitable.
Fig. 1 I got my hands on every recording by Hendrix, Joni Mitchell, CSN, etc (foundations). Thou shalt inherit the kingdom (yellow node). And so why would Bankman-Fried’s FTX go about rescuing other artists failing to keep up with the Hendrixes? Why worry about the fate of the music industry if some unknown joe somewhere in their parents basement may encounter an unknown-unknown that blows them out? Indeed, why did he take on such responsibility? - Great question by interviewer. The tonal inflections and overuse of ecosystem (a node in our layer 1) as well as clichêd variant of one of our output layers nodes (unknown) tells us something: our neural network digests everything and is coherenet. It’s based on our neural anatomy!#
In the architecture of human emotion, each primary node—such as joy, grief, anger, desire, fear, awe—is not an endpoint but a waypoint, a site of convergence for countless trajectories. The shape of a life is drawn not by the mere presence of these emotions but by the density and variety of edges among them. Some move quickly between poles, leaping from despair to exhilaration in a volatile arc. Others, more sedimented, shift slowly, emotions folding over themselves like the strata of a weathered rock. A man who loves with hesitation may be marked by a long, cautious edge between desire and surrender, while one who lives recklessly has drawn a near-straight path between impulse and regret. The most intricate characters in literature—and in life—are those whose emotions are densely interconnected, whose inner landscapes are composed not of isolated peaks and valleys, but of a fractal web where every movement resonates across the whole.
If we attempt to quantify the number of edges in this emotional graph, we quickly move from the finite to the combinatorial. A system of six fundamental nodes creates 15 direct connections. Add a few more nodes, and the number of relationships explodes, growing not linearly but exponentially. This is why, though we may recognize archetypal emotions across all human history, their manifestations never repeat in precisely the same form. Every person, every story, every age rewrites the network, altering the weight of connections, shifting the dominant pathways. Shakespeare’s tragedies and Beethoven’s symphonies touch the same fundamental emotions as the love letters of an anonymous couple, yet the path they take through that space is singular. Compression does not diminish; it enriches. It distills infinity into the comprehensible, allowing complexity to emerge from simplicity, and allowing metaphor—the most human of inventions—to be not an embellishment, but a necessity.
Thus, we do not experience the world as a list of emotions but as a dance of relationships among them, a symphony of edges. The greatest art, the most profound moments of life, are those in which these connections are illuminated, when we are given the sudden clarity to see how one emotion unfurls into another, how grief and love are not opposites but echoes, how awe and fear are twins separated only by perspective. The human soul, then, is not an amorphous cloud of feeling but a structured, dynamic network—a fractal of meaning that forever folds and unfolds itself anew.
Show code cell source
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("Emotional & Cadencial Palate", fontsize=15)
plt.show()
# Run the visualization
visualize_nn()
Fig. 2 Cadences of Proprioty (top-down) & Impropriety (bottom-up). “My poor Elliott, if you’d ever had a grown-up daughter you’d know that by comparison a bucking steer is easy to manage. And as to knowing what goes on inside her—well, it’s much better to pretend you’re the simple, innocent old fool she almost certainly takes you for.” “But you have talked the matter over with her?”. “I tried to. She laughed at me and told me there was really nothing to tell.” “Is she cut up?” “I wouldn’t know. All I do know is that she eats well and sleeps like a child.” “Well, take my word for it, if you let them go on like this they’ll go off one of these days and get married without saying a word to anybody. ”Mrs. Bradley permitted herself to smile. “It must be a relief to you to think that at present we’re living in a country where every facility is afforded to sexual irregularity and every obstacle put in the way of marriage.” “And quite rightly. Marriage is a serious matter on which rest the security of the family and the stability of the state. But marriage can only maintain its authority if extraconjugal relations are not only tolerated but sanctioned. Prostitution, my poor Louisa——”“That’ll do, Elliott,” interrupted Mrs. Bradley. “I’m not interested in your views on the social and moral value of promiscuous fornication.” It was then he put forward a scheme that would interrupt Isabel’s continued intercourse with Larry that was so repugnant to his sense of what was fitting.” Here’s the edited passage with enhanced clarity and effect, aligning with your neural network’s structure. I’ve also speculated on the role of green nodes and their edges. Green nodes represent the iterative, stabilizing forces—the underlying social logic that manages tensions between propriety and impropriety. They govern likelihood, meaning, and empathy, weaving together transactional mechanisms that turn raw impulse (red nodes) into sustained social norms (blue nodes). Green Edges as Feedback Loops: The iterative give-and-take between generations: Mrs. Bradley tolerates her daughter’s autonomy, even as Elliott clings to outdated codes of conduct. Social hypocrisy as a regulating mechanism: Elliott rationalizes extramarital affairs as a safeguard for marriage—a classic self-justifying paradox. The balance between appearances and realities: Isabel’s laughter at her mother’s concern suggests the superficiality of control, where compliance is often just an illusion. The green pathways ensure that neither propriety nor impropriety spirals into chaos. They reweight social constructs, adapting them across generations while preserving the appearance of stability, if not its substance. Source: The Razor’s Edge & Yours Truly#