Veiled Resentment#
Probability#
Survival is often a matter of inches, of fractions of seconds, of unknowable variables aligning in ways that defy logic. The roulette wheel spins, the dice tumble, and somehow, against all statistical likelihood, the ball lands in the one pocket that means life instead of death. Trump tilts his head slightly—an imperceptible movement, unconscious, not a decision but a whisper from fate—and what should have been a fatal bullet becomes a graze. Jules in Pulp Fiction stands as the bullets rip through the air, missing him entirely, and in that moment, he sees not chance but divine intervention. The mangled car, crushed metal and shattered glass, should have sealed their fate at Namagunga. The numbers, the laws of physics, the cold certainty of velocity and impact should have dictated a different outcome. And yet, here they stood, breathing. In a world of fixed odds, they had hit the improbable—black 17 on the wheel when the bet was against them.
Fig. 27 Inheritence, Heir, Brand, Tribe, Religion. We’ve described these stages of societal history. We’re discussing brand: an agent exerts an ecological loss function that some consider “creative destruction”.#
Deception#
But probability is only the starting point. The real game begins in the interpretation. Poker is not about the cards you hold but the illusion you sell. The Irish sister tells them not to worry about the wreckage, about their belongings. Look elsewhere. Look at what truly matters. But is that not itself a form of bluffing? Pain does not disappear simply because attention shifts. Marilyn walks, James speaks, they all appear whole, but there is damage beyond what can be seen. The deception is layered—first, in how the world views them (“They survived, they’re fine”), and second, in how they must now view themselves (“We are fine, we must be fine”). A good poker player knows that sometimes you have to bet big on a weak hand, convince yourself of your own strength before anyone else does. Their faith becomes their tell, their ability to continue despite uncertainty becomes their bluff.
Strategy#
But bluffing will only take you so far. At some point, poker gives way to the kind of game where deception is no longer enough. You need strategy. The Formula One driver at full throttle does not pray for the best; he calculates, adjusts, makes a hundred micro-decisions a second to stay ahead. The jockey on the final stretch knows when to push his horse, when to conserve energy, when to go all in. And so, in the aftermath of the crash, strategy takes over. They pray not just out of desperation but as an active move, a calculated response to suffering. If survival itself has been an act of probability, then what follows must be intentional. They must heal, they must push forward, they must not waste the opportunity that fate, or God, or blind luck has given them. Like Lewis Hamilton squeezing every advantage from the track, they cannot afford to be passive. They must navigate their way out of the wreckage.
Knowledge#
Yet strategy without knowledge is reckless. The difference between a gambler and a general is understanding the terrain, knowing when to act and when to wait. Patton did not charge blindly—he read the battlefield, understood the weaknesses of his enemies, anticipated movements before they happened. And so, in the quiet moments after the crash, as the weight of survival settles in, the real knowledge begins to form. This was not just an accident. It was not just luck. The mission ahead is bigger than them, bigger than the scars on their bodies. The launch of the church, the work they had planned—it is now clear that it was never going to be easy. The world does not let good things happen without resistance. And so, knowledge takes shape: if survival was improbable, if deception was necessary, and if strategy was required, then this must mean something. They begin to see the pattern, the larger forces at play.
Certainty#
And in the end, certainty arrives not through logic, nor through planning, but through something deeper—something as irrational and undeniable as love at first sight. Just as Romeo knew, instantly, that Juliet was his fate, so too does faith cement itself in them. The words that had echoed in the silence return: If you are faithful to me, I will be faithful to you. Certainty is not about proof. It is not about evidence. It is about the unshakable belief that some things are true simply because they must be. And so, they step forward, past the broken glass and the bruises, past the fear and the doubts, carrying with them the absolute conviction that they were spared for a reason. The game may still be in play, the odds still shifting, the deceptions and strategies still unfolding—but in their hearts, the outcome is already decided.
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': ['Fewer Men', 'Greater Honor', 'And If To Live', 'Country Loss', "If We Are Mark’d", 'To Die, We Enough'], # Static
'Voir': ['Information'],
'Choisis': ['Baseline', 'Decision'],
'Deviens': ['Adverse Event Markers', 'Comorbidity/ICD Code', 'Temporal Changes'],
"M'èléve": ['Mortality Rate', 'Organ Failure', 'Hospitalization', 'Dependency', 'Physical Frailty']
}
# Assign colors to nodes
def assign_colors():
color_map = {
'yellow': ['Information'],
'paleturquoise': ['To Die, We Enough', 'Decision', 'Temporal Changes', 'Physical Frailty'],
'lightgreen': ["If We Are Mark’d", 'Comorbidity/ICD Code', 'Organ Failure', 'Dependency', 'Hospitalization'],
'lightsalmon': ['And If To Live', 'Country Loss', 'Baseline', 'Adverse Event Markers', 'Mortality Rate'],
}
return {node: color for color, nodes in color_map.items() for node in nodes}
# Define edge weights (hardcoded for editing)
def define_edges():
return {
('Fewer Men', 'Information'): '1/99',
('Greater Honor', 'Information'): '5/95',
('And If To Live', 'Information'): '20/80',
('Country Loss', 'Information'): '51/49',
("If We Are Mark’d", 'Information'): '80/20',
('To Die, We Enough', 'Information'): '95/5',
('Information', 'Baseline'): '20/80',
('Information', 'Decision'): '80/20',
('Baseline', 'Adverse Event Markers'): '49/51',
('Baseline', 'Comorbidity/ICD Code'): '80/20',
('Baseline', 'Temporal Changes'): '95/5',
('Decision', 'Adverse Event Markers'): '5/95',
('Decision', 'Comorbidity/ICD Code'): '20/80',
('Decision', 'Temporal Changes'): '51/49',
('Adverse Event Markers', 'Mortality Rate'): '80/20',
('Adverse Event Markers', 'Organ Failure'): '85/15',
('Adverse Event Markers', 'Hospitalization'): '90/10',
('Adverse Event Markers', 'Dependency'): '95/5',
('Adverse Event Markers', 'Physical Frailty'): '99/1',
('Comorbidity/ICD Code', 'Mortality Rate'): '1/9',
('Comorbidity/ICD Code', 'Organ Failure'): '1/8',
('Comorbidity/ICD Code', 'Hospitalization'): '1/7',
('Comorbidity/ICD Code', 'Dependency'): '1/6',
('Comorbidity/ICD Code', 'Physical Frailty'): '1/5',
('Temporal Changes', 'Mortality Rate'): '1/99',
('Temporal Changes', 'Organ Failure'): '5/95',
('Temporal Changes', 'Hospitalization'): '10/90',
('Temporal Changes', 'Dependency'): '15/85',
('Temporal Changes', 'Physical Frailty'): '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=15, connectionstyle="arc3,rad=0.2"
)
nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
plt.title("Holy Grail", fontsize=23)
plt.show()
# Run the visualization
visualize_nn()
Fig. 28 Vet Advised When to Sell Horse. Right after an outstanding performance. Because you want to control how its perceived, the narrative backstory, and how its future performance might be imputed. Otherwise, reality manifests with point and interval estimates. This is the subject matter of the opening dialogue in Miller’s Crossing.#