Dancing in Chains#
In George Orwell’s Animal Farm, the relationship between the animals and Mr. Jones mirrors an immune system engaging with foreign invaders. Mr. Jones and his human associates can be understood as the immune system, striving to maintain a controlled and stable environment, while the animals represent various evasion and subversion strategies akin to pathogens adapting to an immune response. By mapping this dynamic onto an immunological framework, we can explore how Orwell’s allegory of revolution and power struggles can be interpreted through the lens of biological conflict.
Fig. 31 Given our deep dive into “nonself” and “self” through biological systems, signal detection, and the metaphor of Uganda’s and Africa’s identity, I’d love to ask you: How do you see the interplay of cultural “noise” and “signal” shaping your own perception of Ugandan identity today—particularly in balancing traditional tribal heritage with the modern, global influences that have woven into its fabric? It ties into our exploration of ambiguity and convergence, and I’m curious about your personal lens on this dynamic.#
At the core of the immune system is innate recognition, where the body identifies and responds to threats. In Animal Farm, this corresponds to the foundational authority of Mr. Jones and his methods of control. His rule is unquestioned at the outset, akin to an immune system recognizing self-antigens as non-threatening. However, the revolutionary ideas spread by Old Major act like a pathogenic trigger—akin to a pathogen releasing Major’s Visions to provoke an immune reaction. The animals, initially disorganized and unstructured, start responding to this stimulus, much like an immune response activating against an invading microbe. The Beasts of England anthem functions as an inflammatory cytokine signal, galvanizing the animals into action and setting the stage for an immune reaction to the perceived oppression.
The initial uprising of the animals against Mr. Jones can be compared to the innate immune system’s first response to an infection. This phase, represented in immunology by pattern recognition receptors (PRRs) engaging with molecular patterns associated with pathogens, aligns with the revolutionary surge against Jones’ rule. Just as an immune response relies on pattern recognition to detect pathogens, the animals collectively recognize their shared oppression and act. Their revolution is swift and powerful, much like an acute inflammatory response seeking to expel a foreign invader.
Dear Alien
Are you friend or foe, … or other?
Once the immune system has engaged with a pathogen, adaptive immunity takes over. In the animal hierarchy that emerges post-revolution, the pigs, led by Napoleon and Snowball, serve as adaptive immune elements—refining the revolution’s objectives and implementing new structures of power. Snowball, with his idealistic and intellectual approach, can be compared to Snowball Idealist T-helper cells that orchestrate and regulate immune responses. Napoleon, on the other hand, aligns with Napoleon Enforcer cytotoxic T-cells, which eliminate perceived threats with ruthless efficiency. His purges, propaganda, and eventual consolidation of power reflect how an immune system refines its response, sometimes attacking elements that were previously part of the system, similar to autoimmunity.
Over time, the pigs transition from liberators to oppressors, mirroring how immune responses can sometimes become overactive or misdirected, leading to chronic inflammation or autoimmunity. The pigs enforce new rules, alter history, and suppress dissent, much like how immune regulatory mechanisms sometimes malfunction, causing the system to attack its own cells. This transformation illustrates Orwell’s central theme of revolution’s tendency to replicate the structures it sought to overthrow. As Napoleon consolidates power, the adaptive immune system becomes overly aggressive, leading to an environment where former allies are now targeted, and the line between friend and foe becomes blurred.
The evolution of the farm’s hierarchy and oppression under Napoleon also introduces regulatory immune elements. Regulatory T-cells (Tregs) in immunology function to suppress excessive immune responses and maintain tolerance. In Animal Farm, characters such as Squealer serve this role—manipulating information to convince the animals that Napoleon’s rule is justified, thus preventing further revolts. The remaining animals, exhausted and compliant, resemble an immune system that has been manipulated into tolerating persistent infection, much like how chronic infections or cancer subvert immune regulation to evade destruction.
In the end, Animal Farm serves as a compelling biological metaphor for the interplay between immune defenses and evasion strategies. The revolutionary movement that sought to free the animals ultimately morphs into a new form of oppression, just as immune responses, initially protective, can lead to autoimmunity or chronic disease if misregulated. Orwell’s critique of power is thus reflected in a biological reality: systems meant to protect can become instruments of control, and adaptation can turn survival mechanisms into cycles of domination and subjugation.
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': ['Mr. Jones, 5%', 'Pigs Doctrine', "Major's Vision", 'Beasts of England', "Napoleons Strategy", 'Snowball Plan'],
'Voir': ['Rebellion, 20%'],
'Choisis': ['Napoleon Enforcer, 50%', 'Snowball Idealist'],
'Deviens': ['Exhaution of Animals', 'Dogs, Loyal Enforcers', 'Squealors Propaganda, 20%'],
"M'èléve": ['Final Oppression', 'Surveillance', 'Indoctrination', 'Absolute Rule, 5%', 'New Hierarchy']
}
# Assign colors to nodes
def assign_colors():
color_map = {
'yellow': ['Rebellion, 20%'],
'paleturquoise': ['Snowball Plan', 'Snowball Idealist', 'Squealors Propaganda, 20%', 'New Hierarchy'],
'lightgreen': ["Napoleons Strategy", 'Dogs, Loyal Enforcers', 'Surveillance', 'Absolute Rule, 5%', 'Indoctrination'],
'lightsalmon': ["Major's Vision", 'Beasts of England', 'Napoleon Enforcer, 50%', 'Exhaution of Animals', 'Final Oppression'],
}
return {node: color for color, nodes in color_map.items() for node in nodes}
# Define edge weights
def define_edges():
return {
('Mr. Jones, 5%', 'Rebellion, 20%'): '1/99',
('Pigs Doctrine', 'Rebellion, 20%'): '5/95',
("Major's Vision", 'Rebellion, 20%'): '20/80',
('Beasts of England', 'Rebellion, 20%'): '51/49',
("Napoleons Strategy", 'Rebellion, 20%'): '80/20',
('Snowball Plan', 'Rebellion, 20%'): '95/5',
('Rebellion, 20%', 'Napoleon Enforcer, 50%'): '20/80',
('Rebellion, 20%', 'Snowball Idealist'): '80/20',
('Napoleon Enforcer, 50%', 'Exhaution of Animals'): '49/51',
('Napoleon Enforcer, 50%', 'Dogs, Loyal Enforcers'): '80/20',
('Napoleon Enforcer, 50%', 'Squealors Propaganda, 20%'): '95/5',
('Snowball Idealist', 'Exhaution of Animals'): '5/95',
('Snowball Idealist', 'Dogs, Loyal Enforcers'): '20/80',
('Snowball Idealist', 'Squealors Propaganda, 20%'): '51/49',
('Exhaution of Animals', 'Final Oppression'): '80/20',
('Exhaution of Animals', 'Surveillance'): '85/15',
('Exhaution of Animals', 'Indoctrination'): '90/10',
('Exhaution of Animals', 'Absolute Rule, 5%'): '95/5',
('Exhaution of Animals', 'New Hierarchy'): '99/1',
('Dogs, Loyal Enforcers', 'Final Oppression'): '1/9',
('Dogs, Loyal Enforcers', 'Surveillance'): '1/8',
('Dogs, Loyal Enforcers', 'Indoctrination'): '1/7',
('Dogs, Loyal Enforcers', 'Absolute Rule, 5%'): '1/6',
('Dogs, Loyal Enforcers', 'New Hierarchy'): '1/5',
('Squealors Propaganda, 20%', 'Final Oppression'): '1/99',
('Squealors Propaganda, 20%', 'Surveillance'): '5/95',
('Squealors Propaganda, 20%', 'Indoctrination'): '10/90',
('Squealors Propaganda, 20%', 'Absolute Rule, 5%'): '15/85',
('Squealors Propaganda, 20%', 'New Hierarchy'): '20/80'
}
# Define edges to be highlighted in black
def define_black_edges():
return {
('Mr. Jones, 5%', 'Rebellion, 20%'): '1/99',
('Pigs Doctrine', 'Rebellion, 20%'): '5/95',
}
# Calculate node positions
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()
black_edges = define_black_edges()
G = nx.DiGraph()
pos = {}
node_colors = []
# Create mapping from original node names to numbered labels
mapping = {}
counter = 1
for layer in layers.values():
for node in layer:
mapping[node] = f"{counter}. {node}"
counter += 1
# Add nodes with new numbered labels 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):
new_node = mapping[node]
G.add_node(new_node, layer=layer_name)
pos[new_node] = position
node_colors.append(colors.get(node, 'lightgray'))
# Add edges with updated node labels
edge_colors = []
for (source, target), weight in edges.items():
if source in mapping and target in mapping:
new_source = mapping[source]
new_target = mapping[target]
G.add_edge(new_source, new_target, weight=weight)
edge_colors.append('black' if (source, target) in black_edges else 'lightgrey')
# 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=edge_colors,
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("Animal Farm", fontsize=18)
plt.show()
# Run the visualization
visualize_nn()
Fig. 32 Chapter X. Years passed. The seasons came and went, the short animal lives fled by. A time came when there was no one who remembered the old days before the Rebellion, except Clover, Benjamin, Moses the raven, and a number of the pigs.Muriel was dead; Bluebell, Jessie, and Pincher were dead. Jones too was dead—he had died in an inebriates’ home in another part of the country. Snowball was forgotten. Boxer was forgotten, except by the few who had known him. Clover was an old stout mare now, stiff in the joints and with a tendency to rheumy eyes. She was two years past the retiring age, but in fact no animal had ever actually retired. The talk of setting aside a corner of the pasture for superannuated animals had long since been dropped. Napoleon was now a mature boar of twenty-four stone. Squealer was so fat that he could with difficulty see out of his eyes. Only old Benjamin was much the same as ever, except for being a little greyer about the muzzle, and, since Boxer’s death, more morose and taciturn than ever.#