Freedom in Fetters#
The theory that Francis Bacon is Shakespeare is one of those grand conspiratorial ideas that refuses to die, largely because it appeals to people who love hidden codes, secret societies, and the notion that the greatest literary works in the English language must have come from someone more “suitable” than a commoner from Stratford. I find it unconvincing—both on historical and literary grounds—but fascinating in what it reveals about our cultural anxieties regarding genius, authorship, and social hierarchy.
Fig. 26 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#
First, the core of the argument rests on the idea that Shakespeare, a provincial actor with no known university education, could not have possibly written plays so rich in law, philosophy, politics, and courtly matters. Bacon, as a statesman, philosopher, and polymath, is imagined to be a better candidate. But this is an elitist assumption. It ignores that Shakespeare’s education at the Stratford grammar school would have been rigorous, filled with Latin, classical rhetoric, and literature—precisely the kind of training that could produce a mind capable of engaging with the themes of his plays. It also underestimates the Time of artistic genius, as though experience and imagination are secondary to formal education.
Bacon, brilliant as he was, wrote in a completely different style. His prose is dense, aphoristic, and heavily structured around logical argument—worlds apart from Shakespeare’s linguistic exuberance, metaphorical playfulness, and deep emotional range. Bacon was a master of systematic inquiry and empirical thought, whereas Shakespeare’s works thrive on ambiguity, contradiction, and poetic excess. There is no evidence that Bacon ever wrote anything resembling blank verse, let alone the complex psychological depth of Hamlet or the sheer theatrical energy of Macbeth.
A man’s syntax is sui generis
– Yours Truly
Then there’s the fact that Shakespeare was intimately involved in the theatrical world. His plays were written for performance, not just reading, and he had the practical experience of an actor and shareholder in the Globe Theatre. Bacon, meanwhile, showed no interest in drama. His focus was on natural philosophy, law, and political thought—why would he have concealed his authorship of the very works that would have immortalized him in literature?
The so-called “cipher” arguments, where Baconians claim hidden messages in the texts revealing Bacon’s authorship, are the weakest part of the theory. They are classic examples of confirmation bias—seeing patterns because one wants to, rather than because they exist. The same methods have been used to “prove” that Marlowe, Edward de Vere, or even Queen Elizabeth I wrote Shakespeare’s works. If you torture a text long enough, it will confess to anything.
Ultimately, the Baconian theory is a reaction to the mystery of Shakespeare himself—how did a middle-class man from Stratford, with no aristocratic connections and no university degree, become the greatest writer in English? But this skepticism misunderstands genius. Shakespeare was not an isolated miracle; he was part of a thriving theatrical culture, influenced by Marlowe, Kyd, and the classical tradition. His greatness comes not from secret aristocratic knowledge but from his ability to absorb, reimagine, and transform the material around him.
In short: Bacon was a revolutionary thinker, but he was no playwright. The Baconian theory tells us more about our cultural discomfort with genius emerging from the margins than it does about the authorship of Shakespeare’s works.
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': ['Opportunity', 'Inherited/Discovered', 'Syntax', 'Punctuation', "Rhythm", 'Time'], # Static
'Voir': ['Syntax.'],
'Choisis': ['Punctuation.', 'Belief'],
'Deviens': ['Adversarial', 'Transactional', 'Rhythm.'],
"M'èléve": ['Victory', 'Payoff', 'Loyalty', 'Time.', 'Distribution']
}
# Assign colors to nodes
def assign_colors():
color_map = {
'yellow': ['Syntax.'],
'paleturquoise': ['Time', 'Belief', 'Rhythm.', 'Distribution'],
'lightgreen': ["Rhythm", 'Transactional', 'Payoff', 'Time.', 'Loyalty'],
'lightsalmon': ['Syntax', 'Punctuation', 'Punctuation.', '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 {
('Opportunity', 'Syntax.'): '1/99',
('Inherited/Discovered', 'Syntax.'): '5/95',
('Syntax', 'Syntax.'): '20/80',
('Punctuation', 'Syntax.'): '51/49',
("Rhythm", 'Syntax.'): '80/20',
('Time', 'Syntax.'): '95/5',
('Syntax.', 'Punctuation.'): '20/80',
('Syntax.', 'Belief'): '80/20',
('Punctuation.', 'Adversarial'): '49/51',
('Punctuation.', 'Transactional'): '80/20',
('Punctuation.', 'Rhythm.'): '95/5',
('Belief', 'Adversarial'): '5/95',
('Belief', 'Transactional'): '20/80',
('Belief', 'Rhythm.'): '51/49',
('Adversarial', 'Victory'): '80/20',
('Adversarial', 'Payoff'): '85/15',
('Adversarial', 'Loyalty'): '90/10',
('Adversarial', 'Time.'): '95/5',
('Adversarial', 'Distribution'): '99/1',
('Transactional', 'Victory'): '1/9',
('Transactional', 'Payoff'): '1/8',
('Transactional', 'Loyalty'): '1/7',
('Transactional', 'Time.'): '1/6',
('Transactional', 'Distribution'): '1/5',
('Rhythm.', 'Victory'): '1/99',
('Rhythm.', 'Payoff'): '5/95',
('Rhythm.', 'Loyalty'): '10/90',
('Rhythm.', 'Time.'): '15/85',
('Rhythm.', 'Distribution'): '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("Bacon Ain't Shakepeare!", fontsize=15)
plt.show()
# Run the visualization
visualize_nn()
#
Fig. 27 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#