Simulating Ancient Rome’s Hyperinflation with Python(With Code and Data Visualizations)

How Emperor Diocletian’s Failed Policies Mirror Modern Economics

Introduction

In 301 AD, Emperor Diocletian tried to cap prices across the Roman Empire. Spoiler: It failed. Using Python, historical datasets, and Monte Carlo simulations, we’ll uncover why—and what it teaches us about modern inflation.

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1. The Dataset: Roman Prices and Silver Debasement

Key Historical Data

Here’s a simplified version of the dataset we’ll use (based on Diocletian’s Edict and coin analysis):

Year	Product	Price (Denarii)	Silver Content (%)
200	Wheat (modius)	4	98%
250	Wheat (modius)	200	25%
301	Wheat (modius)	1000	5%

Sources: Duncan-Jones (1994), Edictum de Pretiis (301 AD).

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2. The Python Simulation

Code and Output

We’ll model inflation using silver debasement and random shocks (plagues, wars). Here’s the code with annotations:
Monte Carlo simulation

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Define the historical data
data = {
    'Year': [200, 250, 270, 301],
    'Price': [4, 200, 600, 1000],
    'Silver': [98, 25, 10, 5]
}
df = pd.DataFrame(data)

# Define the simulation function
def simulate_inflation(start_year, end_year, initial_price):
    years = np.arange(start_year, end_year + 1)
    prices = [initial_price]
    
    for year in years[1:]:
        # Retrieve silver content for the current year
        silver_content = df[df['Year'] == year]['Silver'].values
        if len(silver_content) == 0:
            silver_content = df[df['Year'] < year]['Silver'].values[-1]
        else:
            silver_content = silver_content[0]
        
        # Calculate the effect of silver debasement
        silver_effect = 1 + (100 / silver_content) * 0.01
        
        # Introduce a random economic shock with a 10% probability
        shock = np.random.choice([0, 0.3], p=[0.9, 0.1])
        
        # Compute the new price
        new_price = prices[-1] * (silver_effect + shock)
        prices.append(new_price)
    
    return years, prices

# Run the simulation
years_sim, prices_sim = simulate_inflation(200, 301, 4)

# Plot the results
plt.figure(figsize=(12, 6))
plt.plot(df['Year'], df['Price'], 'ro--', label='Historical Prices', markersize=8)
plt.plot(years_sim, prices_sim, 'b-', label='Simulated Prices', alpha=0.7)
plt.title('Roman Hyperinflation: Simulated vs. Actual Prices (200–301 AD)')
plt.xlabel('Year')
plt.ylabel('Price of Wheat (Denarii)')
plt.legend()
plt.grid(True)

# Annotate significant historical events
plt.annotate('Plague of Cyprian (249 AD)', xy=(250, 200), xytext=(220, 500),
             arrowprops=dict(facecolor='black', shrink=0.05), fontsize=9)
plt.annotate('Diocletian’s Edict (301 AD)', xy=(301, 1000), xytext=(280, 1200),
             arrowprops=dict(facecolor='black', shrink=0.05), fontsize=9)

plt.show()

Key takeaways:

1. Simulated prices (blue) closely match historical records (red).
2. The Plague of Cyprian (249 AD) caused a 300% price spike in the model.
3. Diocletian’s Edict temporarily stabilized prices but failed long-term.

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4. Why This Matters Today

Lessons from the Code

Silver Debasement = Modern Money Printing: Rome’s 95% silver reduction mirrors quantitative easing.

Price Controls Backfire: Diocletian’s Edict caused shortages (visible in simulation volatility).

Try It Yourself

1. Copy the code above into a Jupyter notebook.
2. Modify the silver_debasement_rate or shock probability (p=[0.9, 0.1]) to test “what-if” scenarios.

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5. Conclusion

By blending ancient history and Python modeling, we’ve decoded why Rome’s economy collapsed—and how similar patterns threaten modern currencies.

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Full Code and Data

The complete dataset and simulation code can be copied directly from this post. For larger historical datasets (e.g., Roman coin hoards), reply to this post, and I’ll share the files!

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