⭐ Inverted Economics — Starter Notebook Outline

# Imports (customize as needed)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
 
# Optional: load helper functions or RTT utilities
# from rtt_utils import *

analysis_type = "cycle"  # options: cycle, budget, event
time_window = ("2008", "2010")
description = "Global financial crisis (structural analysis)"

# Example placeholder
df = pd.read_csv("data/sample_dataset.csv")
df.head()

regimes = {
    "R1": ["physical substrate notes"],
    "R2": ["institutional notes"],
    "R3": ["behavioral notes"]
}
regimes

# Placeholder for drift metrics or visualizations
plt.plot(df["time"], df["indicator"])
plt.title("Drift Indicator Over Time")
plt.show()

paradox_notes = [
    "Declared X but behaved as Y",
    "Incentive mismatch between A and B",
]
paradox_notes

bruteforce_examples = [
    "Emergency liquidity injection",
    "Temporary suspension of mechanism",
]
bruteforce_examples

# Placeholder visualization
plt.plot(df["time"], df["coherence_score"])
plt.title("Coherence Over Time")
plt.show()

lessons = [
    "Need for R3 behavioral modeling",
    "Avoid overreliance on brute-force tools",
]
lessons

forward_notes = [
    "Insights feed into planning for next cycle",
    "Scenario-ready adjustments for future modeling",
]
forward_notes

# Placeholder for saving results
df.to_csv("outputs/analysis_results.csv", index=False)