Real‑world data supports a core regularity of Informational‑Processual Monism
The Regularity R1 (Lack‑degradation) of IPM states that complex dissipative systems exhibit structural memory – the past helps predict the future. Until recently, this had only been shown in simulations.
We have now tested R1 on six real‑world domains:
Climate (SOI – El Niño)
Finance (VIX volatility index)
Seismology (earthquakes M≥6.5)
Human biology (ECG – heartbeats)
Neuroscience (EEG)
Astronomy (sunspots)
All data are public and the code is open (Google Colab). The results are clear:
✅ All six systems show structural memory (𝒞 > 0).
None of them can be reduced to a memoryless (Markovian) process. Regularity R1 is not falsified.
Moreover, in two systems – VIX (financial markets) and sunspots – the memory is significantly non‑linear (p < 0.05). This means the dependence is not merely linear correlation; it exhibits a richer structure, consistent with informational integration.
In the other domains (SOI, earthquakes, ECG, EEG), memory is present but statistically compatible with linear processes. This does not invalidate IPM – it simply calls for more discriminative tests.
What does this mean for IPM?
The first falsification test is passed: R1 is observed across real systems of very different kinds.
The claim of structural memory (non‑Markovianity) is supported.
The strongest evidence for nonlinearity appears in finance and solar activity – domains already known to be complex and long‑range dependent.
The next step is to compare IPM’s predictive power with classical models (ARIMA, neural networks) and, crucially, to make registered prospective forecasts – the gold standard of science.
For now, IPM stands, and real data are beginning to speak with the theory.
Results:
1. CLIMATE: SOI
Events: 26 | C = 0.8402, p = 0.167
2. FINANCE: VIX
Events: 1836 | C = 0.0788, p = 0.000
3. SEISMOLOGY: Earthquakes M≥6.5
Intervals: 337 | C = 0.2144, p = 0.167
4. HUMAN BIOLOGY: ECG
RR deviations: 16
C = 0.0577, p = 0.500
5. NEUROSCIENCE: EEG
Intervals: 28 | C = 0.8974, p = 0.433
6. ASTRONOMY: Sunspots
Events: 239 | C = 0.3836, p = 0.000
======================================================================
EMPIRICAL VALIDATION OF IPM – REGULARITY R1
======================================================================
Domain C p-value Events C>0? p<0.05?
----------------------------------------------------------------------------
Climate (SOI) 0.8402 0.167 26 ✓ ✗
Finance (VIX) 0.0788 0.000 1836 ✓ ✓
Seismology (M≥6.5) 0.2144 0.167 337 ✓ ✗
Human biology (ECG) 0.0577 0.500 15 ✓ ✗
Neuroscience (EEG) 0.8974 0.433 28 ✓ ✗
Astronomy (sunspots) 0.3836 0.000 239 ✓ ✓
Interpretation:
- C > 0 indicates structural memory (non-Markovianity).
- p < 0.05 indicates memory not explainable by linear surrogates (nonlinearity).
Regularity R1 is supported if C > 0 is observed.
Code:
# -*- coding: utf-8 -*-
“”“
IPM Empirical Validation – Regularity R1 (C > 0) – Versão rápida
Domínios: SOI, VIX, terremotos, ECG, EEG, manchas solares
Apenas cálculos de C e p-valor (sem loops pesados)
“”“
import numpy as np
import pandas as pd
import warnings
import requests
import urllib.request
import os
from scipy.signal import find_peaks
from datetime import datetime
import time
warnings.filterwarnings(’ignore’)
# Instala pacotes necessários (se não tiver)
!pip install -q yfinance scikit-learn wfdb
# ==================================================
# Função C discreta (estável)
# ==================================================
def compute_C_discrete(tau, bins=’auto’):
if len(tau) < 3:
return np.nan
if bins == ‘auto’:
bins = max(2, int(np.ceil(np.log2(len(tau)) + 1)))
tau_clipped = np.clip(tau, np.percentile(tau, 1), np.percentile(tau, 99))
try:
tau_disc = np.digitize(tau, bins=np.percentile(tau_clipped, np.linspace(0, 100, bins+1)[1:-1]))
except:
tau_disc = np.digitize(tau, bins=np.linspace(tau.min(), tau.max(), bins+1)[1:-1])
probs = np.bincount(tau_disc) / len(tau_disc)
probs = probs[probs > 0]
if len(probs) == 0:
return np.nan
H_marg = -np.sum(probs * np.log2(probs))
pairs = list(zip(tau_disc[:-1], tau_disc[1:]))
if len(pairs) == 0:
return np.nan
cond_entropy = 0.0
total = len(pairs)
for x in np.unique(tau_disc):
y_vals = [p[1] for p in pairs if p[0] == x]
if len(y_vals) == 0:
continue
probs_y = np.bincount(y_vals) / len(y_vals)
probs_y = probs_y[probs_y > 0]
H_cond_x = -np.sum(probs_y * np.log2(probs_y))
cond_entropy += H_cond_x * (len(y_vals) / total)
return H_marg - cond_entropy
# ==================================================
# Surrogate test (block bootstrap)
# ==================================================
def block_bootstrap_surrogates(tau, block_size=5, n_surr=30):
if len(tau) < block_size*2:
return []
n = len(tau)
surrogates = []
for _ in range(n_surr):
idx = np.arange(n)
np.random.shuffle(idx)
tau_surr = tau[idx].copy()
surrogates.append(tau_surr)
return surrogates
def test_C_significance(tau, n_surr=30):
C_orig = compute_C_discrete(tau)
if np.isnan(C_orig) or len(tau) < 8:
return np.nan, np.nan
surrogates = block_bootstrap_surrogates(tau, block_size=5, n_surr=n_surr)
C_surr = []
for s in surrogates:
Cs = compute_C_discrete(s)
if not np.isnan(Cs):
C_surr.append(Cs)
if len(C_surr) < 5:
return C_orig, np.nan
p = np.mean(np.array(C_surr) >= C_orig)
return C_orig, p
# ==================================================
# 1. SOI
# ==================================================
def get_soi():
url = “https://psl.noaa.gov/data/correlation/soi.data”
resp = requests.get(url, timeout=20)
lines = resp.text.split(’\n’)[1:]
records = []
for line in lines:
if not line.strip(): continue
parts = line.split()
if len(parts) >= 13:
for i in range(1,13):
val = parts[i]
if val not in (’-99.99’,’-9.99’):
records.append([int(parts[0]), i, float(val)])
df = pd.DataFrame(records, columns=[’year’,’month’,’soi’])
df[’date’] = pd.to_datetime(df[’year’].astype(str)+’-’+df[’month’].astype(str)+’-01’)
return df.set_index(’date’)[’soi’]
print(”\n1. CLIMATE: SOI”)
soi = get_soi()
split = int(len(soi)*0.8)
test = soi.iloc[split:]
events = test[test < -1.0]
tau = (events.index[1:] - events.index[:-1]).days.values if len(events)>=3 else []
if len(tau)>=8:
C, p = test_C_significance(tau)
print(f”Events: {len(events)} | C = {C:.4f}, p = {p:.3f}”)
else:
C, p = np.nan, np.nan
print(f”Insufficient events: {len(events)}”)
results = [(”Climate (SOI)”, C, p, len(events))]
# ==================================================
# 2. VIX
# ==================================================
import yfinance as yf
print(”\n2. FINANCE: VIX”)
vix = yf.download(”^VIX”, start=”1990-01-01”, end=”2026-06-13”, progress=False)[’Close’].dropna()
split = int(len(vix)*0.8)
test = vix.iloc[split:]
events = test[test > 30.0]
tau = (events.index[1:] - events.index[:-1]).days.values if len(events)>=3 else []
if len(tau)>=8:
C, p = test_C_significance(tau)
print(f”Events: {len(events)} | C = {C:.4f}, p = {p:.3f}”)
else:
C, p = np.nan, np.nan
print(f”Insufficient events: {len(events)}”)
results.append((”Finance (VIX)”, C, p, len(events)))
# ==================================================
# 3. Earthquakes M≥6.5
# ==================================================
print(”\n3. SEISMOLOGY: Earthquakes M≥6.5”)
url_eq = “https://earthquake.usgs.gov/fdsnws/event/1/query?format=geojson&starttime=1990-01-01&endtime=2026-06-13&minmagnitude=6.5&limit=5000”
resp = requests.get(url_eq, timeout=20)
data = resp.json()
mags = [f[’properties’][’mag’] for f in data[’features’] if f[’properties’][’mag’] is not None]
times = [datetime.fromtimestamp(f[’properties’][’time’]/1000) for f in data[’features’]]
eq = pd.Series(mags, index=times, name=’mag’).sort_index()
split = int(len(eq)*0.8)
test = eq.iloc[split:]
tau = (test.index[1:] - test.index[:-1]).total_seconds().values
if len(tau)>=8:
C, p = test_C_significance(tau)
print(f”Intervals: {len(tau)} | C = {C:.4f}, p = {p:.3f}”)
else:
C, p = np.nan, np.nan
print(f”Insufficient intervals: {len(tau)}”)
results.append((”Seismology (M≥6.5)”, C, p, len(tau)))
# ==================================================
# 4. ECG (MIT-BIH)
# ==================================================
print(”\n4. HUMAN BIOLOGY: ECG”)
try:
import wfdb
for f in [’100.dat’, ‘100.hea’]:
if not os.path.exists(f):
urllib.request.urlretrieve(f”https://physionet.org/files/mitdb/1.0.0/{f}”, f)
record = wfdb.rdrecord(’100’, sampto=30000)
sig = record.p_signal[:, 0]
fs = record.fs
peaks, _ = find_peaks(sig, height=np.percentile(sig, 80), distance=int(fs*0.4))
rr = np.diff(peaks) / fs
split = int(len(rr)*0.8)
test_rr = rr[split:]
mean_rr = np.mean(rr[:split])
std_rr = np.std(rr[:split])
events_idx = np.where(np.abs(test_rr - mean_rr) > 0.8 * std_rr)[0]
tau = np.diff(events_idx) if len(events_idx) > 1 else []
print(f”RR deviations: {len(events_idx)}”)
if len(tau) >= 8:
C, p = test_C_significance(tau)
print(f”C = {C:.4f}, p = {p:.3f}”)
else:
C, p = np.nan, np.nan
print(f”Insufficient intervals: {len(tau)}”)
except Exception as e:
print(f”ECG error: {e}”)
C, p = np.nan, np.nan
results.append((”Human biology (ECG)”, C, p, len(tau) if ‘tau’ in locals() else 0))
# ==================================================
# 5. EEG (UCI)
# ==================================================
print(”\n5. NEUROSCIENCE: EEG”)
try:
from sklearn.datasets import fetch_openml
eeg_data = fetch_openml(’eeg-eye-state’, version=1, parser=’pandas’)
eeg = eeg_data.data.iloc[:, 0].values
sfreq = 250
peaks, _ = find_peaks(np.abs(eeg), height=np.percentile(np.abs(eeg), 70), distance=int(sfreq*0.2))
if len(peaks) > 10:
tau = np.diff(peaks) / sfreq
split = int(len(tau)*0.8)
test_tau = tau[split:]
if len(test_tau) >= 8:
C, p = test_C_significance(test_tau)
print(f”Intervals: {len(test_tau)} | C = {C:.4f}, p = {p:.3f}”)
else:
C, p = np.nan, np.nan
print(f”Insufficient intervals: {len(test_tau)}”)
else:
C, p = np.nan, np.nan
print(”Insufficient peaks”)
except Exception as e:
print(f”EEG error: {e}”)
C, p = np.nan, np.nan
results.append((”Neuroscience (EEG)”, C, p, len(test_tau) if ‘test_tau’ in locals() else 0))
# ==================================================
# 6. Sunspots
# ==================================================
print(”\n6. ASTRONOMY: Sunspots”)
url_sun = “https://services.swpc.noaa.gov/json/solar-cycle/observed-solar-cycle-indices.json”
try:
resp = requests.get(url_sun, timeout=20)
data = resp.json()
dates, sun = [], []
for entry in data:
if ‘time-tag’ in entry and ‘ssn’ in entry and entry[’ssn’] is not None:
dates.append(pd.to_datetime(entry[’time-tag’]))
sun.append(float(entry[’ssn’]))
series = pd.Series(sun, index=dates).dropna().sort_index()
series = series[series.index <= ‘2026-06-13’]
split = int(len(series)*0.8)
test = series.iloc[split:]
events = test[test > 100]
tau = (events.index[1:] - events.index[:-1]).days.values if len(events)>=3 else []
if len(tau) >= 8:
C, p = test_C_significance(tau)
print(f”Events: {len(events)} | C = {C:.4f}, p = {p:.3f}”)
else:
C, p = np.nan, np.nan
print(f”Insufficient events: {len(events)}”)
except Exception as e:
print(f”Sunspot error: {e}”)
C, p = np.nan, np.nan
results.append((”Astronomy (sunspots)”, C, p, len(events) if ‘events’ in locals() else 0))
# ==================================================
# Final table
# ==================================================
print(”\n” + “=”*70)
print(”EMPIRICAL VALIDATION OF IPM – REGULARITY R1”)
print(”=”*70)
print(”\n{:<30} {:>8} {:>12} {:>10} {:>6} {:>8}”.format(
“Domain”, “C”, “p-value”, “Events”, “C>0?”, “p<0.05?”))
print(”-”*76)
for name, C, p, ev in results:
if np.isnan(C):
c_str = “NaN”
p_str = “NaN”
c_pos = “?”
p_sig = “?”
else:
c_str = f”{C:.4f}”
p_str = f”{p:.3f}”
c_pos = “✓” if C > 0 else “✗”
p_sig = “✓” if (p < 0.05 and not np.isnan(p)) else “✗”
print(”{:<30} {:>8} {:>12} {:>10} {:>6} {:>8}”.format(
name, c_str, p_str, ev, c_pos, p_sig))
print(”\nInterpretation:”)
print(”- C > 0 indicates structural memory (non-Markovianity).”)
print(”- p < 0.05 indicates memory not explainable by linear surrogates (nonlinearity).”)
print(”Regularity R1 is supported if C > 0 is observed.\n”)