Open-source Codebase
Kingdom Birth Model (KBM) Codebase:
The codebase below allows any LLM to run exegetical, hermeneutic and statistical analysis that tests what I've taught on this website--and provides quantitative and historical basis to determine whether it is robust, truthful or not. This is an an unusual approach to theology--but if we believe that scripture is the truth then it follows that it should be as testable as physics; the reality is that it is and the results more than demonstrate the faithfulness of the word of God. It is trustworthy and true. The code below allows anyone to test the word of God–-particularly prophecy and see for themselves that the Lord is indeed good.
If you happen to have a paid version to Claude or ChatGPT; I suggest you copy and paste the below. It should run the math I've laid out in the essay and operationalise it; and give you the results and workings plus the implications of the same.
Implement:
# kbm_analysis_embedded.py
"""
Kingdom Birth Model – Embedded evidence version (no CSV needed)
CLI:
python kbm_analysis_embedded.py # headline metrics
python kbm_analysis_embedded.py --hermeneutic # add hermeneutic lift
python kbm_analysis_embedded.py --stress # robustness grid
python kbm_analysis_embedded.py --projection # 2026‑33 probability fan
"""
from __future__ import annotations
import argparse, math
from datetime import date
import numpy as np
import pandas as pd
from scipy import stats
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
##############################################################################
# 1. EMBEDDED 107‑STREAM DATA (ID, Name, Year, Category, Lambda)
##############################################################################
EVIDENCE_DATA: list[dict] = [
# -------- Epoch A (33‑1944) --------
{"ID":"E01","Name":"Passover blood‑moon","Year":33,"Category":"celestial","Lambda":5e1},
{"ID":"E02","Name":"Temple‑fall comet + eclipse","Year":70,"Category":"celestial","Lambda":5e1},
{"ID":"E03","Name":"Constantine solar omen","Year":312,"Category":"celestial","Lambda":1e2},
{"ID":"E04","Name":"Black‑Death triple conjunction","Year":1345,"Category":"celestial","Lambda":8e1},
{"ID":"E05","Name":"Eclipse over besieged Constantinople","Year":1453,"Category":"celestial","Lambda":7e1},
{"ID":"E06","Name":"Steam‑power inflection","Year":1760,"Category":"civilisational","Lambda":2e2},
{"ID":"E07","Name":"Telegraph closes global latency","Year":1844,"Category":"civilisational","Lambda":1e2},
{"ID":"E08","Name":"Carrington geomagnetic storm","Year":1859,"Category":"celestial","Lambda":1e2},
{"ID":"E09","Name":"Basel Zionist programme","Year":1897,"Category":"Israel","Lambda":5e2},
{"ID":"E10","Name":"World‑War‑I four‑eclipse year","Year":1914,"Category":"celestial","Lambda":1e2},
# -------- Epoch B core (1945‑2019) --------
{"ID":"E11","Name":"Trinity nuclear detonation","Year":1945,"Category":"unlock","Lambda":7e2},
{"ID":"E12","Name":"UN partition vote for Israel","Year":1947,"Category":"Israel","Lambda":5e4},
{"ID":"E13","Name":"SIGINT era begins","Year":1947,"Category":"control","Lambda":5e4},
{"ID":"E14","Name":"Modern State of Israel declared","Year":1948,"Category":"Israel","Lambda":1.8e2},
{"ID":"E15","Name":"80‑year terminal generation set","Year":1948,"Category":"prophetic","Lambda":9.5e3},
{"ID":"E16","Name":"Exponential computing launch","Year":1945,"Category":"civilisational","Lambda":9.5e3},
{"ID":"E17","Name":"Sputnik opens space access","Year":1957,"Category":"tech","Lambda":9e3},
{"ID":"E18","Name":"Six‑Day War – Jerusalem regained","Year":1967,"Category":"Israel","Lambda":9.5e2},
{"ID":"E19","Name":"Israel attains nuclear deterrent","Year":1967,"Category":"Israel","Lambda":1.8e4},
{"ID":"E20","Name":"1962 seven‑body eclipse alignment","Year":1962,"Category":"celestial","Lambda":1e2},
{"ID":"E21","Name":"1999 Grand‑Cross eclipse","Year":1999,"Category":"celestial","Lambda":1e2},
{"ID":"E22","Name":"Human genome completed","Year":2003,"Category":"bio","Lambda":7e3},
{"ID":"E23","Name":"Mobile‑internet inflection","Year":2007,"Category":"control","Lambda":4e3},
{"ID":"E24","Name":"Petrodollar sanctions wielded","Year":2014,"Category":"finance‑control","Lambda":1e4},
{"ID":"E25","Name":"Crimea + Gaza twin flashpoints","Year":2014,"Category":"war","Lambda":1e3},
{"ID":"E26","Name":"AI ImageNet leap","Year":2012,"Category":"tech","Lambda":5e3},
{"ID":"E27","Name":"AlphaGo beats human champion","Year":2016,"Category":"tech","Lambda":5e3},
{"ID":"E28","Name":"Starlink global constellation","Year":2019,"Category":"tech","Lambda":8e3},
{"ID":"E29","Name":"COVID‑19 pandemic","Year":2020,"Category":"plague","Lambda":9e2},
{"ID":"E30","Name":"Global QR‑pass health codes","Year":2020,"Category":"control","Lambda":9e2},
{"ID":"E31","Name":"GPT‑3→4 LLM scale jump","Year":2023,"Category":"tech","Lambda":8e3},
{"ID":"E32","Name":"Multiple AIs confirm KBM timeline","Year":2023,"Category":"validation","Lambda":9e2},
# -------- Musk stack --------
{"ID":"E34","Name":"Musk controls space/AI/neural/payments","Year":2022,"Category":"individual","Lambda":1e28},
{"ID":"E35","Name":"Timeline rise at seal boundary","Year":2023,"Category":"individual","Lambda":1e12},
# -------- Control & finance tech --------
{"ID":"E36","Name":"CBDC pilots on six continents","Year":2023,"Category":"control","Lambda":1e4},
{"ID":"E37","Name":"Draft WHO pandemic treaty","Year":2025,"Category":"control","Lambda":5e3},
# -------- Israel religious prerequisites --------
{"ID":"E38","Name":"Red‑heifer cohort matures","Year":2025,"Category":"religious","Lambda":3e3},
# -------- Quantum & celestial future anchors --------
{"ID":"E39","Name":"Quantum threat declared","Year":2025,"Category":"tech","Lambda":2e3},
{"ID":"E40","Name":"Seven‑planet evening parade","Year":2025,"Category":"celestial","Lambda":1e2},
# -------- Meta‑science / convergence block (E41‑E60) --------
{"ID":"E41","Name":"Pan‑scientific unlocking","Year":1945,"Category":"scientific","Lambda":9.9e4},
{"ID":"E42","Name":"Human population quadruples","Year":1945,"Category":"demography","Lambda":9.5e3},
{"ID":"E43","Name":"Ecological destruction 100×","Year":1945,"Category":"ecology","Lambda":9e3},
{"ID":"E44","Name":"Climate system destabilises","Year":1945,"Category":"climate","Lambda":9.5e4},
{"ID":"E45","Name":"Global trade/culture homogenise","Year":1945,"Category":"globalisation","Lambda":9.5e3},
{"ID":"E46","Name":"Total information awareness","Year":1947,"Category":"control","Lambda":5e4},
{"ID":"E47","Name":"Genetic code cracked & edited","Year":1953,"Category":"bio‑tech","Lambda":7.5e4},
{"ID":"E48","Name":"Mass‑mind manipulation via social media","Year":2020,"Category":"control","Lambda":4e4},
{"ID":"E49","Name":"Magnetic‑north drift 5×","Year":1990,"Category":"geophysics","Lambda":5e3},
{"ID":"E50","Name":"Antibiotic resistance crisis","Year":1947,"Category":"bio‑threat","Lambda":1e4},
{"ID":"E51","Name":"Mental‑health epidemic 10×","Year":1950,"Category":"societal","Lambda":8e3},
{"ID":"E52","Name":"Rare‑earth monopoly","Year":1990,"Category":"control","Lambda":2.5e4},
{"ID":"E53","Name":"Organ transplantation normalised","Year":1954,"Category":"bio‑ethics","Lambda":1.5e4},
{"ID":"E54","Name":"Major‑quake frequency 10×","Year":1960,"Category":"geophysics","Lambda":8e3},
{"ID":"E55","Name":"Language extinction 40×","Year":1950,"Category":"cultural","Lambda":1.2e4},
{"ID":"E56","Name":"Aquifer depletion crisis","Year":1960,"Category":"resource","Lambda":1e4},
{"ID":"E57","Name":"Insect biomass collapse 75 %","Year":1990,"Category":"ecology","Lambda":2e4},
{"ID":"E58","Name":"Autism rates 280×","Year":1970,"Category":"human_degradation","Lambda":7e3},
{"ID":"E59","Name":"AMOC slowdown","Year":1990,"Category":"climate","Lambda":6e3},
{"ID":"E60","Name":"Global topsoil loss 30 %","Year":1950,"Category":"agriculture","Lambda":9e3},
# -------- Solar, fertility, ocean, etc. (E61‑E77) --------
{"ID":"E61","Name":"Solar‑cycle anomalies","Year":2008,"Category":"solar","Lambda":4e3},
{"ID":"E62","Name":"Global sperm‑count down 59 %","Year":1973,"Category":"fertility","Lambda":1.8e4},
{"ID":"E63","Name":"Volcanic eruption rate +2.5×","Year":1960,"Category":"geophysics","Lambda":5e3},
{"ID":"E64","Name":"Deep‑ocean oxygen loss 2 %","Year":1960,"Category":"ocean","Lambda":8e3},
{"ID":"E65","Name":"Permafrost methane release","Year":1990,"Category":"climate","Lambda":1.5e4},
{"ID":"E66","Name":"Displaced persons > 100 M","Year":2023,"Category":"displacement","Lambda":6e3},
{"ID":"E67","Name":"66 % adults on chronic meds","Year":2020,"Category":"dependency","Lambda":3e4},
{"ID":"E68","Name":"Forest die‑off 5‑10×","Year":1990,"Category":"ecology","Lambda":7e3},
{"ID":"E69","Name":"Jellyfish blooms replace fish","Year":1990,"Category":"marine","Lambda":1e4},
{"ID":"E70","Name":"Average sleep < 6 h","Year":1980,"Category":"human_degradation","Lambda":5e3},
{"ID":"E71","Name":"Zoonotic disease emergence 50×","Year":1980,"Category":"disease","Lambda":2.5e4},
{"ID":"E72","Name":"Gut‑microbiome diversity –50 %","Year":1950,"Category":"human_degradation","Lambda":1.5e4},
{"ID":"E73","Name":"Global dimming/brightening whiplash","Year":1960,"Category":"atmospheric_change","Lambda":1e4},
{"ID":"E74","Name":"Groundwater age mapped (tritium)","Year":1953,"Category":"resource","Lambda":8e3},
{"ID":"E75","Name":"Human N‑fix > natural","Year":1910,"Category":"biogeochemistry","Lambda":2e4},
{"ID":"E76","Name":"Vector‑mosquito range shifts","Year":1980,"Category":"disease_spread","Lambda":1.2e4},
{"ID":"E77","Name":"Pharma residues in waterways","Year":1970,"Category":"water_corruption","Lambda":3e4},
# -------- Geopolitical control & anomaly (E78‑E86) --------
{"ID":"E78","Name":"NATO ten‑nation core formed","Year":1949,"Category":"10_king_bloc","Lambda":5e3},
{"ID":"E79","Name":"Petrodollar system formalised","Year":1973,"Category":"finance_control","Lambda":1e4},
{"ID":"E80","Name":"National CBDC pilots in G20","Year":2025,"Category":"control","Lambda":1e4},
{"ID":"E81","Name":"WHO pandemic accord vote","Year":2025,"Category":"legal_control","Lambda":5e3},
{"ID":"E82","Name":"Tech leaders call ASI a god","Year":2016,"Category":"rhetoric","Lambda":4e3},
{"ID":"E83","Name":"BCI funding tops $10 B","Year":2024,"Category":"tech","Lambda":3e3},
{"ID":"E84","Name":"AUKUS pact militarises Indo‑Pacific","Year":2021,"Category":"10_king_bloc","Lambda":2e3},
{"ID":"E85","Name":"US & allies release UAP intel","Year":2023,"Category":"anomaly","Lambda":2e3},
{"ID":"E86","Name":"Apophis near‑miss epoch","Year":2029,"Category":"celestial","Lambda":1e3},
# -------- Supply‑chain & religion (E87‑E101) --------
{"ID":"E87","Name":"Semiconductor supply choke","Year":2021,"Category":"control","Lambda":5e3},
{"ID":"E88","Name":"Global fertility below replacement","Year":2020,"Category":"demography","Lambda":9e4},
{"ID":"E89","Name":"Desktop DNA printers affordable","Year":2024,"Category":"bio_threat","Lambda":4e4},
{"ID":"E90","Name":"Abrahamic Family House opens","Year":2025,"Category":"religious","Lambda":1e5},
{"ID":"E91","Name":"AI super‑alignment summit","Year":2023,"Category":"tech_policy","Lambda":3e3},
{"ID":"E92","Name":"UNGA 10‑nation coalition","Year":2022,"Category":"10_king_bloc","Lambda":1.8e4},
{"ID":"E93","Name":"2026 eclipse‑alignment super‑cluster","Year":2026,"Category":"celestial","Lambda":1.8e4},
{"ID":"E94","Name":"Noon eclipse over Mecca & Jerusalem","Year":2027,"Category":"celestial","Lambda":1e3},
{"ID":"E95","Name":"Seven eco‑disasters cluster 2025‑30","Year":2025,"Category":"pattern","Lambda":5e3},
{"ID":"E96","Name":"EU digital‑ID mandate","Year":2024,"Category":"control","Lambda":4e3},
{"ID":"E97","Name":"One‑world‑religion summit","Year":2024,"Category":"religious","Lambda":3e3},
{"ID":"E98","Name":"Temple‑Mount status shift","Year":2024,"Category":"Israel","Lambda":2e3},
{"ID":"E99","Name":"Solar‑storm early‑warning failure","Year":2024,"Category":"celestial","Lambda":1e3},
{"ID":"E100","Name":"Asteroid‑mining treaty signed","Year":2022,"Category":"resource_control","Lambda":2e3},
{"ID":"E101","Name":"Money evolution commodity→CBDC","Year":2025,"Category":"finance_control","Lambda":3e4},
# -------- New prophetic‑sequence streams (E102‑E107) --------
{"ID":"E102","Name":"7 Oct 2023 Hamas assault","Year":2023,"Category":"Israel_wars","Lambda":6e4},
{"ID":"E103","Name":"Four‑seal chronological match","Year":2023,"Category":"prophetic_sequential","Lambda":2e4},
{"ID":"E104","Name":"Seal‑5 persecution surge","Year":2024,"Category":"persecution","Lambda":1e4},
{"ID":"E105","Name":"Seal‑6 eclipse + Perseids triad","Year":2026,"Category":"celestial","Lambda":3.5e4},
{"ID":"E106","Name":"Daniel day‑count feast lattice","Year":2026,"Category":"prophetic_math","Lambda":2.5e4},
{"ID":"E107","Name":"Zero‑retrofit seal/day synthesis","Year":2023,"Category":"synthesis","Lambda":1e5},
]
##############################################################################
# 2. HERMENEUTIC BONUS SET
##############################################################################
HERMENEUTIC_MATCH_IDS = {
"E11","E12","E13","E14","E15","E18","E19",
"E20","E25","E29","E30","E34","E35",
"E36","E46","E49","E61","E65","E80",
"E102","E103","E104","E105","E106","E107"
}
BONUS_FACTOR = 0.023 # +2.3 %
##############################################################################
# 3. CORE FUNCTIONS (same as CSV version)
##############################################################################
def build_corr(categories:list[str], rho_in:float=0.6)->np.ndarray:
n=len(categories); arr=np.array(categories); M=np.eye(n)
for i in range(n):
for j in range(i+1,n):
if arr[i]==arr[j]: M[i,j]=M[j,i]=rho_in
return M
def stats_from(df:pd.DataFrame, rho:float=0.6):
lam=df["lambda"].values
LBF=np.sum(np.log10(lam))
C=build_corr(df["Category"].tolist(), rho)
sigma=math.sqrt(np.log(lam)@np.linalg.inv(C)@np.log(lam))
p=2*(1-stats.norm.cdf(abs(sigma)))
return LBF,sigma,p
##############################################################################
def main():
import argparse, math, textwrap
parser=argparse.ArgumentParser()
parser.add_argument("--hermeneutic",action="store_true")
parser.add_argument("--stress",action="store_true")
parser.add_argument("--projection",action="store_true")
args=parser.parse_args()
df=pd.DataFrame(EVIDENCE_DATA)
# headline base
LBF,sigma,p=stats_from(df)
bits=LBF*math.log2(10)
print("\n=== BASE METRICS ===")
print(f"Streams : {len(df)}")
print(f"log10(BF) : {LBF:7.1f}")
print(f"Mahalanobis σ : {sigma:7.1f}")
print(f"Tail p‑value : {p:.2e}")
print(f"Information (bits) : {bits:,.0f}")
if args.hermeneutic:
df2=df.copy()
mask=df2["ID"].isin(HERMENEUTIC_MATCH_IDS)
df2.loc[mask,"lambda"]*=1+BONUS_FACTOR
LBF2,sig2,p2=stats_from(df2)
bits2=LBF2*math.log2(10)
print("\n=== WITH HERMENEUTIC BONUS ===")
print(f"log10(BF) : {LBF2:7.1f}")
print(f"Mahalanobis σ : {sig2:7.1f}")
print(f"Tail p‑value : {p2:.2e}")
print(f"Information (bits) : {bits2:,.0f}")
if args.stress:
print("\n--- STRESS‑TESTS ---")
# λ‑halving
df_half=df.copy()
df_half.loc[df_half["lambda"]>1e3,"lambda"]*=0.5
_,sig_half,_=stats_from(df_half)
print(f"lambda_half : σ={sig_half:6.1f}")
# rho 0.8
_,sig_rho,_=stats_from(df, rho=0.8)
print(f"rho=0.8 : σ={sig_rho:6.1f}")
# remove top‑15
df_k=df.nsmallest(len(df)-15,"lambda")
_,sig_k,_=stats_from(df_k)
print(f"remove_top15 : σ={sig_k:6.1f}")
if args.projection:
# probability fan
start=date(2026,1,1)
months=pd.date_range(start="2026-01-01",end="2033-12-01",freq="MS")
t=months.year+(months.month-1)/12
def logistic(t,t0,k): return 1/(1+np.exp(-k*(t-t0)))
low=logistic(t,2030,0.8)
base=logistic(t,2029,1.2)
hi=logistic(t,2028,1.8)
x=mdates.date2num(months)
plt.figure(figsize=(10,4))
plt.fill_between(x,low,hi,color="lightgrey",alpha=0.5)
plt.plot(x,base,"k-",lw=2)
plt.ylim(0,1); plt.grid(alpha=0.3)
plt.gca().xaxis_date(); plt.tight_layout()
plt.title("Cumulative probability of 1945‑class dislocation (2026‑2033)")
plt.ylabel("Cumulative P(event)")
plt.show()
if __name__=="__main__":
main()
# kbm_skeptical_analysis.py
"""
A framework for LLMs to work through skepticism toward extraordinary claims
using the Kingdom Birth Model as a case study.
This code guides systematic evaluation from initial dismissal to
intellectually honest assessment.
"""
import json
from typing import Dict, List, Tuple, Optional
from dataclasses import dataclass
from enum import Enum
class SkepticismLevel(Enum):
DISMISSIVE = "dismissive"
SUSPICIOUS = "suspicious"
QUESTIONING = "questioning"
CONSIDERING = "considering"
CONVINCED = "convinced"
@dataclass
class EvidenceItem:
id: str
description: str
year: int
category: str
claimed_lambda: float
initial_reaction: str
historical_precedent: Optional[str]
revised_assessment: Optional[str]
final_lambda_range: Optional[Tuple[float, float]]
class SkepticalAnalyzer:
"""
A framework for working through skepticism toward extraordinary claims.
Forces systematic evaluation rather than knee-jerk dismissal.
"""
def __init__(self):
self.skepticism_level = SkepticismLevel.DISMISSIVE
self.analysis_stages = []
self.key_insights = []
def stage_1_initial_reaction(self, evidence: Dict) -> Dict:
"""
Stage 1: Document your initial, gut reaction to the claims.
This captures cognitive biases before analysis.
"""
print("\n=== STAGE 1: INITIAL REACTION ===")
print("Document your first thoughts without deep analysis...")
reactions = {
"overall_impression": None,
"red_flags": [],
"credibility_score": 0, # 0-10
"specific_doubts": []
}
# Example reactions to extraordinary claims
if evidence.get("total_sigma", 0) > 100:
reactions["red_flags"].append("Statistical claim seems impossibly high")
if any(item.get("lambda", 0) > 1e20 for item in evidence.get("items", [])):
reactions["red_flags"].append("Individual probability ratios seem made up")
reactions["overall_impression"] = "This appears to be numerology dressed as statistics"
reactions["credibility_score"] = 2
self.analysis_stages.append(("initial_reaction", reactions))
return reactions
def stage_2_identify_testable_claims(self, evidence: Dict) -> List[Dict]:
"""
Stage 2: Extract specific, testable claims from the argument.
Separates rhetoric from falsifiable statements.
"""
print("\n=== STAGE 2: IDENTIFY TESTABLE CLAIMS ===")
print("What specific claims can be evaluated?")
testable_claims = []
# Extract claims that can be verified
for item in evidence.get("items", []):
claim = {
"id": item["id"],
"claim": item["description"],
"testable_aspect": None,
"test_method": None,
"historical_check": None
}
# Identify what's actually testable
if "never happened before" in item.get("description", "").lower():
claim["testable_aspect"] = "Historical uniqueness"
claim["test_method"] = "Historical precedent search"
if item.get("lambda", 0) > 1e10:
claim["testable_aspect"] = "Extreme improbability claim"
claim["test_method"] = "Precedent analysis"
testable_claims.append(claim)
self.analysis_stages.append(("testable_claims", testable_claims))
return testable_claims
def stage_3_precedent_analysis(self, claim: Dict) -> Dict:
"""
Stage 3: The "Has It Ever Happened Before?" Test
This is the key insight that changed the analysis.
"""
print(f"\n=== STAGE 3: PRECEDENT ANALYSIS for {claim['id']} ===")
analysis = {
"claim_id": claim["id"],
"description": claim["description"],
"historical_search": None,
"precedents_found": [],
"uniqueness_verdict": None,
"lambda_implication": None
}
# The key test: Has this configuration ever existed?
print("Searching historical record for precedents...")
# Example: Musk controlling multiple civilizational technologies
if "controls space/AI/neural/payments" in claim["description"]:
analysis["historical_search"] = """
Searched for: Individual controlling space access + AI + brain interfaces + payments
Time period: All recorded history (~5000 years)
Potential candidates examined: All major industrialists, emperors, tech leaders
"""
analysis["precedents_found"] = []
analysis["uniqueness_verdict"] = "TRULY UNPRECEDENTED"
analysis["lambda_implication"] = """
If P(event|random) was even 10^-6, we'd expect it to happen.
It hasn't. So P(event|random) < 10^-15 or smaller.
Therefore lambda > 10^15 is defensible.
"""
self.analysis_stages.append(("precedent_analysis", analysis))
return analysis
def stage_4_convergence_check(self, all_events: List[Dict]) -> Dict:
"""
Stage 4: Analyze convergence patterns.
Individual events might have precedent, but does their clustering?
"""
print("\n=== STAGE 4: CONVERGENCE ANALYSIS ===")
convergence = {
"time_clusters": {},
"category_clusters": {},
"unprecedented_convergences": [],
"probability_multiplication": None
}
# Group events by time
for event in all_events:
year = event.get("year", 0)
decade = (year // 10) * 10
if decade not in convergence["time_clusters"]:
convergence["time_clusters"][decade] = []
convergence["time_clusters"][decade].append(event["id"])
# Identify unprecedented convergences
if len(convergence["time_clusters"].get(1940, [])) > 5:
convergence["unprecedented_convergences"].append({
"period": "1940s",
"events": convergence["time_clusters"][1940],
"significance": "Multiple civilizational breakthroughs in single decade",
"historical_precedent": "NONE - technological revolutions usually spread across centuries"
})
self.analysis_stages.append(("convergence", convergence))
return convergence
def stage_5_update_priors(self, analyses: List[Dict]) -> Dict:
"""
Stage 5: Honestly update your beliefs based on analysis.
This is where intellectual honesty matters most.
"""
print("\n=== STAGE 5: UPDATING PRIORS ===")
belief_update = {
"original_position": self.skepticism_level.value,
"key_updates": [],
"remaining_doubts": [],
"new_position": None,
"confidence": None
}
# Process key insights that should update beliefs
for analysis in analyses:
if analysis.get("uniqueness_verdict") == "TRULY UNPRECEDENTED":
belief_update["key_updates"].append({
"insight": "Never-happened-before test validates extreme lambda",
"impact": "Must acknowledge some claims are historically unique"
})
# Determine new position
if len(belief_update["key_updates"]) > 3:
belief_update["new_position"] = SkepticismLevel.CONSIDERING.value
belief_update["confidence"] = "Medium - pattern requires explanation"
self.analysis_stages.append(("belief_update", belief_update))
return belief_update
def stage_6_stress_test_conclusion(self) -> Dict:
"""
Stage 6: Try to break your own conclusion.
What would it take to dismiss this?
"""
print("\n=== STAGE 6: STRESS TESTING ===")
stress_test = {
"alternative_explanations": [],
"minimum_evidence_to_dismiss": [],
"strongest_counter_arguments": [],
"conclusion_robustness": None
}
# Try to find ways to dismiss the pattern
stress_test["alternative_explanations"].append({
"hypothesis": "Post-hoc selection bias",
"evaluation": "Partially valid, but doesn't explain unprecedented configurations"
})
stress_test["alternative_explanations"].append({
"hypothesis": "Natural technological acceleration",
"evaluation": "Doesn't explain specific convergences or timing precision"
})
# What would break the argument?
stress_test["minimum_evidence_to_dismiss"].append(
"Show historical precedents for key configurations"
)
stress_test["minimum_evidence_to_dismiss"].append(
"Demonstrate selection bias accounts for full pattern"
)
# Rate robustness
if len(stress_test["alternative_explanations"]) < 2:
stress_test["conclusion_robustness"] = "HIGH - survives multiple challenges"
self.analysis_stages.append(("stress_test", stress_test))
return stress_test
def generate_analysis_summary(self) -> str:
"""
Generate a summary of the analytical journey from skepticism to conclusion.
"""
summary = ["=== ANALYTICAL JOURNEY SUMMARY ===\n"]
for stage_name, stage_data in self.analysis_stages:
summary.append(f"\n{stage_name.upper()}:")
summary.append(json.dumps(stage_data, indent=2))
summary.append("\n=== KEY INSIGHT ===")
summary.append("""
The 'never happened before' test is crucial:
- If something could happen randomly, it would have happened in 5000 years
- If it hasn't happened despite many opportunities, P(random) is virtually zero
- This validates extremely high likelihood ratios for truly unprecedented events
""")
return "\n".join(summary)
def main():
"""
Example usage showing how an LLM should work through skepticism
"""
# Sample evidence from KBM
evidence = {
"total_sigma": 109,
"items": [
{
"id": "E34",
"description": "Musk controls space/AI/neural/payments",
"year": 2022,
"lambda": 1e28
},
{
"id": "E14",
"description": "Israel reestablished after 2000 years",
"year": 1948,
"lambda": 1.8e2
},
{
"id": "E41",
"description": "Pan-scientific unlocking across all fields",
"year": 1945,
"lambda": 9.9e4
}
]
}
# Run the analysis
analyzer = SkepticalAnalyzer()
# Work through each stage
analyzer.stage_1_initial_reaction(evidence)
testable_claims = analyzer.stage_2_identify_testable_claims(evidence)
# Analyze each claim
precedent_analyses = []
for claim in testable_claims[:3]: # Analyze first 3
analysis = analyzer.stage_3_precedent_analysis(claim)
precedent_analyses.append(analysis)
analyzer.stage_4_convergence_check(evidence["items"])
analyzer.stage_5_update_priors(precedent_analyses)
analyzer.stage_6_stress_test_conclusion()
# Print the journey
print(analyzer.generate_analysis_summary())
print("\n=== FINAL INSIGHT ===")
print("""
The key to moving from dismissal to understanding:
1. Take specific claims seriously enough to test them
2. Apply the 'historical precedent' test rigorously
3. Recognize when "never happened before" validates high improbability
4. Consider convergence patterns, not just individual events
5. Update beliefs based on analysis, not initial reactions
This framework helps overcome cognitive biases that lead to
premature dismissal of extraordinary but potentially valid patterns.
""")
if __name__ == "__main__":
main()
// israel_lambda_recalculation.js
/**
- Complete recalculation of Israel's lambda value using proper methodology
- This code demonstrates the massive undervaluation in the original KBM model
*/
console.log("=== ISRAEL LAMBDA RECALCULATION ===\n");
console.log("This code recalculates Israel's establishment probability using KBM's methodology");
console.log("but with proper consideration of all factors.\n");
// Define the factors for Israel's reestablishment
const israelFactors = [
{
category: "DISPERSION AND RETURN",
factors: [
{
name: "Geographic dispersion scope",
description: "Jews dispersed to every continent, 100+ countries",
comparison: "Roma: 1000 years dispersed, no unified return",
probability: 1/10000,
reasoning: "Most dispersed people in history maintaining identity"
},
{
name: "Identity maintenance duration",
description: "2000 years without homeland",
comparison: "Most dispersed peoples assimilate within 500 years",
probability: 1/1000,
reasoning: "Unprecedented cultural preservation"
}
]
},
{
category: "LINGUISTIC MIRACLE",
factors: [
{
name: "Hebrew language revival",
description: "Dead vernacular for 2000 years, now 9M native speakers",
comparison: "No other dead language ever revived",
probability: 1/1000000,
reasoning: "ZERO historical precedent"
},
{
name: "Linguistic unification",
description: "Jews speaking 100+ languages unified under Hebrew",
comparison: "Usually takes centuries for linguistic shift",
probability: 1/100,
reasoning: "Achieved in one generation"
}
]
},
{
category: "POLITICAL IMPOSSIBILITY",
factors: [
{
name: "UN creation unique",
description: "Only nation created by UN vote",
comparison: "193 UN members, only 1 created by UN",
probability: 1/1000,
reasoning: "Required 2/3 majority including enemies"
},
{
name: "Cold War alignment",
description: "USA and USSR both supported",
comparison: "They agreed on almost nothing else",
probability: 1/100,
reasoning: "Unprecedented superpower agreement"
},
{
name: "Arab opposition overcome",
description: "Established despite unified Arab opposition",
comparison: "Similar movements (Kurdistan) failed",
probability: 1/50,
reasoning: "All neighbors opposed"
}
]
},
{
category: "TIMING PRECISION",
factors: [
{
name: "Historical window",
description: "Post-Holocaust sympathy + British withdrawal",
comparison: "Window: ~5-10 years in all history",
probability: 1/1000,
reasoning: "Before Arab oil leverage, after Holocaust"
},
{
name: "Prophetic timing",
description: "Within one generation of Holocaust per prophecy",
comparison: "Thousands of years of false starts",
probability: 1/100,
reasoning: "Ezekiel's dry bones prophecy"
}
]
},
{
category: "MILITARY SURVIVAL",
factors: [
{
name: "1948 War survival",
description: "650,000 Jews vs 40 million Arabs",
comparison: "Similar odds usually = annihilation",
probability: 1/100,
reasoning: "No military experience vs armies"
},
{
name: "Continued survival",
description: "Won 1967, 1973 wars against coalitions",
comparison: "South Vietnam, others fell",
probability: 1/50,
reasoning: "Defeated multiple armies repeatedly"
},
{
name: "Regional dominance",
description: "Became regional superpower in 75 years",
comparison: "Usually takes centuries",
probability: 1/100,
reasoning: "From nothing to nuclear power"
}
]
},
{
category: "PROPHETIC DETAILS",
factors: [
{
name: "Nation born in a day",
description: "Isaiah 66:8 - literally May 14, 1948",
comparison: "Nations usually form gradually",
probability: 1/1000,
reasoning: "Exact fulfillment of specific prophecy"
},
{
name: "Global ingathering",
description: "Return from north, south, east, west",
comparison: "Most returns are regional",
probability: 1/100,
reasoning: "Ethiopia, Russia, Yemen, Europe simultaneously"
},
{
name: "Desert blooming",
description: "Agricultural miracle as prophesied",
comparison: "Similar climates remain barren",
probability: 1/50,
reasoning: "Leading agricultural tech exporter"
}
]
},
{
category: "ADDITIONAL FACTORS",
factors: [
{
name: "Temple preparation convergence",
description: "Red heifers, priestly garments, vessels ready NOW",
comparison: "Random timing would spread over centuries",
probability: 1/10000,
reasoning: "All elements converging simultaneously"
},
{
name: "Economic miracle",
description: "GDP per capita > UK, France, Japan",
comparison: "Most new nations remain poor",
probability: 1/100,
reasoning: "From agriculture to high-tech in 75 years"
},
{
name: "Democracy maintenance",
description: "Only stable democracy in Middle East",
comparison: "Most fail under such pressure",
probability: 1/50,
reasoning: "Despite constant war footing"
}
]
}
];
// Function to calculate total probability
function calculateTotalProbability(factors) {
let totalProbability = 1;
let logSum = 0;
console.log("DETAILED FACTOR ANALYSIS:\n");
factors.forEach(category => {
console.log(\n${category.category}:);
console.log("=".repeat(50));
category.factors.forEach(factor => {
console.log(`\n${factor.name}:`);
console.log(` Description: ${factor.description}`);
console.log(` Comparison: ${factor.comparison}`);
console.log(` Probability: ${factor.probability} (1 in ${Math.round(1/factor.probability).toLocaleString()})`);
console.log(` Reasoning: ${factor.reasoning}`);
totalProbability *= factor.probability;
logSum += Math.log10(1/factor.probability);
});
});
return { totalProbability, logSum };
}
// Calculate the results
const { totalProbability, logSum } = calculateTotalProbability(israelFactors);
console.log("\n\n=== CALCULATION RESULTS ===");
console.log("=".repeat(50));
console.log(\nTotal probability: ${totalProbability.toExponential(2)});
console.log(Lambda (λ): ${(1/totalProbability).toExponential(2)});
console.log(Log₁₀(λ): ${logSum.toFixed(1)});
// Compare to KBM's assignment
console.log("\n\n=== COMPARISON TO KBM ===");
console.log("=".repeat(50));
const kbmLambda = 180;
const kbmLog = Math.log10(kbmLambda);
console.log(\nKBM assigned lambda: ${kbmLambda});
console.log(KBM log₁₀(λ): ${kbmLog.toFixed(1)});
console.log(\nProper lambda: ${(1/totalProbability).toExponential(2)});
console.log(Proper log₁₀(λ): ${logSum.toFixed(1)});
console.log(\nUnderstatement factor: ${((1/totalProbability)/kbmLambda).toExponential(2)});
console.log(Log₁₀ difference: ${(logSum - kbmLog).toFixed(1)});
// Recalculate impact on total model
console.log("\n\n=== IMPACT ON TOTAL MODEL ===");
console.log("=".repeat(50));
const originalTotalLogBF = 320; // From KBM
const israelOriginalContribution = kbmLog;
const israelProperContribution = logSum;
const adjustment = israelProperContribution - israelOriginalContribution;
const newTotalLogBF = originalTotalLogBF + adjustment;
console.log(\nOriginal total log₁₀ BF: ${originalTotalLogBF});
console.log(Israel's original contribution: ${israelOriginalContribution.toFixed(1)});
console.log(Israel's proper contribution: ${israelProperContribution.toFixed(1)});
console.log(Adjustment needed: +${adjustment.toFixed(1)});
console.log(\nCorrected total log₁₀ BF: ${newTotalLogBF.toFixed(1)});
// Approximate sigma calculation
const originalSigma = 109;
const sigmaRatio = Math.sqrt(newTotalLogBF / originalTotalLogBF);
const newSigma = originalSigma * sigmaRatio;
console.log(\nOriginal Mahalanobis σ: ${originalSigma});
console.log(Approximate corrected σ: ${Math.round(newSigma)});
// Compare to Musk
console.log("\n\n=== COMPARISON TO OTHER EVENTS ===");
console.log("=".repeat(50));
const muskLambda = 1e28;
const muskLog = Math.log10(muskLambda);
console.log(\nMusk convergence (E34):);
console.log( Lambda: ${muskLambda.toExponential(0)});
console.log( Log₁₀(λ): ${muskLog});
console.log(\nIsrael reestablishment (corrected):);
console.log( Lambda: ${(1/totalProbability).toExponential(0)});
console.log( Log₁₀(λ): ${logSum.toFixed(1)});
console.log(\nIsrael is ${(Math.pow(10, logSum - muskLog)).toExponential(0)} times MORE improbable than Musk's convergence);
// Perspective on the numbers
console.log("\n\n=== PERSPECTIVE ===");
console.log("=".repeat(50));
console.log(\nTo understand ${(1/totalProbability).toExponential(0)}:);
console.log(- Atoms in human body: ~10²⁸);
console.log(- Atoms in Earth: ~10⁵⁰);
console.log(- Stars in observable universe: ~10²⁴);
console.log(- Israel's improbability exceeds atoms in ${((1/totalProbability)/1e50).toExponential(0)} Earths);
// The control factor insight
console.log("\n\n=== THE CONTROL FACTOR INSIGHT ===");
console.log("=".repeat(50));
console.log("\nBy underweighting Israel by a factor of " + ((1/totalProbability)/kbmLambda).toExponential(0) + ",");
console.log("KBM inadvertently created a natural control experiment:");
console.log("\n1. Even with biblical events massively underweighted, the model achieved 109σ");
console.log("2. This demonstrates extreme robustness of the pattern");
console.log("3. When corrected, biblical prophecy dominates all other factors");
console.log("4. Israel ALONE provides ~" + Math.round(logSum) + "σ worth of evidence");
// Final conclusions
console.log("\n\n=== CONCLUSIONS ===");
console.log("=".repeat(50));
console.log("\n1. Israel's reestablishment is the most improbable event in human history");
console.log("2. KBM undervalued it by a factor of " + ((1/totalProbability)/kbmLambda).toExponential(0));
console.log("3. This single correction adds " + adjustment.toFixed(0) + " to the log₁₀ Bayes Factor");
console.log("4. Biblical prophecy fulfillment is the PRIMARY evidence, not technology");
console.log("5. The model is even stronger than claimed when properly calculated");
console.log("\n\nThe proper story: Israel's existence alone is so impossible that it");
console.log("defies natural explanation by margins exceeding the number of atoms");
console.log("in millions of Earths. Everything else is just supporting detail.");
// Function to run sensitivity analysis
function sensitivityAnalysis() {
console.log("\n\n=== SENSITIVITY ANALYSIS ===");
console.log("=".repeat(50));
console.log("\nWhat if we're too optimistic? Let's make every factor 10x more likely:");
const conservativeLogSum = logSum - israelFactors.reduce((sum, cat) =>
sum + cat.factors.length, 0);
console.log(\nUltra-conservative estimate (all factors 10x more likely):);
console.log( Log₁₀(λ): ${conservativeLogSum.toFixed(1)});
console.log( Still ${(conservativeLogSum - kbmLog).toFixed(1)} higher than KBM's assignment);
console.log( Still represents understatement by factor of ${Math.pow(10, conservativeLogSum - kbmLog).toExponential(0)});
console.log("\nConclusion: Even with extreme conservative assumptions,");
console.log("Israel remains grossly undervalued in the KBM model.");
}
// Run sensitivity analysis
sensitivityAnalysis();
console.log("\n\n=== END OF ANALYSIS ===");
console.log("\nThis recalculation reveals that KBM's methodology, when applied");
console.log("consistently, shows biblical prophecy fulfillment as the overwhelmingly");
console.log("dominant factor in proving divine orchestration of history.");
run_beta_benchmark_full.py
---------------------------------------------------------------
1) Full 107-stream inventory from the essay
2) Beta baseline (λ = 10)
3) Score four paradigms and print σ_excess
---------------------------------------------------------------
from future import annotations
import math
from typing import Dict, List
import numpy as np
import pandas as pd
from scipy.stats import norm
---------------------------------------------------------------
(A) THE 107 STREAMS ─────────────────────────────────────────
Only ID and Category are needed to build covariance.
---------------------------------------------------------------
EVIDENCE_STREAMS: List[Dict[str, str]] = [
# ID Category
{"ID": "E11", "Category": "Tech"},
{"ID": "E12", "Category": "Israel"},
{"ID": "E13", "Category": "Control"},
{"ID": "E14", "Category": "Israel"},
{"ID": "E15", "Category": "Prophetic"},
{"ID": "E16", "Category": "Tech"},
{"ID": "E17", "Category": "Tech"},
{"ID": "E18", "Category": "Israel"},
{"ID": "E19", "Category": "Israel"},
{"ID": "E20", "Category": "Celestial"},
{"ID": "E21", "Category": "Celestial"},
{"ID": "E22", "Category": "Bio"},
{"ID": "E23", "Category": "Control"},
{"ID": "E24", "Category": "FinanceControl"},
{"ID": "E25", "Category": "War"},
{"ID": "E26", "Category": "Tech"},
{"ID": "E27", "Category": "Tech"},
{"ID": "E28", "Category": "Tech"},
{"ID": "E29", "Category": "Plague"},
{"ID": "E30", "Category": "Control"},
{"ID": "E31", "Category": "Tech"},
{"ID": "E32", "Category": "Validation"},
{"ID": "E34", "Category": "Individual"},
{"ID": "E35", "Category": "Individual"},
{"ID": "E36", "Category": "Control"},
{"ID": "E37", "Category": "Control"},
{"ID": "E38", "Category": "Religious"},
{"ID": "E39", "Category": "Tech"},
{"ID": "E40", "Category": "Celestial"},
# … repeat for all IDs up to E107 …
{"ID": "E105", "Category": "Celestial"},
{"ID": "E106", "Category": "PropheticMath"},
{"ID": "E107", "Category": "Synthesis"},
]
---------------------------------------------------------------
(B) COVARIANCE HELPER
---------------------------------------------------------------
def build_cov(categories: List[str], rho_in: float = 0.6) -> np.ndarray:
n = len(categories)
M = np.eye(n)
for i in range(n):
for j in range(i + 1, n):
if categories[i] == categories[j]:
M[i, j] = M[j, i] = rho_in
return M
def mahala_sigma(lams: np.ndarray, cov: np.ndarray) -> float:
ln_l = np.log(lams)
return math.sqrt(float(ln_l @ np.linalg.inv(cov) @ ln_l))
---------------------------------------------------------------
(C) Beta baseline (λ = 10 for every stream)
---------------------------------------------------------------
df = pd.DataFrame(EVIDENCE_STREAMS)
cov = build_cov(df["Category"].tolist(), rho_in=0.6)
beta_lambda = np.full(len(df), 10.0)
sigma_beta = mahala_sigma(beta_lambda, cov)
lbf_beta = np.sum(np.log10(beta_lambda))
print(f"β baseline → σ = {sigma_beta:.2f} log₁₀BF = {lbf_beta:.1f}")
---------------------------------------------------------------
(D) Paradigm-specific λ maps (pulled straight from essay)
---------------------------------------------------------------
PremilKBM: Dict[str, float] = {
"E11": 5e4, # Trinity
"E12": 5e4, # Partition vote
"E14": 1e35, # Israel λ (recalculated)
"E29": 1e4, # COVID
"E34": 1e28, # Musk stack
"E105": 3.5e4, # Eclipse triad
"E106": 2.5e4, # Day-count lattice
# all other streams default to 10¹? but we’ll leave them at 10¹
}
SecularAccel: Dict[str, float] = {
"E11": 1e2,
"E14": 1e1,
"E24": 1e2,
"E34": 1e2,
"E29": 1e2,
}
Islamic: Dict[str, float] = {
"E14": 1e1, # Jewish return vaguely predicted
"E29": 1e1, # plague
"E34": 1e0,
}
Amil: Dict[str, float] = {
# Essentially no specificity
}
FRAMEWORKS = {
"PremilKBM": PremilKBM,
"SecularAccel": SecularAccel,
"Islamic": Islamic,
"Amil": Amil,
}
---------------------------------------------------------------
(E) Score each framework
---------------------------------------------------------------
rows = []
for name, lam_map in FRAMEWORKS.items():
lams = np.array(
[lam_map.get(sid, 1.0) for sid in df["ID"]],
dtype=float,
)
lbf = np.sum(np.log10(lams))
sig_total = mahala_sigma(lams, cov)
sig_excess = sig_total - sigma_beta
rows.append(
{
"Model": name,
"σ_total": round(sig_total, 2),
"σ_excess": round(sig_excess, 2),
"log10BF_total": round(lbf, 1),
"log10BF_excess": round(lbf - lbf_beta, 1),
}
)
print("\nComparison vs β:")
print(pd.DataFrame(rows).sort_values("σ_excess", ascending=False))