Monte Carlo Divination: Probabilistic Scenario Generation
BY NICOLE LAU
Traditional divination gives you one reading, one prediction. But the future is not deterministicβit's probabilistic, a distribution of possible outcomes with varying likelihoods. A single reading cannot capture this uncertainty. What if you could run 100 readings, 1000 readings, and see the full probability distribution of futures?
In Dynamic Divination Modeling Theory, we apply Monte Carlo simulationβa statistical method that runs thousands of random scenarios to generate probability distributions. This transforms divination from single-point prediction into rigorous probabilistic forecasting, revealing not just what might happen, but how likely each outcome is.
This article teaches you how to apply Monte Carlo methods to divination, generating robust probability distributions and confidence intervals for any question.
What is Monte Carlo Simulation?
Definition
Monte Carlo simulation is a computational technique that uses repeated random sampling to model the probability distribution of outcomes in systems with uncertainty.
How it works:
1. Define variables and their probability distributions
2. Randomly sample values for each variable
3. Calculate outcome based on sampled values
4. Repeat steps 2-3 thousands of times
5. Analyze the distribution of outcomes
Why It's Called Monte Carlo
Named after the Monte Carlo Casino in Monacoβthe method relies on randomness, like casino games. But unlike gambling, Monte Carlo simulation uses randomness to reveal underlying probabilities.
Applications
Monte Carlo is used in:
β’ Finance (risk analysis, portfolio optimization)
β’ Engineering (reliability testing, design optimization)
β’ Physics (particle simulations, quantum mechanics)
β’ Climate science (weather forecasting, climate models)
β’ And now: Divination
Monte Carlo Divination: The Basic Method
Step 1: Define the Question
What outcome are you trying to predict?
Example: "What is the probability distribution of my career satisfaction 6 months from now?"
Step 2: Identify Key Variables
What variables influence the outcome?
Example variables:
β’ Current job satisfaction (internal)
β’ New opportunity availability (external)
β’ Skill development (internal)
β’ Economic conditions (external)
β’ Relationship with boss (relational)
Step 3: Assign Probability Distributions to Variables
For each variable, define its possible values and probabilities.
Example: New opportunity availability
β’ No opportunity (0): 30% probability
β’ Moderate opportunity (+5): 50% probability
β’ Excellent opportunity (+10): 20% probability
Example: Skill development
β’ No growth (0): 20% probability
β’ Moderate growth (+3): 60% probability
β’ Significant growth (+7): 20% probability
Step 4: Run Simulation (100-1000 iterations)
For each iteration:
1. Randomly sample a value for each variable based on its probability distribution
2. Calculate the outcome (e.g., career satisfaction = current + opportunity + skill + ...)
3. Record the outcome
Example iteration 1:
β’ Opportunity: +5 (sampled from distribution)
β’ Skill: +3 (sampled)
β’ Boss relationship: -2 (sampled)
β’ Economic conditions: +1 (sampled)
β’ Outcome: Current (4) + 5 + 3 - 2 + 1 = 11 (very satisfied)
Example iteration 2:
β’ Opportunity: 0 (sampled)
β’ Skill: 0 (sampled)
β’ Boss relationship: -5 (sampled)
β’ Economic conditions: -2 (sampled)
β’ Outcome: 4 + 0 + 0 - 5 - 2 = -3 (dissatisfied)
Repeat 1000 times...
Step 5: Analyze Results
After 1000 iterations, you have 1000 possible outcomes. Analyze the distribution:
Mean outcome: Average of all 1000 outcomes = Expected value
Standard deviation: Spread of outcomes = Uncertainty
Probability distribution: Histogram showing frequency of each outcome
Confidence intervals: 90% of outcomes fall between X and Y
Example results:
β’ Mean outcome: +6.2 (moderately satisfied)
β’ Standard deviation: 4.3 (moderate uncertainty)
β’ 90% confidence interval: [-1.0, +13.4]
β’ Probability of positive outcome (>5): 68%
β’ Probability of very positive outcome (>10): 22%
β’ Probability of negative outcome (<0): 12%
Monte Carlo Tarot: Practical Implementation
Method 1: Repeated Readings
Physically draw cards multiple times, record results, analyze distribution.
Process:
1. Shuffle deck thoroughly
2. Draw 3-card spread (situation-challenge-outcome)
3. Quantify outcome card (polarity -10 to +10)
4. Record outcome value
5. Reshuffle, repeat 50-100 times
6. Analyze distribution of outcome values
Example (50 readings):
Outcome card frequencies:
β’ The Sun (+9): 2 times (4%)
β’ Six of Wands (+8): 3 times (6%)
β’ Three of Wands (+7): 5 times (10%)
β’ Eight of Pentacles (+6): 7 times (14%)
β’ Six of Pentacles (+4): 8 times (16%)
β’ Two of Pentacles (0): 10 times (20%)
β’ Five of Cups (-4): 6 times (12%)
β’ Eight of Swords (-5): 5 times (10%)
β’ Five of Pentacles (-8): 3 times (6%)
β’ Ten of Swords (-10): 1 time (2%)
Analysis:
β’ Mean outcome: +2.1 (slightly positive)
β’ Most likely outcome: Two of Pentacles (0, neutral/balancing) β 20%
β’ Probability of positive outcome: 60%
β’ Probability of negative outcome: 30%
β’ Probability of neutral outcome: 10%
Method 2: Virtual Simulation
Use probability distributions without physical cards.
Process:
1. Assign probability to each card based on question context
2. Randomly sample cards according to probabilities
3. Calculate outcome
4. Repeat 1000 times
5. Analyze distribution
Example: Career question
Assign higher probability to work-related cards (Pentacles, some Wands), lower to relationship cards (Cups).
Probability distribution:
β’ Pentacles: 40% (work/material domain)
β’ Wands: 30% (action/creativity domain)
β’ Swords: 20% (mental/challenge domain)
β’ Cups: 10% (emotional domain, less relevant to career)
Run 1000 simulations, analyze results.
Monte Carlo I Ching: Hexagram Distribution
Method: Repeated Consultations
Cast I Ching 50-100 times on same question, analyze hexagram distribution.
Example (100 consultations on "Business launch success?"):
Hexagram frequencies:
β’ Hex 1 (Creative): 8 times (8%) β Very positive
β’ Hex 14 (Great Possession): 6 times (6%) β Very positive
β’ Hex 11 (Peace): 5 times (5%) β Positive
β’ Hex 3 (Difficulty at Beginning): 12 times (12%) β Challenging but normal
β’ Hex 5 (Waiting): 10 times (10%) β Neutral, patience needed
β’ Hex 12 (Standstill): 7 times (7%) β Negative
β’ Hex 29 (Abysmal): 4 times (4%) β Very negative
β’ Other hexagrams: 48 times (48%) β Mixed
Analysis:
β’ Positive hexagrams (1, 11, 14, etc.): 25%
β’ Neutral hexagrams (5, etc.): 15%
β’ Challenging but workable (3, etc.): 20%
β’ Negative hexagrams (12, 29, etc.): 15%
β’ Mixed/unclear: 25%
Interpretation: 25% probability of strong success, 20% probability of initial difficulty but eventual success, 15% probability of stagnation/failure. Overall: 45% positive, 15% neutral, 15% negative, 25% uncertain.
Advanced Technique: Variable Correlation
Some variables are correlatedβwhen one changes, another tends to change too.
Example: Confidence and Opportunity
Hypothesis: When confidence is high, you're more likely to attract/recognize opportunities.
Correlation: +0.7 (strong positive correlation)
Implementation in Monte Carlo:
1. Sample confidence value
2. If confidence is high, increase probability of high opportunity value
3. If confidence is low, increase probability of low opportunity value
4. Sample opportunity value from adjusted distribution
5. Calculate outcome
Effect: More realistic simulationβvariables don't change independently, they influence each other.
Risk Analysis: Downside Probability
Monte Carlo reveals not just expected outcome, but riskβthe probability of bad outcomes.
Value at Risk (VaR)
Question: "What's the worst outcome I might face with 95% confidence?"
Method: Find the 5th percentile of the outcome distribution.
Example:
After 1000 simulations, sort outcomes from worst to best.
5th percentile outcome = -3.2
Interpretation: There's a 5% chance the outcome will be worse than -3.2. This is your downside risk.
Probability of Failure
Question: "What's the probability the outcome is negative?"
Method: Count how many simulations resulted in outcome < 0, divide by total simulations.
Example:
120 out of 1000 simulations had negative outcomes
Probability of failure: 12%
Case Study: Relationship Decision Monte Carlo
Question: "If I stay in this relationship, what's the probability distribution of satisfaction 1 year from now?"
Variables identified:
1. Communication improvement (internal/relational)
2. Partner's commitment (external/relational)
3. External stressors (external)
4. Personal growth (internal)
5. Conflict resolution (relational)
Probability distributions assigned (based on tarot readings):
Communication improvement:
β’ Significant improvement (+5): 20%
β’ Moderate improvement (+2): 40%
β’ No change (0): 30%
β’ Worsening (-3): 10%
Partner's commitment:
β’ Fully committed (+8): 30%
β’ Moderately committed (+4): 40%
β’ Ambivalent (0): 20%
β’ Withdrawing (-5): 10%
External stressors:
β’ Low stress (+2): 30%
β’ Moderate stress (0): 50%
β’ High stress (-4): 20%
Personal growth:
β’ Significant growth (+6): 25%
β’ Moderate growth (+3): 50%
β’ Stagnation (0): 25%
Conflict resolution:
β’ Effective (+4): 30%
β’ Adequate (+1): 40%
β’ Poor (-3): 30%
Baseline satisfaction: Current = 3 (slightly positive)
Monte Carlo simulation (1000 iterations):
Results:
β’ Mean outcome: +5.8 (moderately satisfied)
β’ Standard deviation: 6.2 (high uncertainty)
β’ 90% confidence interval: [-4.2, +15.8]
β’ Probability of positive outcome (>5): 58%
β’ Probability of very positive outcome (>10): 18%
β’ Probability of negative outcome (<0): 22%
β’ Probability of very negative outcome (<-5): 8%
Interpretation:
β’ Expected outcome: Moderately positive (+5.8), better than current (+3)
β’ Uncertainty: High (SD = 6.2)βwide range of possible outcomes
β’ Upside potential: 18% chance of very positive outcome (>10)
β’ Downside risk: 22% chance of negative outcome, 8% chance of very negative
β’ Most likely scenario: Moderate improvement (58% positive probability)
Decision framework:
β’ If risk-averse: 22% failure probability may be too highβconsider leaving
β’ If risk-tolerant: 58% success probability + 18% very positive upside may be worth the riskβstay and work on it
β’ Key leverage: Communication and conflict resolution (highest sensitivity variables)βfocus here to shift probability distribution toward positive outcomes
Why Monte Carlo Changes Divination
Traditional divination: One reading, one prediction, binary outcome.
Monte Carlo divination: 1000 readings, probability distribution, confidence intervals, risk analysis, expected value, upside/downside quantification.
This transforms divination from single-point prediction into probabilistic forecastingβyou see the full range of possibilities and their likelihoods.
The old way: One reading, one answer, hope it's right. The new way: 1000 simulations, probability distribution, confidence intervals, risk analysis, expected value. From deterministic to probabilistic. From single point to distribution. From certainty to probability. This is Monte Carlo divination.
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