Software Tools for System Dynamics Divination

BY NICOLE LAU

Paper worksheets are essential for learning and contemplation, but software tools enable complexity that paper cannot: Monte Carlo simulations with 1000 iterations, stock-flow projections over 10 years, searchable databases of hundreds of readings, and automated convergence analysis. The right software doesn't replace intuition—it amplifies it by handling calculations, visualizations, and data management, freeing you to focus on interpretation and insight.

This article provides a complete software toolkit for Dynamic Divination Modeling Theory, from free spreadsheet templates to specialized system dynamics software, designed for practitioners at all technical levels.

The DDMT Software Stack (Three Tiers)

Tier 1: Essential (Free, Beginner-Friendly)

Tools:
• Spreadsheet (Google Sheets or Excel)
• Note-taking app (Notion, Obsidian, or Evernote)
• Simple diagramming tool (Google Drawings or draw.io)

Capabilities:
• Variable tracking and quantification
• Basic stock-flow calculations
• Reading documentation and search
• Simple causal loop diagrams

Best for: Beginners, personal practice, low-complexity readings

Tier 2: Intermediate (Paid, More Powerful)

Tools:
• Advanced spreadsheet (Excel with macros or Google Sheets with scripts)
• Diagramming software (Lucidchart or Miro)
• Database (Airtable or Notion databases)
• Visualization tool (Tableau Public or Google Data Studio)

Capabilities:
• Automated calculations (sensitivity analysis, Monte Carlo)
• Professional diagrams (causal loops, stock-flow)
• Relational databases (link readings, track patterns)
• Interactive visualizations (dashboards, trend analysis)

Best for: Serious practitioners, professional use, complex multi-variable analysis

Tier 3: Advanced (Specialized, Technical)

Tools:
• System dynamics software (Vensim, Stella, or InsightMaker)
• Statistical software (R or Python with libraries)
• Custom database (PostgreSQL or MongoDB)
• Business intelligence (Power BI or Tableau)

Capabilities:
• Full system dynamics modeling (differential equations, feedback loops)
• Advanced Monte Carlo (10,000+ iterations, sensitivity analysis)
• Big data analysis (hundreds of readings, pattern recognition)
• Predictive analytics (machine learning on historical readings)

Best for: Advanced practitioners, researchers, collective/organizational divination

Tool 1: Spreadsheet Setup (Tier 1 Essential)

Google Sheets DDMT Template

Sheet 1: Variable Mapping

Columns:
• A: Date
• B: Question
• C: Variable Name
• D: Category (Internal/External/Relational/Temporal)
• E: Tarot Card / I Ching Hex / Astrology Indicator
• F: Interpretation
• G: Polarity (-10 to +10)
• H: Notes

Formulas:
• Cell G100: =AVERAGE(G2:G99) (average polarity)
• Cell G101: =COUNTIF(G2:G99,">0")/COUNTA(G2:G99) (% positive variables)

Sheet 2: Stock-Flow Tracker

Columns:
• A: Date
• B: Stock Name (e.g., "Financial Runway", "Energy Level")
• C: Current Level
• D: Inflow Rate (per month)
• E: Outflow Rate (per month)
• F: Net Flow (=D-E)
• G: Projected Level (Month 1) (=C+F)
• H: Projected Level (Month 3) (=C+F*3)
• I: Projected Level (Month 6) (=C+F*6)
• J: Projected Level (Month 12) (=C+F*12)
• K: Critical Threshold
• L: Months to Critical (=IF(F<0,(C-K)/ABS(F),"N/A"))

Sheet 3: Scenario Comparison

Rows: Criteria (Financial, Health, Relationship, Purpose, Overall Score)
Columns: Scenario A, Scenario B, Scenario C
Bottom row: =AVERAGE(scores) for each scenario

Sheet 4: Convergence Tracker

Columns:
• A: Date
• B: Question
• C: Tarot Prediction
• D: I Ching Prediction
• E: Astrology Prediction
• F: Convergence % (manual assessment: 0%, 50%, 75%, 100%)
• G: Actual Outcome (filled in later)
• H: Accuracy (manual assessment after validation)

Sheet 5: Reading Archive

Columns:
• A: Date
• B: Question
• C: Category (Career/Relationship/Health/Finance/Other)
• D: Method (Tarot/I Ching/Astrology/Multi-system)
• E: Key Insight
• F: Decision Made
• G: Validation Date
• H: Outcome
• I: Accuracy
• J: Learning

Features:
• Filter by Category (see all career readings)
• Filter by Method (see all I Ching readings)
• Sort by Accuracy (which methods work best for you?)
• Search by keyword (find all readings mentioning "burnout")

Excel Advanced Features (Tier 2)

Monte Carlo Simulation (using Data Table feature):

Setup:
• Column A: Iteration number (1-1000)
• Column B: =RANDBETWEEN(min, max) for each variable
• Column C: Outcome calculation based on random variables
• Use Data Table to run 1000 iterations automatically
• Analyze results: =AVERAGE(C:C), =STDEV(C:C), =PERCENTILE(C:C, 0.05) for 5th percentile

Sensitivity Analysis (using Scenario Manager):

Setup:
• Define baseline scenario (all variables at current values)
• Create scenarios: Variable 1 +20%, Variable 1 -20%, Variable 2 +20%, etc.
• Run Scenario Manager to compare outcomes
• Create Scenario Summary report showing impact of each variable

Tool 2: Diagramming Software (Tier 2)

Lucidchart for Causal Loop Diagrams

Template Setup:

Shapes:
• Rectangle: Variables (e.g., "Confidence", "Action", "Results")
• Arrow: Causal links
• Text on arrow: + or - (polarity)
• Circle with R or B: Loop identifier

Process:
1. Create shape library with pre-made variable boxes
2. Drag and drop to create loops
3. Add polarity (+/-) to each arrow
4. Count negative links, label loop R or B
5. Color code: Green for R+ (virtuous), Red for R- (vicious), Blue for B (balancing)
6. Export as PNG for documentation

Stock-Flow Diagrams:

Shapes:
• Rectangle: Stocks
• Arrow with valve symbol: Flows
• Cloud: Sources and sinks
• Text: Flow rates

Process:
1. Draw stock as rectangle
2. Add inflow arrows from clouds
3. Add outflow arrows to clouds
4. Add valve symbols on arrows
5. Label flow rates
6. Calculate net flow visually

Miro for Collaborative Diagramming

Features:
• Infinite canvas (map entire life system on one board)
• Real-time collaboration (work with coach, therapist, or partner)
• Templates (create DDMT template library)
• Sticky notes (brainstorm variables before formalizing)
• Voting (if doing group divination, vote on interpretations)

Use case: Couples therapy using DDMT—both partners map their perspective of relationship dynamics on shared Miro board, identify where causal loops differ, find convergence.

Tool 3: Database Systems (Tier 2-3)

Airtable for Reading Database (Tier 2)

Base Structure:

Table 1: Readings
Fields:
• Reading ID (auto-number)
• Date (date)
• Question (long text)
• Category (single select: Career, Relationship, Health, Finance, Spiritual, Other)
• Method (multiple select: Tarot, I Ching, Astrology, Stock-Flow, Causal Loops)
• Convergence % (number: 0-100)
• Decision Made (long text)
• Status (single select: Pending Validation, Validated, Archived)
• Link to Variables (linked record to Variables table)
• Link to Outcomes (linked record to Outcomes table)

Table 2: Variables
Fields:
• Variable ID (auto-number)
• Reading ID (linked record to Readings table)
• Variable Name (single line text)
• Category (single select: Internal, External, Relational, Temporal)
• Polarity (number: -10 to +10)
• Tarot Card (single line text)
• I Ching Hexagram (single line text)
• Interpretation (long text)

Table 3: Outcomes
Fields:
• Outcome ID (auto-number)
• Reading ID (linked record to Readings table)
• Validation Date (date)
• Predicted Outcome (long text)
• Actual Outcome (long text)
• Accuracy (single select: Exact, Close, Partial, Inaccurate)
• Learning (long text)

Views:
• All Readings (default)
• Pending Validation (filter: Status = Pending Validation)
• Career Readings (filter: Category = Career)
• High Convergence (filter: Convergence % > 75)
• Accurate Predictions (filter: Accuracy = Exact or Close)
• Calendar View (group by Date)

Automations:
• When Reading created → Send email reminder to validate in 3 months
• When Outcome added → Calculate accuracy rate across all readings
• When Convergence % > 90 → Tag as "High Confidence"

Notion for Integrated Knowledge Base (Tier 2)

Database Structure:

Databases:
• Readings (same structure as Airtable)
• Variables (linked to Readings)
• Outcomes (linked to Readings)
• Insights (standalone, can link to multiple Readings)
• Resources (books, articles, courses on DDMT)

Unique Notion Features:
• Inline databases (embed Variables table inside Reading page)
• Linked databases (show same Reading in multiple views: by Category, by Date, by Accuracy)
• Templates (create Reading Template with pre-filled structure)
• Relations (link Readings to Insights: "This reading taught me X")
• Rollups (calculate average accuracy across all Career readings)

Example Workflow:
1. Create new Reading from template
2. Fill in Question, Date, Method
3. Add Variables (inline database auto-creates Variable records)
4. Add interpretation and decision
5. Set reminder for validation (3 months)
6. When validating, add Outcome record
7. Link to Insights page if new pattern discovered

Tool 4: System Dynamics Software (Tier 3)

InsightMaker (Free, Web-Based)

Capabilities:
• Stock-flow modeling with visual interface
• Differential equations (automatic, no coding)
• Simulation over time (run model for 10 years, see trajectory)
• Sensitivity analysis (built-in)
• Scenario comparison (save multiple model versions)

DDMT Use Case: Financial Planning

Model:
• Stock: Net Worth
• Inflow: Income (variable: can change over time)
• Outflow: Expenses (variable)
• Additional stock: Investment Portfolio
• Flow: Savings → Investment Portfolio
• Flow: Investment Returns → Net Worth
• Variables: Income growth rate, expense inflation, investment return rate

Simulation:
• Run for 30 years
• See Net Worth trajectory
• Test scenarios: What if income grows 5% vs. 10%? What if investment returns are 7% vs. 9%?
• Sensitivity analysis: Which variable has biggest impact on retirement readiness?

Export:
• Graph of Net Worth over time
• Data table (Net Worth at each year)
• Sensitivity chart (tornado diagram showing variable impacts)

Vensim (Professional, Paid)

Advanced Features:
• Subscripts (model multiple stocks simultaneously: Net Worth for Person A, Person B, Person C)
• Optimization (find optimal values for variables to achieve goal)
• Monte Carlo (built-in, 1000+ iterations)
• Calibration (fit model to historical data)

DDMT Use Case: Organizational Change

Model:
• Stocks: Employee Morale, Productivity, Revenue, Customer Satisfaction
• Flows: Hiring, Attrition, Sales, Churn
• Feedback loops: Morale → Productivity → Revenue → Hiring → Morale (R+)
• Delays: Hiring takes 3 months to impact productivity

Analysis:
• Calibrate model to company's historical data (past 2 years)
• Run Monte Carlo with uncertainty in hiring rate, market conditions
• Optimize: What hiring rate maximizes revenue while maintaining morale?
• Scenario: What if we implement 4-day workweek? (increase morale, decrease productivity hours, net effect?)

Tool 5: Statistical Software (Tier 3)

R for Advanced Analysis

Libraries:
• dplyr: Data manipulation
• ggplot2: Visualization
• forecast: Time series analysis
• sensitivity: Sensitivity analysis
• mc2d: Monte Carlo simulation

DDMT Use Case: Pattern Recognition Across 100+ Readings

Analysis:
• Load reading database (CSV export from Airtable)
• Calculate: Which variables appear most frequently in accurate predictions?
• Correlation: Do high-convergence readings predict better outcomes?
• Time series: Is my prediction accuracy improving over time?
• Clustering: Do my readings fall into distinct categories (career vs. relationship patterns)?

Code example (simplified):
```r
library(dplyr)
library(ggplot2)

# Load data
readings <- read.csv("readings.csv")

# Calculate accuracy by method
accuracy_by_method <- readings %>%
group_by(Method) %>%
summarize(avg_accuracy = mean(Accuracy))

# Visualize
ggplot(accuracy_by_method, aes(x=Method, y=avg_accuracy)) +
geom_bar(stat="identity") +
labs(title="Prediction Accuracy by Divination Method")
```

Python for Automation

Libraries:
• pandas: Data analysis
• numpy: Numerical computing
• matplotlib/seaborn: Visualization
• scipy: Statistical functions
• scikit-learn: Machine learning

DDMT Use Case: Automated Convergence Analysis

Script:
• Input: Tarot reading, I Ching reading, Astrology reading (text)
• NLP analysis: Extract key themes from each
• Similarity calculation: How similar are the themes? (0-100%)
• Output: Convergence percentage (automated, not manual)

Code example (simplified):
```python
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# Readings
tarot = "The Tower indicates breakdown, Death shows transformation"
iching = "Hexagram 23 Splitting Apart, transformation through crisis"
astro = "Pluto transit: breakdown and rebirth"

# Vectorize
vectorizer = TfidfVectorizer()
vectors = vectorizer.fit_transform([tarot, iching, astro])

# Calculate similarity
similarity = cosine_similarity(vectors)
convergence = similarity.mean() * 100

print(f"Convergence: {convergence:.0f}%")
# Output: Convergence: 78%
```

Tool 6: Visualization & Dashboards (Tier 2-3)

Tableau Public (Free) for Interactive Dashboards

Dashboard Components:

Panel 1: Reading Volume Over Time
• Line chart: Number of readings per month
• Filter by Category (Career, Relationship, etc.)

Panel 2: Accuracy Trends
• Line chart: Average accuracy over time
• Shows if prediction skill improving

Panel 3: Convergence vs. Accuracy
• Scatter plot: X-axis = Convergence %, Y-axis = Accuracy
• Hypothesis: Higher convergence → Higher accuracy?

Panel 4: Method Comparison
• Bar chart: Average accuracy by method (Tarot, I Ching, Astrology, Multi-system)
• Shows which methods work best for you

Panel 5: Variable Frequency
• Word cloud or bar chart: Most common variables across all readings
• Reveals recurring themes in your life

Interactivity:
• Click on a month → See all readings from that month
• Filter by Category → Dashboard updates to show only Career readings
• Hover over data point → See reading details

Google Data Studio (Free) for Simple Dashboards

Data Source: Google Sheets (Reading Archive)
Charts:
• Scorecard: Total readings, Average accuracy, High convergence count
• Time series: Readings per month
• Pie chart: Readings by category
• Table: Recent readings with validation status

Sharing:
• Generate shareable link (if working with coach/therapist)
• Embed in website (if teaching DDMT)
• Schedule email reports (monthly summary of readings)

Software Selection Guide

For Beginners (Just Starting DDMT)

Recommended Stack:
• Google Sheets (free, easy, sufficient for 90% of needs)
• Google Drawings (free, simple diagrams)
• Notion (free tier, reading archive + notes)

Cost: $0
Learning curve: Low (1-2 hours to set up)
Capabilities: Variable tracking, basic stock-flow, reading archive, simple diagrams

For Serious Practitioners (Daily DDMT Practice)

Recommended Stack:
• Excel or Google Sheets with advanced formulas
• Lucidchart ($8/month, professional diagrams)
• Airtable ($10/month, powerful database)
• Tableau Public (free, dashboards)

Cost: ~$20/month
Learning curve: Medium (1-2 weeks to master)
Capabilities: Monte Carlo, sensitivity analysis, relational database, interactive dashboards, professional diagrams

For Professionals (Coaching, Consulting, Research)

Recommended Stack:
• Vensim or InsightMaker (system dynamics modeling)
• R or Python (advanced analysis, automation)
• PostgreSQL (large-scale database)
• Tableau or Power BI (enterprise dashboards)

Cost: $50-200/month (depending on tools)
Learning curve: High (1-3 months to master)
Capabilities: Full system dynamics modeling, big data analysis, machine learning, automation, enterprise-grade dashboards

Key Software Tool Learnings

1. Start simple, add complexity as needed
Google Sheets is sufficient for 90% of practitioners. Don't over-engineer.

2. Automation saves time, enables depth
Monte Carlo with 1000 iterations by hand = impossible. In Excel = 5 minutes. Software enables analysis that paper cannot.

3. Databases enable pattern recognition
After 100 readings, patterns emerge ("I'm always too optimistic about timing"). Database makes this visible.

4. Visualization clarifies complexity
Stock-flow graph over 10 years shows trajectory at a glance. Numbers in table = hard to interpret. Graph = instant insight.

5. The tool should serve the practice, not vice versa
If software feels like bureaucracy, you're using the wrong tool or over-complicating. Tools should make DDMT easier, not harder.

Software tools transform DDMT from manual to scalable, from simple to sophisticated, from individual to collaborative. From paper worksheets to interactive dashboards, from single readings to pattern recognition across hundreds. This is how you amplify divination with technology.