Smart System Dynamics Modeler

Model complex system dynamics with feedback loop analysis, stock-flow diagrams, emergent behavior prediction, and policy intervention testing.

When to Use This Command

Run this command when...

• You need to understand how feedback loops in a business ecosystem, organizational process, or market create non-obvious emergent behaviors over time
• You want to identify leverage points where small interventions produce disproportionate system-wide effects
• Your analysis involves stocks (accumulations) and flows (rates of change) that interact through reinforcing and balancing feedback loops

Do NOT use this command when...

• Your system is purely linear with no feedback effects -- a simple spreadsheet model will suffice
• You need to model individual agent behavior rather than aggregate system dynamics

Quick Start

Copy
# .claude/commands/smart-system-dynamics-modeler.md
# Model system dynamics
Model dynamics of: $ARGUMENTS

Copy
# Run the command
claude "smart-system-dynamics-modeler SaaS growth dynamics with word-of-mouth, churn, and support capacity constraints"

Expected output:
- Stock-flow diagram with labeled feedback loops
- Reinforcing and balancing loop identification
- Leverage point analysis ranked by impact
- Emergent behavior predictions over time
- Policy intervention recommendations

Core Concepts

Concept	Description
Stocks	Accumulations in the system (users, inventory, cash, reputation)
Flows	Rates that increase or decrease stocks (signups, churn, spending)
Reinforcing Loops	Feedback that amplifies change (growth begets growth)
Balancing Loops	Feedback that stabilizes the system (capacity limits growth)
Leverage Points	Locations where interventions have outsized system-wide impact

System Dynamics Structure:

  +-->[Stock A]---Flow AB--->[Stock B]--+
  |       |                      |      |
  |   [Reinforcing           [Balancing |
  |     Loop R1]              Loop B1]  |
  |       |                      |      |
  +---[Flow Back]<-----------[Limit]<---+

  Legend:
  ---> = Flow direction
  [...] = Process/feedback
  R = Reinforcing, B = Balancing

Configuration

Parameter	Default	Description
System Scope	Auto-detect	Boundaries of the system being modeled
Time Horizon	12 months	Simulation period for dynamic behavior observation
Loop Depth	2 levels	How many nested feedback loops are explicitly modeled
Intervention Count	3	Number of policy interventions tested against the model
Visualization	Stock-flow diagram	Output format: diagram, narrative, or tabular

Best Practices

1. Name your stocks explicitly -- state what accumulates in your system (users, revenue, technical debt, brand equity) so the model captures the right state variables
2. Identify known feedback loops -- describe loops you already suspect ("more users leads to more content leads to more users") to accelerate model construction
3. Specify time delays -- many system dynamics effects have delays (hiring takes months to increase capacity). Mention known delays for accurate behavior modeling
4. Look for balancing loops -- every growing system hits limits. Describe capacity constraints, market saturation, or resource depletion explicitly
5. Test interventions at leverage points -- use the model to simulate policy changes at identified leverage points rather than uniformly across the system

Common Issues

1. Model shows unrealistic exponential growth -- you are likely missing balancing loops. Add capacity constraints, market size limits, or resource depletion to the system
2. Behavior is too stable -- the model may overweight balancing feedback. Check whether reinforcing loops have the correct gain and whether delays are accurately represented
3. Too many variables to interpret -- simplify by aggregating related stocks and flows. A system dynamics model should capture structure, not every operational detail