ReddRadar
Agent skill
system-dynamics-modeler
System dynamics modeling skill for feedback loop analysis, stock-flow diagrams, and dynamic simulation
Install this agent skill to your Project
npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/business/decision-intelligence/skills/system-dynamics-modeler
Metadata
Additional technical details for this skill
- domain
- business
- category
- simulation
- priority
- lower
- specialization
- decision-intelligence
- tools libraries
-
[ "pysd", "BPTK-Py", "simdynamics" ]
SKILL.md
System Dynamics Modeler
Overview
The System Dynamics Modeler skill provides capabilities for building and analyzing system dynamics models to understand complex systems with feedback loops, delays, and non-linear behaviors. It supports causal loop diagramming, stock-flow modeling, and policy testing for strategic decision support.
Capabilities
- Stock and flow model construction
- Causal loop diagram creation
- Feedback loop identification
- Simulation execution
- Policy testing and comparison
- Equilibrium analysis
- Sensitivity to initial conditions
- Model validation tests
Used By Processes
- Strategic Scenario Development
- What-If Analysis Framework
- War Gaming and Competitive Response Modeling
Usage
Causal Loop Diagram
# Define causal relationships
causal_loops = {
"variables": [
"Market Share", "Revenue", "R&D Investment",
"Product Quality", "Customer Satisfaction", "Word of Mouth"
],
"links": [
{"from": "Market Share", "to": "Revenue", "polarity": "+"},
{"from": "Revenue", "to": "R&D Investment", "polarity": "+"},
{"from": "R&D Investment", "to": "Product Quality", "polarity": "+", "delay": True},
{"from": "Product Quality", "to": "Customer Satisfaction", "polarity": "+"},
{"from": "Customer Satisfaction", "to": "Word of Mouth", "polarity": "+"},
{"from": "Word of Mouth", "to": "Market Share", "polarity": "+"}
],
"loops": [
{"name": "Growth Engine", "type": "reinforcing", "variables": ["Market Share", "Revenue", "R&D Investment", "Product Quality", "Customer Satisfaction", "Word of Mouth"]}
]
}
Stock and Flow Model
# Define stock-flow structure
model = {
"stocks": {
"Customers": {
"initial_value": 1000,
"inflows": ["customer_acquisition"],
"outflows": ["customer_churn"]
},
"Brand_Awareness": {
"initial_value": 0.1,
"inflows": ["marketing_effect"],
"outflows": ["awareness_decay"]
}
},
"flows": {
"customer_acquisition": "potential_customers * conversion_rate * Brand_Awareness",
"customer_churn": "Customers * churn_rate",
"marketing_effect": "marketing_spend * effectiveness / market_size",
"awareness_decay": "Brand_Awareness * decay_rate"
},
"auxiliaries": {
"potential_customers": "market_size - Customers",
"conversion_rate": "base_conversion * (1 + product_quality_factor)"
},
"constants": {
"market_size": 100000,
"base_conversion": 0.05,
"churn_rate": 0.02,
"decay_rate": 0.1,
"effectiveness": 0.001
}
}
Simulation Configuration
# Simulation settings
simulation_config = {
"time_settings": {
"initial_time": 0,
"final_time": 120, # months
"time_step": 1,
"save_interval": 1
},
"integration_method": "euler|rk4",
"scenarios": [
{"name": "Base Case", "parameters": {}},
{"name": "High Marketing", "parameters": {"marketing_spend": 50000}},
{"name": "Low Churn", "parameters": {"churn_rate": 0.01}}
]
}
Feedback Loop Types
| Type | Behavior | Example |
|---|---|---|
| Reinforcing (R) | Exponential growth/decline | Sales -> Revenue -> Marketing -> Sales |
| Balancing (B) | Goal-seeking, oscillation | Inventory -> Orders -> Production -> Inventory |
Input Schema
{
"model_type": "causal_loop|stock_flow",
"model_definition": {
"stocks": "object",
"flows": "object",
"auxiliaries": "object",
"constants": "object",
"causal_links": ["object"]
},
"simulation_config": {
"initial_time": "number",
"final_time": "number",
"time_step": "number",
"scenarios": ["object"]
},
"analysis_options": {
"equilibrium_analysis": "boolean",
"sensitivity_analysis": "boolean",
"loop_analysis": "boolean"
}
}
Output Schema
{
"simulation_results": {
"time": ["number"],
"variables": {
"variable_name": ["number"]
}
},
"scenario_comparison": {
"scenario_name": {
"final_values": "object",
"peak_values": "object",
"time_to_equilibrium": "number"
}
},
"feedback_loops": [
{
"name": "string",
"type": "reinforcing|balancing",
"variables": ["string"],
"dominance_periods": ["object"]
}
],
"equilibrium_analysis": {
"stable_points": ["object"],
"unstable_points": ["object"]
},
"visualization_paths": ["string"]
}
Best Practices
- Start with causal loop diagrams to understand structure
- Identify dominant feedback loops for each behavior mode
- Use dimensional analysis to validate equations
- Test model against historical data when available
- Perform extreme condition tests (zero, very high values)
- Document model boundary and assumptions
- Use sensitivity analysis to identify leverage points
Policy Analysis
The skill supports policy testing:
- Compare scenarios with different interventions
- Identify unintended consequences
- Test timing and magnitude of interventions
- Analyze policy resistance (counterintuitive behavior)
Integration Points
- Feeds into Scenario Narrative Generator for storylines
- Connects with Agent-Based Simulator for hybrid models
- Supports Sensitivity Analyzer for leverage point identification
- Integrates with Decision Visualization for time series plots
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