System Dynamics Modeler Skill

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

Agent Skills: System Dynamics Modeler

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