CatColab Causal Loop Diagrams - systems dynamics modeling with reinforcing (R) and balancing (B) feedback loops, delays, and Lotka-Volterra semantics for strategic analysis.
Trit: 0 (ERGODIC - coordinator/mediator) Color: Yellow (#FFD700)
Causal Loop Diagrams (CLDs) in CatColab model feedback systems:
CLDs are essential for understanding system behavior, policy analysis, and strategic planning.
A causal loop diagram is a signed directed graph with loop classification:
┌─────────────────────────────────────────────────────┐
│ CAUSAL LOOP DIAGRAM │
├─────────────────────────────────────────────────────┤
│ Variables: │
│ Population, Resources, Pollution, Quality │
│ │
│ Positive Links (+): │
│ Population ──(+)──► Pollution │
│ Resources ──(+)──► Quality │
│ │
│ Negative Links (-): │
│ Pollution ──(-)──► Quality │
│ Quality ──(-)──► Population (emigration) │
│ │
│ Loops: │
│ R1: Population→Births→Population (reinforcing) │
│ B1: Population→Resources→Quality→Pop (balancing) │
└─────────────────────────────────────────────────────┘
Reinforcing Loop (R): Even number of negative links
Balancing Loop (B): Odd number of negative links
REINFORCING (R): BALANCING (B):
A ──(+)──► B A ──(+)──► B
▲ │ ▲ │
│ │ │ │
└──(+)─────┘ └──(-)─────┘
(exponential) (equilibrium)
// Causal loop double theory with decorated edges
pub fn th_causal_loop() -> DiscreteDblTheory {
let mut cat = FpCategory::new();
// Object type
cat.add_ob_generator(name("Variable"));
// Morphism types (polarized links)
cat.add_mor_generator(name("Positive"), name("Variable"), name("Variable"));
cat.add_mor_generator(name("Negative"), name("Variable"), name("Variable"));
// Decorations (CatColab 0.2)
cat.add_mor_generator(name("Delay"), name("Variable"), name("Variable"));
cat.add_mor_generator(name("Indeterminate"), name("Variable"), name("Variable"));
cat.into()
}
{
"type": "ObDecl",
"name": "MarketShare",
"theory_type": "Variable",
"description": "company's percentage of total market"
}
{
"type": "MorDecl",
"name": "growth_effect",
"dom": "MarketShare",
"cod": "Revenue",
"theory_type": "Positive",
"description": "higher market share increases revenue"
}
{
"type": "MorDecl",
"name": "saturation_effect",
"dom": "MarketShare",
"cod": "GrowthRate",
"theory_type": "Negative",
"description": "higher share reduces growth potential"
}
{
"type": "MorDecl",
"name": "investment_lag",
"dom": "RnD_Spending",
"cod": "ProductQuality",
"theory_type": "Delay",
"delay_time": 12, // months
"description": "R&D takes time to improve products"
}
CatColab generates Lotka-Volterra ODEs from causal loops:
For variables X, Y with positive link X→Y:
dY/dt = α·X·Y
For negative link X→Y:
dY/dt = -β·X·Y
General form:
dXᵢ/dt = Xᵢ · Σⱼ aᵢⱼ·Xⱼ
Word of Mouth
↗ (+)
Users ────────► Adoption Rate
▲ │
│ │
└────(+)──────────┘
R1: Viral Growth
Adoption Rate ──(+)──► Users
│
└──(-)──► Potential Users
│
B1: Market Saturation
Desired Temp ──(+)──► Gap
▲ │
│ │
│ (+)
│ ▼
Actual Temp ◄──(+)── Heating
│
└──(-)──► Gap
B1: Temperature Control
Country A Arms ──(+)──► Country A Threat Perception
▲ │
│ (+)
│ ▼
Country B Arms ◄──(+)── Country B Arms Spending
│
└──(+)──► Country A Threat Perception
R1: Escalation Spiral
catcolab-regulatory-networks (-1) ⊗ catcolab-causal-loop (0) ⊗ catcolab-stock-flow (+1) = 0 ✓
open-games (-1) ⊗ catcolab-causal-loop (0) ⊗ dynamical-system-functor (+1) = 0 ✓
# Create causal loop diagram
just catcolab-new causal-loop "market-dynamics"
# Identify all loops
just catcolab-analyze market-dynamics --loops
# Simulate Lotka-Volterra
just catcolab-simulate market-dynamics --lotka-volterra
# Export to Vensim format
just catcolab-export market-dynamics --format=mdl
Skill Name: catcolab-causal-loop Type: Systems Dynamics / Feedback Analysis Trit: 0 (ERGODIC) GF(3): Conserved via triadic composition