Understanding Causal Loop Diagrams

Introduction

• Presenter: Dr. Donna Gurule
• Purpose: To help understand causal loop diagrams and their components.

Importance of Causal Loop Diagrams

• Clarify mental models
• Make thinking clearer
• Identify common archetypes driving system behavior
• Share and modify mental models with others
• Facilitate rich dialogue

Components of Causal Loop Diagrams

• Variables: Things, actions, or feelings
• Causal Links/Arrows: With polarities (positive or negative)
• Delays: Represented as double yellow lines or double lines
• Feedback Loops: Positive and negative feedback loops describing cause and effect

Breakdown of Diagram Parts

Population Change

• Factors: Births and deaths
  • More births ⟶ Increased population (Positive polarity)
  • Fewer births ⟶ Decreased population (Positive polarity)
  • More deaths ⟶ Decreased population (Negative polarity)
  • Fewer deaths ⟶ Increased population (Negative polarity)

Feedback Loops

• Reinforcing Loop (R): More births ⟶ population increase ⟶ more births (positive feedback)
• Balancing Loop (B): More deaths ⟶ population decrease ⟶ fewer deaths (negative feedback)

Delays in Feedback Loops

• Example: Birth rate delays due to maturation age
  • Longer delays in developed countries (e.g., New Zealand)
  • Shorter delays in developing countries

Additional Examples of Feedback Loops

Bank Account

• Reinforcing Loop: Adding money ⟶ earns interest ⟶ more money (positive feedback)

Body Temperature Regulation

• Balancing Loop: Increase temperature ⟶ sweating ⟶ cooling down (negative feedback)
• Balancing Loop: Decrease temperature ⟶ shivering ⟶ warming up (negative feedback)

Technology Adoption

• Reinforcing Loop: More sales ⟶ more word-of-mouth ⟶ more sales (positive feedback)
• Balancing Loop: Market saturation ⟶ decreased sales ⟶ fewer word-of-mouth (negative feedback)

Application to Developing Countries

• Resource Constraints and Life Expectancy: Population ⟶ fewer resources ⟶ lower life expectancy ⟶ increased death rate ⟶ population decrease (balancing loop)
• Family Size and Infant Mortality: Lower life expectancy ⟶ larger families ⟶ increased population ⟶ higher resource constraints ⟶ lower life expectancy (reinforcing loop)

Context Dependence

• Models represent specific contexts; not universally applicable
• Useful for framing problems and suggesting improvements

Pop Quiz Examples

Traffic Model

• More traffic density ⟶ decreased speed (inverse relationship)

Racial Tension

• Increased inequality ⟶ more conflict (direct relationship)

Greenhouse Gas Emissions

• More GHG ⟶ higher temperature ⟶ more thawing matter ⟶ more GHG (reinforcing loop)

Grass-Cattle Interaction

• Loop 4: More grass ⟶ more seeding ⟶ more growth (reinforcing loop)
• Loop 5: More grass ⟶ less erosion ⟶ more growth (reinforcing loop)
• Loop 6: More grass ⟶ fewer cattle deaths ⟶ more grazing ⟶ less grass (balancing loop)

Conclusion

• Understanding causal loop diagrams helps describe system behavior.