264. Feedback Loops

Closed causal chains where an effect comes back to influence its cause. The engine of dynamic behavior in system dynamics.

264.1. Two types

Reinforcing loops (R, positive feedback): the loop amplifies changes. Math: with . Solution: exponential growth .

Balancing loops (B, negative feedback): the loop resists changes. Math: with . Solution: exponential approach to target with time constant .

264.2. Behavior patterns

Loop structure Behavior Example
Pure reinforcing Exponential growth/collapse Compound interest, viral spread
Pure balancing Goal-seeking to steady state Thermostat, predator-prey equilibrium
R + B (limit) S-curve (logistic) Market penetration, learning curves
Two B with delay Oscillation (damped or sustained) Inventory cycles, beer game
R + B unstable Overshoot and collapse Resource depletion

264.3. First-order behavior

Reinforcing ():

Balancing ():

264.4. Combined loops: limits to growth

Reinforcing growth () plus balancing loop tied to capacity :

Initially R dominates → exponential growth. As , the balancing factor shrinks . Asymptote: . S-curve.

See Logistic Growth.

264.5. Second-order: oscillation

A loop with delays produces oscillation. Simple example: (damped harmonic oscillator).

Damping Behavior Pattern
Sustained oscillation sine wave
Damped oscillation decaying sine
Critical damping smoothest approach
Over-damped no oscillation, slow approach

264.6. Why feedback loops matter

Most “surprising” system behavior comes from feedback:

Without feedback, a system can’t generate dynamic patterns; it’s just open-loop input → output.

264.7. Identifying loops in a system

  1. List variables and how they affect each other
  2. Trace cycles in the diagram
  3. Compute polarity of each cycle (R or B)
  4. Identify dominant loops in each behavior phase

The same model can have R-dominant phase (early growth) and B-dominant phase (saturation).

264.8. See also