Scientific Definition
Map reinforcing (R) and balancing (B) feedback loops between variables to see system behavior.
Plain-English Definition
Map reinforcing (R) and balancing (B) feedback loops between variables to see system behavior.
Feynman Explanation
Most 'root cause' analysis stops one arrow too early.
Core Principle
Map reinforcing (R) and balancing (B) feedback loops between variables to see system behavior.
Mechanisms
Pending editorial review.
Map reinforcing (R) and balancing (B) feedback loops between variables to see system behavior.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Why every push for short-term metrics eventually produces a balancing loop that erodes the metric.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
Most 'root cause' analysis stops one arrow too early.
Examples
- Standard tool in system dynamics, from Forrester to Sterman.
- Why every push for short-term metrics eventually produces a balancing loop that erodes the metric.
Pending editorial review.
Famous Experiments
Pending editorial review.
Design Principles
- Draw the loops before drawing the action plan. Look for delays — they're where surprises live.
Measurement Approaches
Pending editorial review.
Evidence
Pending editorial review.
Pending editorial review.
The Perverse Incentive Lens™
How this behavior is exploited — and how to redesign around it.
- Draw the loops before drawing the action plan. Look for delays — they're where surprises live.
Pending editorial review.
Pending editorial review.
Interactive Mini Network
Click any neighbor to re-center the graph and follow the threads of connection.
Knowledge Graph Neighbors
Auto-linked to the rest of the Human Behavior Taxonomy by family, domain, dimension, and shared keywords.
A system with many interacting parts that learn and adapt.
Many interacting parts producing emergent behavior nobody designed.
Interdependent network of actors evolving together.
The whole has properties that the individual parts do not.
Systems maintain stability by self-regulating around a setpoint.
Outputs of a system are routed back as inputs, amplifying or dampening change.
Stocks are accumulations; flows are rates of change that affect them.
Behavior emerges from structure, not from individuals.
Old systems decline as new ones emerge in parallel; leaders steward both, not just kill the old or chase the new.
Three-stage cycle: learn by seeing, learn by doing, learn by teaching — looped continuously.
Observe, Orient, Decide, Act — faster than your competitor.
Single-loop fixes errors; double-loop questions the assumptions producing them.