Scientific Definition
Systems maintain stability by self-regulating around a setpoint.
Plain-English Definition
Systems maintain stability by self-regulating around a setpoint.
Feynman Explanation
Push the system; it pushes back to its setpoint.
Core Principle
Systems maintain stability by self-regulating around a setpoint.
Mechanisms
Pending editorial review.
Systems maintain stability by self-regulating around a setpoint.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Pending editorial review.
Lasting change requires moving the setpoint, not just the current state.
Inputs (Triggers)
Pending editorial review.
Outputs (Behaviors)
Pending editorial review.
Behavioral Signature
Push the system; it pushes back to its setpoint.
Examples
- Process improvements that get absorbed by reverted behavior.
- Lasting change requires moving the setpoint, not just the current state.
Pending editorial review.
Famous Experiments
Pending editorial review.
Design Principles
- Identify the homeostatic mechanism before changing inputs.
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.
- Identify the homeostatic mechanism before changing inputs.
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.
Map reinforcing (R) and balancing (B) feedback loops between variables to see system behavior.
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.
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.
Subjects often converge to neoclassical predictions only after many rounds of feedback — not on the first try.
A state where no player benefits from changing strategy unilaterally.
A behavioral relaxation of Nash: players choose better strategies more often, but not always — errors are smooth, not binary.