Adaptive System
Updated 2026-08-13
INTRODUCTION
English translation pending.
CORE DEFINITION
An adaptive system is one that adjusts its own behavior, parameters, or structure according to changes in its environment or feedback from its own operation, and so can learn and evolve. The core proposition is that flexibility answers uncertainty: a sense-compare-act control loop keeps the system on target while conditions shift, whether the target is a temperature, a difficulty level, or a strategic goal. The key qualification is that adaptation supplies no direction of its own, so it can optimize a local optimum or the noise itself.
SCAFFOLDING EFFECT
Reduce cognitive load
- Goal and indicator definition: state the target state and the measurable indicators before adapting anything. - Feedback channel: build the sensor and comparison path that makes deviation visible. - Adaptation rate: set how fast the system reacts so it neither oscillates nor lags.
Anchor fast decisions
The system senses its own state or the environment, compares the reading against the goal, and acts on the difference, which is the negative feedback loop that holds a target despite disturbance. Learning can operate at several levels, from parameter tuning to structural reconfiguration, and the goal or fitness landscape sets the direction; the loop itself supplies no judgment about whether that goal is right.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Adaptive_systemverified
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