Drift Propagation Between Systems
Abstract
Control drift does not remain confined to a single system when coupling is present. This monograph defines Drift Propagation Between Systems (DPBS) as the process through which gradual control changes in one system spread across coupled systems through signal exchange, feedback loops, and shared normalization.
We show that drift is not isolated. It becomes distributed, allowing small changes in one system to reshape entire networks over time.
1. From Local Drift to Distributed Drift
In isolated systems:
- drift remains internal
In coupled systems:
Drift travels.
Changes in one system:
- influence others
- reshape shared dynamics
2. Defining Drift Propagation
Drift Propagation Between Systems (DPBS) is defined as:
The transmission and accumulation of control drift from one system to others through coupling, resulting in distributed changes across multiple systems.
Propagation involves:
- signal transmission
- feedback reinforcement
- normalization
3. Mechanism of Drift Propagation
Drift spreads through:
3.1 Signal Transfer
Changes in one system:
- alter outgoing signals
These signals:
- enter other systems
- influence their control
3.2 Feedback Reinforcement
Receiving systems:
- respond to altered signals
- send modified feedback
This creates:
- recursive propagation
3.3 Coupled Normalization
As drift spreads:
- it becomes normalized
- across multiple systems
4. Gradual Expansion of Drift
Propagation is:
- incremental
- cumulative
Small changes:
- expand over time
- across systems
5. Types of Drift Propagation
5.1 Linear Propagation
Drift spreads:
- sequentially
- from one system to another
5.2 Network Propagation
Drift spreads:
- across multiple systems simultaneously
5.3 Feedback-Amplified Propagation
Drift is:
- reinforced through loops
- amplified across cycles
6. Propagation Without Awareness
Systems:
- do not detect incoming drift
- do not recognize influence
Drift appears:
- as normal adjustment
7. Interaction With Interference
Interference:
- modifies propagation paths
- alters drift intensity
This leads to:
- uneven distribution
8. Interaction With Amplification
Amplification:
- accelerates propagation
- increases impact
Reinforced drift:
- spreads faster
9. Stabilization of Propagated Drift
Once widespread:
- drift becomes baseline
- across systems
Stabilization:
- masks origin
10. Loss of Origin Traceability
As drift propagates:
- original source becomes unclear
- attribution becomes difficult
Drift appears:
- system-wide
11. Substrate Independence
Drift propagation appears in:
- human cognition
- machine learning systems
- distributed networks
- organizational systems
The invariant lies in:
- connected control dynamics
12. Modeling Implications
Models must include:
- multi-system drift
- propagation pathways
- feedback reinforcement
Ignoring propagation leads to:
- incomplete system analysis
13. Structural Consequence
Drift propagation transforms:
- local change → global change
Systems become:
- collectively reshaped
14. Closing Statement
In coupled systems, drift does not stay where it begins.
It spreads.
Through signals, feedback, and normalization, small changes in one system can reshape entire networks, creating distributed evolution across interconnected control systems.