Drift Is Not Exciting / 漂移不刺激

Author: Dexin Kong
ORCID: https://orcid.org/0009-0008-3831-5725
Structured and refined with assistance from ChatGPT
AI Automatic Translation (Unreviewed)

Background

When people hear the word “drift” in everyday life, many immediately think of the iconic scenes from the anime Initial D, where the AE86 swings through sharp mountain turns in a stylish sideways slide.

It is an extremely sophisticated driving technique, requiring highly precise control over speed, weight transfer, steering, and tire grip.

Scenes like this were enough to send viewers’ adrenaline surging.

As a result, the AE86 from “Fujiwara Tofu Shop” gradually became a shared memory for an entire generation.

But in science and engineering, “drift” refers to something completely different.

It is not exciting.

Most of the time, it is merely a tiny deviation that appears insignificant.

For example:

• Sensor drift in measurement systems
• Clock drift in timing systems
• System drift in control engineering
• Market drift in financial models
• Model drift in machine learning

Although different fields define “drift” differently, they often describe a set of remarkably similar characteristics:

As time passes, certain internal states, parameters, judgments, or behavioral patterns within a system gradually begin to deviate from their original positions, goals, or reference baselines.

This deviation is usually not sudden.

Instead, it resembles a long-term, slow, continuously accumulated process of change.

Therefore, from the perspective of observing long-running systems, “drift” is better understood as:

the process or state in which a long-running system gradually deviates from its original goals, boundaries, constraints, or reality anchors during continuous operation, adaptation, and feedback.

Related Research

American scholar Diane Vaughan observed a particularly unusual phenomenon while studying the Space Shuttle Challenger disaster.

Many major accidents do not occur suddenly.

On the contrary, before the disaster actually happens, the system often appears to have been operating “normally” for a very long time.

Some originally abnormal phenomena, because they do not immediately produce severe consequences, gradually begin to be accepted within the system itself.

One exception. Two exceptions. Ten exceptions.

Slowly, people begin adapting to these deviations.

Things that were once considered “abnormal” gradually start to feel “normal.”

Eventually, the deviation itself becomes part of the system’s new operational foundation.

Discussion

People often instinctively assume that “drift” is caused by system “errors.”

This understanding is not entirely accurate.

In most cases, the causes of “drift” can be simplified into two situations:

“seeing it wrong” and “not seeing it.”

In the discussion 《Illusion / 错觉》, we mentioned that:

reality is processed by perceptual systems and transformed into internal “projections,” and systems continuously interpret reality and adjust their behavior based on these projections.

But projections are not reality itself.

They naturally contain limitations.

Small inaccuracies are almost inevitable, and sometimes these inaccuracies can cause the system to develop “illusions.”

That is “seeing it wrong.”

In the discussion 《It Just Wouldn’t Parse / 整不会了》, we also discussed another phenomenon:

over long periods of evolution, systems gradually develop filtering mechanisms.

Only projections that satisfy certain conditions are allowed to enter the system.

As a result, projections that fail to meet these conditions are actively ignored.

That is “not seeing it.”

As systems continue operating, every projection cycle may introduce tiny deviations, and these deviations gradually accumulate over time.

“Drift” is precisely this kind of slow, continuous process of accumulated deviation.

So, should “drift” itself be considered an “error”?

Strictly speaking, drift is often a cause of many errors, but the reverse is not necessarily true.

It emerges from the combined effect of:

• the inherent limitations of projection formation within perceptual systems
• and the long-term filtering mechanisms gradually formed by the system itself

Therefore, although drift is difficult to completely avoid, it does not necessarily imply inevitable loss of control.

Note:
This project is an ongoing independent research effort developed in spare time.
Some concepts and terminology are still evolving and may continue to change over time.
Due to limited time and resources, parts of the English translation may contain semantic deviations from the original Chinese version.
The Chinese version remains the primary reference for meaning and interpretation.