Information Gain — Structural Reference

Orientation

Information can change what is known about a system.

Observations, signals, evidence, or data may reduce uncertainty and increase distinguishability between possible states.

A system contains uncertainty. Information gain measures how much uncertainty is reduced.

Problem Space

Uncertainty

System states may remain indistinguishable without additional information.

Signal Relevance

Not all observations contribute equally to uncertainty reduction.

Decision Ambiguity

Actions may remain difficult to evaluate when uncertainty remains high.

System Boundary

The information boundary separates meaningful uncertainty reduction from contexts where additional information does not materially change system understanding.

Within Boundary

Information contributes to measurable uncertainty reduction.

At Boundary

New observations, signals, evidence, or data are incorporated.

Outside Boundary

No meaningful reduction of uncertainty occurs.

Structure

Context and positioning are described in About.

Formal definition, scope boundaries, and structural models are provided in Method.