Granularity: Why Does the Same Problem Change with Scale?

Granularity is the size of the units into which we divide something for observation, description and action. It does not mean that finer is always more accurate. A coarse grain can hide decisive differences, while an excessively fine grain can make the larger relationship disappear.

Consider analysing a piano performance. We might treat it as one complete work, or divide it into phrases, movements and even individual keystrokes. If the question concerns formal coherence, keystroke data may add little. If one rapid figure repeatedly fails, talking only about the style of the whole piece is too coarse. An object has no single correct granularity; it has a granularity suited to the question.

Granularity is connected with boundaries and context: the units we draw determine the relationships that can appear. Change the scale, and a previously stable judgement may change with it. The same applies to AI summaries, learning plans and everyday decisions. Good analysis does not enlarge detail without limit. It knows when to move closer, when to step back, and whether the central judgement still holds at another grain.