Sources of Randomness

Unpredictability can reflect event variability, sampling, measurement limits, incomplete models, changing conditions, or sensitive deterministic dynamics. These sources overlap and need evidence to distinguish.

Unpredictability can arise from variable events, limited samples, incomplete measurements, misspecified models, or deterministic dynamics that amplify small initial differences. These categories can overlap, and their boundaries depend on what is known and what outcome is being predicted. An unexplained residual is not evidence of fundamental randomness.

Choose an investigation that could distinguish relevant explanations. More observations may reduce sampling uncertainty but not repair a biased sample. Better initial measurements may extend useful forecasts in a sensitive system without making long-range prediction reliable. The aim is to connect a plausible uncertainty mechanism to a proportionate response, not to assign every surprise one permanent label.

When to use it

When unpredictability needs to be diagnosed by source for appropriate response; when distinguishing between irreducible randomness and reducible uncertainty; when strategy needs to match the type of randomness being faced; when understanding why some unpredictability can be reduced and some can't.

How it can help

Identify which uncertainties matter to the decision, then choose information, modeling, or contingency measures suited to their plausible sources. Retain uncertainty that the investigation does not resolve.