Decomplexification

Decomplexification is the deliberate process of reducing the apparent or actual complexity of a system, model, or process. It can refer to simplifying structures, rules, interfaces, or representations to improve understandability, reliability, or efficiency, while preserving essential behavior. The term is used across disciplines rather than representing a single formal theory.

In software and engineering, decomplexification involves modularization, removal of unnecessary features, and interface simplification. In data analysis and modeling, it refers to model reduction, feature selection, or abstraction that reduces dimensionality or parameter count without significantly distorting predictions. In user experience design, it addresses cognitive load by presenting information progressively and using clear metaphors.

Examples include refactoring a codebase to fewer dependencies, replacing a dense dashboard with a focused summary, or transforming a complex rule set into a concise decision tree. In mathematics or physics, decomplexification may mean replacing a complex formulation with a real-valued surrogate or lower-order approximation.

Limitations: oversimplification can obscure important dynamics, reduce flexibility, or hide trade-offs. The goal is to balance simplicity with fidelity.

See also: simplification, model reduction, cognitive load theory, abstraction.