nonredundancy

Nonredundancy is the property of a system in which information or components do not duplicate one another in a way that adds no new information. In general, it means that each element contributes unique content, and repeated data or tautological elements are minimized or eliminated. Nonredundancy is pursued in multiple disciplines, including information theory, logic, database design, and knowledge representation.

In information theory and encoding, redundancy refers to extra bits or symbols used for error detection, correction, or resilience. A nonredundant representation seeks a minimal description length where the information content cannot be reduced further without loss. In practice, this involves compression, efficient coding, or data structures that avoid unnecessary duplication.

In databases and data modeling, nonredundancy is closely associated with normalization. By storing a fact in a single place and linking related data via keys, normalization reduces update anomalies and storage waste, though it may require more complex queries or slower access.

In logic and knowledge representation, nonredundancy applies to axioms, premises, and inference rules. A nonredundant theory contains statements that each contribute at least one new consequence; redundant premises can be removed without altering the set of derivable conclusions.

In practice, achieving nonredundancy supports data quality, maintainability, and interpretability. However, it may clash with performance considerations, human readability, or domain constraints that favor denormalization or repetition for speed or clarity.

Assessing nonredundancy involves examining information content, dependencies, and duplication across representations, and balancing theoretical minimality against practical requirements.