Matrixar

Matrixar is a term used in speculative mathematics and fiction to denote a generalized matrix-like structure that incorporates multiple layers, or strata, of data. A matrixar is defined as an array of elements arranged in a grid, with an additional stratification attribute that labels each entry by one of several levels. This stratification enables operations that vary across layers and can encode context or dependency information.

Algebraic structure: Most presentations define two binary operations: addition and multiplication. Matrixar addition is performed elementwise across corresponding entries and strata, mirroring ordinary matrix addition. Matrixar multiplication generalizes the standard matrix product by combining entries using a stratification-aware rule, which may involve composing strata according to a compatibility map. For certain choices of base data and stratification rules, matrixars satisfy properties analogous to those of rings and algebras, such as associativity of multiplication and distributivity over addition. In some models, a matrixar possesses an identity element and inverses, while in others inverses exist only for restricted classes.

Examples and variants: A simple two-stratum matrixar of size 2×2 might have a traditional 2×2 grid of first-stratum entries with a second layer of the same shape attached; multiplication combines corresponding blocks. Diagonal matrixars are defined by having nonzero elements only in the diagonal strata. The concept also extends to higher-dimensional arrays and to non-square shapes.

Uses and significance: In fiction, matrixars serve as a narrative device to represent layered simulations, neural-like networks, or multi-channel data processing. In thought experiments, they illustrate how additional structure can affect linear transformations and system behavior beyond conventional matrices. They are not part of standard mathematical curricula but appear in discussions about generalized linear algebra and computational models.

See also Matrix, Ring, Algebra, Tensor, Block matrix.