Research on data structures and systems provides strong tools for analyzing static represen-tations and operation costs, but it offers less consistent vocabulary for primitives that adapt to workload, maintain internal lifecycle state, and trade steady-state benefit against recurring maintenance overhead. Bio-inspired computing has explored related questions across several tra-ditions, yet primitive-level adaptive structures still lack a shared framework for scoping claims, comparing tradeoffs, and evaluating biological analogs as engineering mechanisms rather than decorative metaphors. This paper argues for a framework for adaptive computing primitives within the Mutuus research program. It distinguishes three classes: adaptive data structures, operational resource-management policies, and inter-system coordination patterns, termed Organics, Metabolics, and Ecologics. It introduces an organic/inorganic taxonomy, a four-dimensional complexity model that extends classical efficiency analysis with adaptiveness, resilience, and thermal cost, and an evaluation methodology for determining when biologically inspired mechanisms justify their complexity. Three implemented exemplars (Nacre Array, Diatom Bitmap, and Mycelial Cache) illustrate the framework. The contribution is conceptual and methodological: this paper defines the vocabulary, scope, and evaluation criteria, while companion papers provide the formal specifications, empirical measurements, and implementation details for individual primitives.
Wm. B. Phillips (Sun,) studied this question.