Parametercentric

Parametercentric is an adjective describing an approach or perspective in which the primary units of analysis, design, or evaluation are the parameters that define a system, model, or process. Variants include parameter-centric or parametric-centric. The term is not tied to a single formal theory but denotes a stance that can be applied across disciplines.

In modeling and simulation, a parameter-centric view treats parameters as the central objects: their values, ranges, sensitivities, identifiability, and interactions guide development and interpretation. This contrasts with data-centric approaches that prioritize data quality, volume, and empirical observations, or model-centric approaches that emphasize the structure of the model itself.

In machine learning and statistics, parameter-centric discussions often focus on estimating and regularizing model parameters, setting hyperparameters, and understanding parameter space. In software engineering and systems design, parameter-centric design emphasizes interfaces and behavior governed by input parameters, enabling systematic exploration of scenarios and configurations. In testing, parameter-centric testing derives test cases from parameter variations.

Critiques of a parameter-centric stance note that overemphasis on parameters can obscure data limitations, domain context, and real-world constraints. Proponents counter that parameter analysis provides clarity about sensitivity, robustness, and reproducibility, especially when parameter spaces are explicitly documented and explored.