simplicityparsimony

Simplicityparsimony is a heuristic used in philosophy of science, statistics, and related fields that selects theories or models by favoring those that are both simple in structure and parsimonious in assumptions and parameters. The term embodies a synthesis of two closely related principles: simplicity, which seeks minimal theoretical constructs and elegant explanations, and parsimony, which minimizes the number of free parameters, mechanisms, or auxiliary assumptions. As a guiding criterion, simplicityparsimony aims to avoid overcomplication while retaining explanatory power and predictive accuracy.

Historically, the idea overlaps with Occam's razor and the broader tradition of model selection. In formal settings, it parallels approaches such as information criteria (AIC, BIC) and the minimum description length principle, which penalize model complexity to balance fit and simplicity. In machine learning, regularization methods and model selection via cross-validation enact the same philosophy: prefer models that perform well with fewer parameters. The concept is pragmatic rather than a strict theorem; its exact balance depends on domain, data, and aims.

Critiques note that what counts as simple or parsimonious can be subjective and context dependent; overly aggressive parsimony risks underfitting and obscuring true complexities. While helpful as a heuristic, simplicityparsimony does not guarantee truth and must be supplemented with empirical validation, theoretical considerations, and domain knowledge.