Parsimoniousness

Parsimoniousness refers to the quality of being parsimonious, that is, frugal or economical in the use of resources. In scholarly contexts, the term extends to a methodological preference for simplicity: explanations or models should not multiply entities beyond necessity. The word derives from Latin parsimonia, meaning thrift, and is closely linked to the concept of parsimony.

In philosophy and the history of science, parsimoniousness underpins the principle of parsimony, often expressed as Occam's razor: among competing hypotheses, the simpler one with fewer assumptions is preferable, provided it adequately explains the data. Parsimony is not a rigid rule but a pragmatic guideline used to evaluate explanations and theories.

In empirical disciplines, pursuing parsimonious models helps avoid overfitting and improves generalization. In statistics and machine learning, methods favoring parsimonious representations, such as regularization or model selection criteria like AIC or BIC, are widely used. In biology and bioinformatics, the method of maximum parsimony searches for phylogenetic trees that minimize evolutionary changes.

Limitations and criticisms: Parsimony is interpretive and context-dependent, and simplistic models may miss important mechanisms. The simplest explanation is not guaranteed to be true, and choosing the level of simplicity involves subjective judgments about the costs of complexity and the quality of data.