Parsimonyyn

Parsimonyyn is a theoretical concept proposed in discussions of model selection and explanation that extends the traditional notion of parsimony. It formalizes the idea that good explanations should be both simple and resilient to uncertainty, explicitly accounting for how results withstand perturbations in data or assumptions. The term combines parsimony with the suffix -yn to denote a doctrine or principle.

Core idea: Parsimonyyn evaluates explanations along two axes: simplicity (the minimal number of distinct assumptions or components) and robustness (the stability of inferences under data noise or model perturbations). Explanations are ranked by a Parsimonyyn score that balances these axes, with lower scores indicating preferable models under the chosen weighting.

Applications: The principle is discussed in theoretical work on machine learning, bioinformatics, and history of science, where researchers must trade off model complexity against sensitivity to data quality. In phylogenetics and epidemiology, Parsimonyyn has been used to argue for models that avoid overfitting while preserving predictive reliability.

Limitations and reception: Parsimonyyn does not define a universal weighting between simplicity and robustness; the choice of metrics and weights is context dependent. Critics caution that the framework can reflect subjective priorities and may not resolve ambiguity when data are limited or highly noisy.

See also: Parsimony, Occam's razor, model selection, robustness.