parsimonybased

Parsimony-based refers to methods and approaches that apply the principle of parsimony—the preference for the simplest explanation that adequately fits the data—to inference, model selection, and reconstruction tasks. In practice, parsimony-based methods seek models or explanations with minimal complexity, often by penalizing additional parameters or by minimizing the number of changes or components required to explain observations.

In phylogenetics, parsimony-based inference aims to reconstruct evolutionary trees that minimize the total number of character-state changes needed to account for the observed data. Classic implementations include Fitch and Wagner parsimony, as well as Dollo parsimony, which imposes specific constraints on character evolution. Parsimony methods in this domain are valued for their model-agnostic nature but can be statistically inconsistent under certain evolutionary scenarios and may become less robust with large or noisy datasets.

In statistics and machine learning, parsimony-based reasoning commonly manifests through regularization and model selection criteria that favor simpler models. Techniques such as L1 regularization (lasso) promote sparsity, reducing the number of features or parameters. Information criteria like AIC or BIC balance goodness-of-fit with model complexity. The overarching goal is to improve interpretability and generalization while avoiding overfitting.

Benefits of parsimony-based approaches include interpretability and resistance to overfitting when appropriate. Limitations involve the potential for oversimplification and the possibility that the simplest adequate model is not the most accurate or informative in a given context. The concept is closely related to Occam’s razor and is applied across disciplines to pursue explanatory sufficiency with minimal assumed complexity.