Sloppy -

: A few parameter combinations ("stiff") tightly constrain model behavior, while others ("sloppy") can vary by orders of magnitude without changing the output.

The primary foundational paper for this concept is , which provides a comprehensive review of the framework. Key Scientific Papers on Sloppiness sloppy

(Transtrum et al., 2015): A definitive review describing the information theoretic framework based on the Fisher Information Matrix (FIM). : A few parameter combinations ("stiff") tightly constrain

: Researchers use the FIM to measure how distinguishable models are based on their predictions. In sloppy models, FIM eigenvalues are distributed roughly evenly over many decades. : Researchers use the FIM to measure how

(Gutenkunst et al., 2007): Demonstrates that sloppiness is a universal feature in systems biology, suggesting that modelers should focus on predictions rather than exact parameter values.

(Waterfall et al., 2006): Proposes that sloppy models belong to a common "universality class" with eigenvalue spectra that are roughly constant on a logarithmic scale.