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 model behavior, while others ("sloppy") can vary by orders of magnitude without changing the output. sloppy

(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. (Transtrum et al

(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. (Gutenkunst et al

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

In scientific literature, a "sloppy" model refers to a complex multiparameter system where model behavior is highly sensitive to only a few "stiff" parameter combinations, while the majority of "sloppy" directions in parameter space have almost no effect on model predictions.

(Machta et al., 2013): Explains why complicated microscopic processes often result in simple macroscopic behavior. Core Concepts of "Sloppy" Research