Sloppy -

: 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.

Below are several major papers and resources that define the field:

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. sloppy

(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. : Researchers use the FIM to measure how

(Transtrum et al., 2015): A definitive review describing the information theoretic framework based on the Fisher Information Matrix (FIM).

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

: The set of all possible model predictions forms a "manifold" that is often extremely narrow in some dimensions, resembling a "hyper-ribbon". Other Contexts of "Sloppy" in Research