(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.
(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. 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. (Waterfall et al
Below are several major papers and resources that define the field: In sloppy models, FIM eigenvalues are distributed roughly
(Transtrum et al., 2015): A definitive review describing the information theoretic framework based on the Fisher Information Matrix (FIM).
: 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