Sloppy Models, Differential Geometry, and Why Science Works

Explore the concept of "sloppy models" in a comprehensive lecture by Jim Sethna from Cornell University. Delve into the world of complex systems biology, climate change, ecology, and macroeconomics, examining how models with hard-to-measure parameters can still make reliable predictions. Discover the intriguing 'hyperribbon' structure of model prediction manifolds using differential geometry and approximation theory. Investigate how sloppiness in physics theories may explain why the world is comprehensible. Learn about new visualization methods for probabilistic systems, including the space of possible universes as measured by cosmic microwave background radiation. Gain insights into topics such as emergent vs. fundamental parameters, Fisher Information Matrix, and the Model Manifold. Understand how the concept of sloppiness relates to various fields and why it's crucial for scientific understanding.

Intro
Sloppy Models, Differential geometry, and why science works
Emergent vs. Fundamental Reducing the number of basic parameters Physics Controlled
Systems Biology: Cell Protein Reactions
Sloppy 'Universality'
Fisher Information is the Metric Fisher Information Matrix (FIM) measures distance
Physics: Sloppiness and Emergence Ben Machta, Ricky Chachra, Mark Transtrum
The Model Manifold: Predictions Two exponentials ..
The Model Manifold is a Hyperribbon Mark Transtrum, Ben Machta
Rigorous hyperellipsoid bounds on model manifold
Why a hyperribbon?
Hyperribbons for Ising: Curing the curse of dimensionality
MBAM Generation of Reduced Models
Sloppy Models, Differential geometry, and the space of model predictions