# Kevin Brown-The Statistics and Statistical Mechanics of Sloppy Models

# Kevin Brown-The Statistics and Statistical Mechanics of Sloppy Models

Scientists are often forced to construct models of natural systems from grossly incomplete information, as in cellular signal transduction, oceanic biogeochemistry, and terrestrial nutrient cycling. Dynamical modeling of such complex systems presents four key challenges: (i) the presence of poorly known parameters, (ii) the necessity of using simplified dynamics, (iii) uncertain model connectivity, and (iv) limited data availability. Can such models even be predictive, and if so, how do we use them to properly infer the results of perturbations to the system under study? I designate problems with these four challenges *sloppy models*. While sloppiness is ubiquitous in physical, chemical, and biological systems, not all models are sloppy. Sloppy models form a well-defined class of problem which tends to arise in nonlinear systems with weak prior parameter constraints. Sloppy model parameter space geometry has important implications for parameter estimation, prediction, model selection, and optimal experimental design in these systems. I will discuss these issues with repeated reference to two specific models: one from cancer biology and another from estuarine biogeochemistry.

Refreshments will be served from 3:30-4pm in the Yunker Library (WNGR 379).