Optimization and variability can coexist
Quantitative Methods
2025-05-30 v1 Disordered Systems and Neural Networks
Abstract
Many biological systems perform close to their physical limits, but promoting this optimality to a general principle seems to require implausibly fine tuning of parameters. Using examples from a wide range of systems, we show that this intuition is wrong. Near an optimum, functional performance depends on parameters in a "sloppy'' way, with some combinations of parameters being only weakly constrained. Absent any other constraints, this predicts that we should observe widely varying parameters, and we make this precise: the entropy in parameter space can be extensive even if performance on average is very close to optimal. This removes a major objection to optimization as a general principle, and rationalizes the observed variability.
Cite
@article{arxiv.2505.23398,
title = {Optimization and variability can coexist},
author = {Marianne Bauer and William Bialek and Chase Goddard and Caroline M. Holmes and Kamesh Krishnamurthy and Stephanie E. Palmer and Rich Pang and David J. Schwab and Lee Susman},
journal= {arXiv preprint arXiv:2505.23398},
year = {2025}
}