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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.23398 |
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| _version_ | 1866909627055079424 |
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| author | Bauer, Marianne Bialek, William Goddard, Chase Holmes, Caroline M. Krishnamurthy, Kamesh Palmer, Stephanie E. Pang, Rich Schwab, David J. Susman, Lee |
| author_facet | Bauer, Marianne Bialek, William Goddard, Chase Holmes, Caroline M. Krishnamurthy, Kamesh Palmer, Stephanie E. Pang, Rich Schwab, David J. Susman, Lee |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23398 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimization and variability can coexist Bauer, Marianne Bialek, William Goddard, Chase Holmes, Caroline M. Krishnamurthy, Kamesh Palmer, Stephanie E. Pang, Rich Schwab, David J. Susman, Lee Quantitative Methods Disordered Systems and Neural Networks 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. |
| title | Optimization and variability can coexist |
| topic | Quantitative Methods Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2505.23398 |