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Inferring statistical complexity
Abstract
Statistical mechanics is used to describe the observed information processing complexity of nonlinear dynamical systems. We introduce a measure of complexity distinct from and dual to the information theoretic entropies and dimensions. A technique is presented that directly reconstructs minimal equations of motion from the recursive structure of measurement sequences. Application to the period-doubling cascade demonstrates a form of superuniversality that refers only to the entropy and complexity of a data stream.
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Cites
On the Length of Programs for Computing Finite Binary Sequences
Chaitin, G. J. (1966) · Journal of the ACM · doi:10.1145/321356.321363 · Cited by 913
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Metadata
- Authors
- Crutchfield, J. P., & Young, K.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 1989-07-10
- Volume
- 63
- Issue
- 2
- Pages
- 105-108
- ISSN
- 0031-9007 (ISSN-L)
- OpenAlex
- W2075694080
- Cited by
- 1,002
- References
- 1
- FWCI
- 20.16
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
