ArticleScientific reports2020
Generic predictions of output probability based on complexities of inputs and outputs.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Time Series Correlations and Kolmogorov Complexity: A Hausdorff Dimension Perspective.Entropy (Basel, Switzerland) · 2026Article
- Bounding phenotype transition probabilities via conditional complexity.Journal of the Royal Society, Interface · 2025Article
- Article
- Exploring Simplicity Bias in 1D Dynamical Systems.Entropy (Basel, Switzerland) · 2024Article
- Bias in the arrival of variation can dominate over natural selection in Richard Dawkins's biomorphs.PLoS computational biology · 2024Article
- Random and Natural Non-Coding RNA Have Similar Structural Motif Patterns but Differ in Bulge, Loop, and Bond Counts.Life (Basel, Switzerland) · 2023Article
- Predicting phenotype transition probabilities via conditional algorithmic probability approximations.Journal of the Royal Society, Interface · 2022Article
- Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For a broad class of input-output maps, arguments based on the coding theorem from algorithmic information theory (AIT) predict that simple (low Kolmogorov complexity) outputs are exponentially more likely to occur upon uniform random sampling of inputs than complex outputs are. Here, we derive probability bounds that are based on the complexities of the inputs as well as the outputs, rather than just on the complexities of the outputs. The more that outputs deviate from the coding theorem bound, the lower the complexity of their inputs. Since the number of low complexity inputs is limited, this behaviour leads to an effective lower bound on the probability. Our new bounds are tested for an RNA sequence to structure map, a finite state transducer and a perceptron. The success of these new methods opens avenues for AIT to be more widely used.
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Registered trials
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