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The Online Algorithmic Complexity Calculator v3.0

R Shiny App

R Shiny code for the Online Algorithmic Complexity Calculator version 3.0 by the Algorithmic Nature Group and the Algorithmic Dynamics Lab.

To run this app locally, download R Studio and follow the instructions here.

Our Block Decomposition Method to estimate Kolmogorov complexity in strings and graphs of arbitrary size is described in A Divide-and-Conquer Method for Local Estimations of Algorithmic Complexity Lower Bounded by Shannon Entropy by H. Zenil, F. Soler-Toscano, N.A. Kiani, S. Hernández-Orozco, and A. Rueda-Toicen.

The Coding Theorem Method and the Block Decomposition Method are also described in Calculating Kolmogorov Complexity from the Output Frequency Distributions of Small Turing Machines by F. Soler-Toscano, H. Zenil, J.-P. Delahaye and N. Gauvrit.

If you make use of results from this calculator, please make sure to visit How To Cite.

This app uses the acss package available at CRAN and maintained by Henrik Singmann.

License

GNU Affero General Public License v3.0

Maintainer

Antonio Rueda-Toicen

https://complexity-calculator.com

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'layman's' example

Thanks for providing this code and shiny app along with your article in Nature Machine Intelligence.
I'm greatly interested in the interplay between causality and machine learning, and personally work on medical applications. As an ML practitioner working on applications your method seems interesting, but the provided examples are not very accessible.

Could you provide an example that may relate to real-world data? I'm thinking:

clinical parameters from patients, generated by distinct disease phenotypes (e.g. records of age, sex, blood pressure, lung function etc, simulated according to some disease model with different underlying factors)
images (e.g. generated through different causal models)

I'd happily think along with generating some example simulations

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