Algorithmic complexity for psychology: A user-friendly implementation of the coding theorem method

Nicolas Gauvrit; Henrik Singmann; Fernando Soler-Toscano; Hector Zenil

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arXiv · 1409.4080

Algorithmic complexity for psychology: A user-friendly implementation of the coding theorem method

Nicolas Gauvrit·Henrik Singmann·Fernando Soler-Toscano·Hector Zenil

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Abstract

Kolmogorov-Chaitin complexity has long been believed to be impossible to approximate when it comes to short sequences (e.g. of length 5-50). However, with the newly developed \emph{coding theorem method} the complexity of strings of length 2-11 can now be numerically estimated. We present the theoretical basis of algorithmic complexity for short strings (ACSS) and describe an R-package providing functions based on ACSS that will cover psychologists' needs and improve upon previous methods in three ways: (1) ACSS is now available not only for binary strings, but for strings based on up to 9 different symbols, (2) ACSS no longer requires time-consuming computing, and (3) a new approach based on ACSS gives access to an estimation of the complexity of strings of any length. Finally, three illustrative examples show how these tools can be applied to psychology.

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Nicolas Gauvrit, Henrik Singmann, Fernando Soler-Toscano, Hector Zenil. 2014-09-14. Algorithmic complexity for psychology: A user-friendly implementation of the coding theorem method. https://arxiv.org/abs/1409.4080

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