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Algorithmic Information Dynamics: A Computational Approach to Causality with Applications to Living Systems
Biological systems are extensively studied as interactions forming complex networks. Reconstructing causal knowledge from, and principles of, these networks from noisy and incomplete data is a challenge in the field of systems biology. Based on an online course hosted by the Santa Fe Institute Complexity Explorer, this book introduces the field of Algorithmic Information Dynamics, a model-driven approach to the study and manipulation of dynamical systems . It draws tools from network and systems biology as well as information theory, complexity science and dynamical systems to study natural and artificial phenomena in software space. It consists of a theoretical and methodological framework to guide an exploration and generate computable candidate models able to explain complex phenomena in particular adaptable adaptive systems, making the book valuable for graduate students and researchers in a wide number of fields in science from physics to cell biology to cognitive sciences.
345 pages, Hardcover
Published May 25, 2023
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December 23, 2025
Fascinating topic. The authors provide a workable method to apply algorithmic information theory to practical problems.
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To explain the world, one needs a causal model. This means that when observing data, a DNA string, a time series, a text, a graph, etc, one has to find the process that can generate this data. Any data-generating model is, in practice, a computer program. For any piece of data, there are an infinite number of programs that can generate it. For example, a source code file containing a print statement that outputs our data does the trick. It is immediately clear that such a program would not satisfy us, nor would it explain the data. Intuitively, when you have a large dataset, which can be generated with a small program or model, it holds a lot of explanatory power. This is the idea behind Occam’s razor: preferring the simplest model for one’s data. Conversely, if it is not possible to find something smaller than just printing your data (meaning the length of your source code is about the length of the data), it might be an indication that your data is “random”. A simple example is the number pi, of which a simple program can generate as many digits as one would like, whereas a very long series of die roll outcomes can only be recorded, not computed. This is the idea behind Algorithmic Information Dynamics, the framework that Zenil and co-authors propose for understanding (living) systems. The keyword is compression: reducing data as much as possible with a computer program.
See https://michielstock.github.io/posts/... for full summary.
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To explain the world, one needs a causal model. This means that when observing data, a DNA string, a time series, a text, a graph, etc, one has to find the process that can generate this data. Any data-generating model is, in practice, a computer program. For any piece of data, there are an infinite number of programs that can generate it. For example, a source code file containing a print statement that outputs our data does the trick. It is immediately clear that such a program would not satisfy us, nor would it explain the data. Intuitively, when you have a large dataset, which can be generated with a small program or model, it holds a lot of explanatory power. This is the idea behind Occam’s razor: preferring the simplest model for one’s data. Conversely, if it is not possible to find something smaller than just printing your data (meaning the length of your source code is about the length of the data), it might be an indication that your data is “random”. A simple example is the number pi, of which a simple program can generate as many digits as one would like, whereas a very long series of die roll outcomes can only be recorded, not computed. This is the idea behind Algorithmic Information Dynamics, the framework that Zenil and co-authors propose for understanding (living) systems. The keyword is compression: reducing data as much as possible with a computer program.
See https://michielstock.github.io/posts/... for full summary.
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