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Topical Collection "Algorithmic Information Dynamics: A Computational Approach to Causality from Cells to Networks"

A topical collection in Entropy (ISSN 1099-4300). This collection belongs to the section "Complexity".

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1. Oxford Immune Algorithmics, Reading RG1 3EU, UK
2. Algorithmic Dynamics Lab, Unit of Computational Medicine, SE-17177 Stockholm, Sweden
Interests: algorithmic information dynamics; causality; algorithmic information theory; cellular automata; machine learning; randomness
Special Issues, Collections and Topics in MDPI journals
1. National Laboratory for Scientific Computing (LNCC), Petropolis 25651-075, RJ, Brazil
2. Algorithmic Nature Group, Laboratoire de Recherche Scientifique (LABORES) for the Natural and Digital Sciences, 75005 Paris, France
Interests: algorithmic information; complexity; emergence; self-organization; evolution

Topical Collection Information

Dear Colleagues,

Classical probability theory and traditional statistics have long helped scientists to find meaningful signals amid the noise and thereby make sense of the world. However, classical approaches have proven a little threadbare in today’s landscape of large datasets and complex data, which are driving new insights in disciplines ranging from biology to ecology to economics. This is as true in biology, with the advent of new techniques of genome editing, as it is in astronomy, with telescope surveys charting the entire sky in the search for new Earth-like planets. The data have changed. Maybe it is time our data analysis tools did, too.

Algorithmic information dynamics (AID) is a new type of discrete calculus based on computer programming, employed to study complex systems by exploring the software space of models explaining a system subject to changes or perturbations. The objective is to look for computable mechanistic generating models and first principles, thereby ushering in the next generation of scientific discovery and model-driven machine learning. Following a popular online course sponsored by the Santa Fe Institute, and the publication in recent years of a number of papers (by original authors as well as independent groups) that utilize AID based on new tools such as the coding theorem and the block decomposition method (motivated by algorithmic probability) to discover new knowledge, AID is becoming a tool fully equal to the challenge of studying rich, complex systems. Whereas we had only been using weak (e.g., computable) measures to study complex systems, now it is possible to match data to models in degree of sophistication.

We encourage authors and researchers to continue exploring how AID can help us to understand new aspects of systems science by building rich causal computational models and submitting their results to this Topical Collection. They would thereby be contributing to progress in the methodological aspects of systems science, advancing it beyond its current reliance on simplistic data analysis and ad hoc measures.

Dr. Hector Zenil
Dr. Felipe S. Abrahão
Collection Editors

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