Algorithmic Information Dynamics
A Computational Approach to Causality with Applications to Living Systems

A book at the intersection of the most exciting current scientific trends in complexity science, information theory and living systems.

Hector Zenil (Author), Narsis A. Kiani (Author), Jesper Tegnér (Author)

9781108497664, Cambridge University Press

Hardback, published 25 May 2023

345 pages
25 x 17.5 x 2.4 cm, 0.79 kg

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.

Introduction
Part I. Preliminaries: 1. A computational approach to causality
2. Networks: from structure to dynamics
3. Information and computability theories
Part II. Theory and Methods: 4. Algorithmic information theory
5. The coding theorem method (CTM)
6. The block decomposition method (BDM)
7. Graph and tensor complexity
8. Algorithmic information dynamics (AID)
Part III. Applications: 9. From theory to practice
10. Algorithmic dynamics in artificial environments
11. Applications to integer and behavioural sequences
12. Applications to evolutionary biology
Postface
Appendix: Mutual and conditional BDM
Glossary.

Subject Areas: Algorithms & data structures [UMB], Genetics [non-medical PSAK]