Algorithmic Information Dynamics
A Computational Approach to Causality with Applications to Living Systems
Published on 25. May 2023
Book
Hardback
344 pages
978-1-108-49766-4 (ISBN)
Description
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.
More details
Language
English
Place of publication
Cambridge
United Kingdom
Target group
College/higher education
Professional and scholarly
Illustrations
Worked examples or Exercises
Dimensions
Height: 176 mm
Width: 252 mm
Thickness: 24 mm
Weight
814 gr
ISBN-13
978-1-108-49766-4 (9781108497664)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Hector Zenil | Narsis A. Kiani | Jesper Tegner
Algorithmic Information Dynamics
A Computational Approach to Causality with Applications to Living Systems
E-Book
05/2023
Cambridge University Press
€78.99
Available for download
Persons
Hector Zenil is a senior researcher at the Alan Turing Institute, British Library, researcher at the Department of Chemical Engineering and Biotechnology, University of Cambridge and the leader of the Algorithmic Dynamics Lab at the Karolinska Institute in Sweden. Previous positions include Computer Science faculty member at the University of Oxford, NASA Payload team member for the Mars Gravity Biosatellite at the Massachusetts Institute of Technology, and researcher at the Evolutionary and Behavioural Theory Lab at the University of Sheffield. He helped develop the factual answering Artificial Intelligence engine behind Siri and Alexa at Wolfram Research. He has published over 120 peer-reviewed papers, edited six books, is Editor of the journal Complex Systems, and the author of Methods and Applications of Algorithmic Complexity (2022).
Content
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.