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

Hector Zenil; Narsis A. Kiani; Jesper Tegnér

doi:10.1017/9781108596619

Algorithmic Information Dynamics

A Computational Approach to Causality with Applications to Living Systems

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Kempt, Hendrik 2024. (Un)explainable Technology. p. 41.

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Abrahão, Felipe S. Hernández-Orozco, Santiago Kiani, Narsis A. Tegnér, Jesper Zenil, Hector and Cherifi, Hocine 2024. Assembly Theory is an approximation to algorithmic complexity based on LZ compression that does not explain selection or evolution. PLOS Complex Systems, Vol. 1, Issue. 1, p. e0000014.

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Nayyeri, Amirahmad Mansoori, Eghbal G. and Taheri, Mohammad 2024. A new strategy to generate scale free networks by capturing the information processing mechanism in a specific family of elementary cellular automata. International Journal of Parallel, Emergent and Distributed Systems, p. 1.

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Scharf, Caleb and Witkowski, Olaf 2024. Rebuilding the Habitable Zone from the Bottom up with Computational Zones. Astrobiology, Vol. 24, Issue. 6, p. 613.

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Uthamacumaran, Abicumaran Abrahão, Felipe S. Kiani, Narsis A. and Zenil, Hector 2024. On the salient limitations of the methods of assembly theory and their classification of molecular biosignatures. npj Systems Biology and Applications, Vol. 10, Issue. 1,

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Gasparini, Abebe Lohmander, Carl Hannes and Lee, Keeheon 2024. Waning Breakthroughs? Investigating the State of Innovation in the Field of NLP. p. 1.

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Varley, Thomas F. 2024. A Synergistic Perspective on Multivariate Computation and Causality in Complex Systems. Entropy, Vol. 26, Issue. 10, p. 883.

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Hector Zenil, University of Cambridge, Narsis A. Kiani, Karolinska Institutet, Stockholm, Jesper Tegnér, King Abdullah University of Science and Technology, Saudi Arabia

Publisher:: Cambridge University Press
Online publication date:: May 2023
Print publication year:: 2023
Online ISBN:: 9781108596619
DOI:: https://doi.org/10.1017/9781108596619

Subjects:: Genomics, Bioinformatics and Systems Biology, Life Sciences, Computer Science, Computational Biology and Bioinformatics

59.99 (GBP)

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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.

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Algorithmic Information Dynamics

A Computational Approach to Causality with Applications to Living Systems

This Book has been cited by the following publications. This list is generated based on data provided by Crossref.

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Contents

Frontmatter
pp i-iv

Contents
pp v-viii

Preface
pp ix-xi

Acknowledgements
pp xii-xii

Introduction: Living Systems, Causality, and Information
pp 1-6

Part I - Preliminaries
pp 7-8

1 - A Computational Approach to Causality
pp 9-53

2 - Networks: From Structure to Dynamics
pp 54-94

3 - Information and Computability Theories
pp 95-138

Part II - Theory and Methods
pp 139-140

4 - Algorithmic Information Theory
pp 141-177

5 - The Coding Theorem Method (CTM)
pp 178-188

6 - The Block Decomposition Method (BDM)
pp 189-213

7 - Graph and Tensor Complexity
pp 214-227

8 - Algorithmic Information Dynamics (AID)
pp 228-240

Part III - Applications
pp 241-242

9 - From Theory to Practice
pp 243-250

10 - Algorithmic Dynamics in Artificial Environments
pp 251-262

11 - Applications to Integer and Behavioural Sequences
pp 263-273

12 - Applications to Evolutionary Biology
pp 274-297

Postface
pp 298-298

Appendix: - Mutual and Conditional BDM
pp 299-307

Glossary
pp 308-309

References
pp 310-321

Index
pp 322-332

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Algorithmic Information Dynamics

A Computational Approach to Causality with Applications to Living Systems

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