Description

Book Synopsis
Aimed at graduate students and researchers, this book offers a model-driven approach to the study and manipulation of dynamical systems. Based on an online course hosted by the Complexity Explorer, it uses analytical tools from information theory and complexity science to tackle key challenges in network and systems biology.

Table of Contents
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.

Algorithmic Information Dynamics

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    A Hardback by Hector Zenil, Narsis A. Kiani, Jesper Tegnér

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      Publisher: Cambridge University Press
      Publication Date: Publication Date: 5/25/2023 12:00:00 AM
      ISBN13: 9781108497664, 978-1108497664
      ISBN10: 1108497667
      Also in:
      Computing Algorithms and data structures Science & Nature Biology, life sciences Computational biology / bioinformatics

      Description

      Book Synopsis
      Aimed at graduate students and researchers, this book offers a model-driven approach to the study and manipulation of dynamical systems. Based on an online course hosted by the Complexity Explorer, it uses analytical tools from information theory and complexity science to tackle key challenges in network and systems biology.

      Table of Contents
      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.

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