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.
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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).
Inhaltsangabe
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.
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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