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

Hector Zenil; Narsis A. Kiani; Jesper Tegnér

doi:10.1017/9781108596619

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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Contents

Frontmatter
pp i-iv
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Contents
pp v-viii
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Preface
pp ix-xi
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Acknowledgements
pp xii-xii
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Introduction: Living Systems, Causality, and Information
pp 1-6
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Part I - Preliminaries
pp 7-8
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1 - A Computational Approach to Causality
pp 9-53
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2 - Networks: From Structure to Dynamics
pp 54-94
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3 - Information and Computability Theories
pp 95-138
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Part II - Theory and Methods
pp 139-140
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4 - Algorithmic Information Theory
pp 141-177
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5 - The Coding Theorem Method (CTM)
pp 178-188
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6 - The Block Decomposition Method (BDM)
pp 189-213
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7 - Graph and Tensor Complexity
pp 214-227
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8 - Algorithmic Information Dynamics (AID)
pp 228-240
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Part III - Applications
pp 241-242
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9 - From Theory to Practice
pp 243-250
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10 - Algorithmic Dynamics in Artificial Environments
pp 251-262
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11 - Applications to Integer and Behavioural Sequences
pp 263-273
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12 - Applications to Evolutionary Biology
pp 274-297
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Postface
pp 298-298
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Appendix: - Mutual and Conditional BDM
pp 299-307
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Glossary
pp 308-309
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References
pp 310-321
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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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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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