Data Science for Complex Systems

Anindya S. Chakrabarti; K. Shuvo Bakar; Anirban Chakraborti

doi:10.1017/9781108953597

Many real-life systems are dynamic, evolving, and intertwined. Examples of such systems displaying 'complexity', can be found in a wide variety of contexts ranging from economics to biology, to the environmental and physical sciences. The study of complex systems involves analysis and interpretation of vast quantities of data, which necessitates the application of many classical and modern tools and techniques from statistics, network science, machine learning, and agent-based modelling. Drawing from the latest research, this self-contained and pedagogical text describes some of the most important and widely used methods, emphasising both empirical and theoretical approaches. More broadly, this book provides an accessible guide to a data-driven toolkit for scientists, engineers, and social scientists who require effective analysis of large quantities of data, whether that be related to social networks, financial markets, economies or other types of complex systems.

Winner, 2024 Choice Awards

‘Complex systems are a subject of popular interest thanks to the efforts of both academics and industry researchers during the last few years, and the 2021 Nobel Prize in Physics. This book is timely and it gives a comprehensive view of complex systems with an emphasis on data-driven contributions, ranging from economic and financial aspects to broader social science applications. Reading this book is a pleasure, and it provides a solid and robust understanding of the key topics in the field. This is a ‘must -read for anybody interested in complex systems’ and I strongly recommend having it on your shelf!’

Tiziana Di Matteo - King's College London, Complexity Science Hub Vienna, and Enrico Fermi Research Centre (CREF)

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Data Science for Complex Systems

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Contents

Frontmatter
pp i-iv

Contents
pp v-vi

Preface
pp vii-ix

Acknowledgments
pp x-x

Part I - Introduction
pp 1-2

1 - Facets of Complex Systems
pp 3-12

Part II - Heterogeneity and Dependence
pp 13-14

2 - Quantifying Heterogeneity: Classical and Bayesian Statistics
pp 15-87

3 - Statistical Analyses of Time-Varying Phenomena
pp 88-130

Part III - Patterns and Interlinkages
pp 131-132

4 - Pattern Recognition in Complex Systems: Machine Learning
pp 133-178

5 - Interlinkages and Heterogeneity: Network Theory
pp 179-234

Part IV - Emergence: From Micro to Macro
pp 235-236

6 - Interaction and Emergence: Agent-Based Models
pp 237-267

Epilogue
pp 268-270

References
pp 271-291

Index
pp 292-294

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Data Science for Complex Systems

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