Data Science for Complex Systems

This book provides a guide to the analysis of complex systems through the lens of data science.

Anindya S. Chakrabarti (Author), K. Shuvo Bakar (Author), Anirban Chakraborti (Author)

9781108844796, Cambridge University Press

Hardback, published 25 May 2023

289 pages
25.1 x 17.6 x 2.1 cm, 0.7 kg

'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)

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.

Preface
Part I. Introduction: 1. Facets of complex systems
Part II. Heterogeneity and Dependence: 2. Quantifying heterogeneity: Classical and Bayesian statistics
3. Statistical analyses of time-varying phenomena
Part III. Patterns and Interlinkages: 4. Pattern recognition in complex systems: machine learning
5. Interlinkages and heterogeneity: network theory. Part IV. Emergence: from Micros to Macro: 6. Interaction and emergence: agent-based models
7. Epilogue
References
Index.

Subject Areas: Mathematical theory of computation [UYA], Data capture & analysis [UNC], Statistical physics [PHS], Complex analysis, complex variables [PBKD], Research methods: general [GPS]