Data Science for Complex Systems

Anindya S. Chakrabarti is an Associate Professor of Economics and UTI Chair of Macroeconomics at the Indian Institute of Management Ahmedabad. His main research interests are macroeconomics, big data in economics, time series econometrics, network theory and complex systems. K. Shuvo Bakar is Senior Lecturer at the University of Sydney. His research interests are Bayesian modelling and computation to reduce uncertainty in inferential statements. He works on statistical machine learning methods and applications to real-life data-driven problems. Anirban Chakraborti is Dean of Research at the School of Engineering and Technology at BML Munjal University, India. His main research interests lie in the areas of econophysics, data science, quantum physics and nanomaterial science.

'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)

• 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.