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OverviewMany 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. Full Product DetailsAuthor: Anindya S. Chakrabarti (Indian Institute of Management Ahmedabad) , K. Shuvo Bakar (University of Sydney) , Anirban Chakraborti (BML Munjal University, India)Publisher: Cambridge University Press Imprint: Cambridge University Press Dimensions: Width: 17.60cm , Height: 2.10cm , Length: 25.10cm Weight: 0.700kg ISBN: 9781108844796ISBN 10: 1108844790 Pages: 289 Publication Date: 25 May 2023 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of ContentsPreface; 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.Reviews'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) Author InformationAnindya 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. Tab Content 6Author Website:Countries AvailableAll regions |
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