Overview

Many professionals are facing a monumental challenge: navigating the intricate landscape of information to make impactful choices. The sheer volume and complexity of big data have ushered in a shift, demanding innovative methodologies and frameworks. Big Data Quantification for Complex Decision-Making tackles this challenge head-on, offering a comprehensive exploration of the tools necessary to distill valuable insights from datasets. This book serves as a tool for professionals, researchers, and students, empowering them to not only comprehend the significance of big data in decision-making but also to translate this understanding into real-world decision making. The central objective of the book is to examine the relationship between big data and decision-making. It strives to address multiple objectives, including understanding the intricacies of big data in decision-making, navigating methodological nuances, managing uncertainty adeptly, and bridging theoretical foundations with real-world applications. The book's core aspiration is to provide readers with a comprehensive toolbox, seamlessly integrating theoretical frameworks, practical applications, and forward-thinking perspectives. This equips readers with the means to effectively navigate the data-rich landscape of modern decision-making, fostering a heightened comprehension of strategic big data utilization. Tailored for a diverse audience, this book caters to researchers and academics in data science, decision science, machine learning, artificial intelligence, and related domains. Additionally, it extends its reach to practitioners in data analytics, business intelligence, risk management, and strategic decision-making, offering pragmatic insights and methodologies. Graduate students in data science and decision analysis can also benefit from the book's potential as a textbook or supplementary reading material. By covering an array of topics, from big data analytics to deep learning models and game theory, this book positions itself as an indispensable resource, guiding readers towards mastering the fusion of big data and decision-making in an era defined by complexity and uncertainty.

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Chao Zhang received the M.S. degree in Electrical and Electronic Engineering from the University of Hong Kong, Hong Kong, China, in 2014, and the Ph.D. degree in Systems Engineering from the Shanxi University, Taiyuan, China, in 2017. He is currently an Associate Professor with Shanxi University, Taiyuan, China. He has published more than 50 journal and conference papers. His current research interests include granular computing and intelligent decision-making.

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