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This edited volume focuses on big data implications for computational social science and humanities from management to usage. The first part of the book covers geographic data, text corpus data, and social media data, and exemplifies their concrete applications in a wide range of fields including anthropology, economics, finance, geography, history, linguistics, political science, psychology, public health, and mass communications. The second part of the book provides a panoramic view of the development of big data in the fields of computational social sciences and humanities. The following questions are addressed: why is there a need for novel data governance for this new type of data?, why is big data important for social scientists?, and how will it revolutionize the way social scientists conduct research? With the advent of the information age and technologies such as Web 2.0, ubiquitous computing, wearable devices, and the Internet of Things, digital society has fundamentally changed what we now know as "data", the very use of this data, and what we now call "knowledge". Big data has become the standard in social sciences, and has made these sciences more computational. Big Data in Computational Social Science and Humanities will appeal to graduate students and researchers working in the many subfields of the social sciences and humanities.

Social sciences --- Digital humanities. --- Big data --- Data processing. --- Social aspects. --- Data sets, Large --- Large data sets --- Data sets --- Humanities --- Data processing --- Information technology --- Data mining. --- Behavioral economics. --- Big data. --- Natural language processing (Computer science). --- Philosophy. --- Political science. --- Data Mining and Knowledge Discovery. --- Behavioral/Experimental Economics. --- Big Data/Analytics. --- Natural Language Processing (NLP). --- Philosophy of Technology. --- Political Science. --- Administration --- Civil government --- Commonwealth, The --- Government --- Political theory --- Political thought --- Politics --- Science, Political --- State, The --- Mental philosophy --- NLP (Computer science) --- Artificial intelligence --- Electronic data processing --- Human-computer interaction --- Semantic computing --- Behavioral economics --- Behavioural economics --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Economics --- Natural language processing (Computer science) --- Psychological aspects.

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Being published as a celebration of the 60th anniversary of John von Neumann’s “Theory of Self-Reproducing Automata,” this handbook attempts to provide a unique reflection on the nature of computational economics and finance (CEF) in light of natural computationalism. We restructure CEF by including both nature-inspired computing and natural computing. This new framework allows us to have a view of CEF much broader than just the conventional algorithmic consideration. The book begins with a historical review of computational economics (CE), tracing its history far back to the era of analog computing. In these early days, advancements were mainly made using the idea of natural computing, and the subjects pursued by CE were the computing system as a whole, not just numerical computing. The handbook then is organized by distinguishing computing from computing systems. Six chapters (Chapters 2 to 7) are devoted to the former. They together present a review on the recent progresses in CE, as illustrated by the computation of rational expectations, general equilibrium, risk, and volatility. The subsequent 16 chapters are devoted to the computing-systemic view of CE, including natural-inspired computing (Chapters 8 to 12) and network, agent-based computing and neural computing (Chapters 13 to 23). In addition to providing alternative approaches to forecasting, investment strategies and risk management, etc., they enable us to have a 'natural' or more realistic description of the economy, starting from its decision makers; hence, market-design or policy-design issues involving different levels of the economy, be microscopic, mesoscopic and macroscopic, can be simultaneously addressed and coherently integrated. The handbook concludes with a chapter on what we may hope from CE by providing an in-depth review on the epistemological aspects of computation.

Economics --- Economics, Mathematical --- Finance --- 305.0 --- Mathematical economics --- Econometrics --- Mathematics --- Data processing --- Toegepaste econometrie en statistiek (algemene naslagwerken). Statistische onderzoekingen en studiën --- Methodology

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Social sciences (general) --- Economics --- Computer. Automation --- economie --- informatica --- sociale wetenschappen --- methodologieën

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Computational intelligence (CI), as an alternative to statistical and econometric approaches, has been applied to a wide range of economics and finance problems in recent years, for example to price forecasting and market efficiency. This book contains research ranging from applications in financial markets and business administration to various economics problems. Not only are empirical studies utilizing various CI algorithms presented, but so also are theoretical models based on computational methods. In addition to direct applications of computational intelligence, readers can also observe how these methods are combined with conventional analytical methods such as statistical and econometric models to yield preferred results. Chen, Wang, and Kuo have grouped the 12 contributions following their introductory chapter into applications of fuzzy logic, neural networks (including self-organizing maps and support vector machines), and evolutionary computation. All chapters were selected either by invitation or based on a careful selection and extension of best papers from the International Workshop on Computational Intelligence in Economics and Finance in 2005. Overall, the book offers researchers an excellent overview of current advances and applications of computational intelligence techniques to economics and finance problems.

Economics -- Data processing. --- Economics, Mathematical. --- Economics. --- Finance -- Data processing. --- Mechanical Engineering --- Engineering & Applied Sciences --- Business & Economics --- Economic Theory --- Computer Science --- Mechanical Engineering - General --- Information Technology --- Artificial Intelligence --- Economics --- Finance --- Data processing. --- Mathematical economics --- Mathematics --- Computer science. --- Finance. --- Artificial intelligence. --- Application software. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Finance, general. --- Computer Appl. in Administrative Data Processing. --- Information Systems Applications (incl. Internet). --- Econometrics --- Methodology --- Information systems. --- Artificial Intelligence. --- Funding --- Funds --- Currency question --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Artificial intelligence --- Financial applications. --- Data processing

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The special session on Decision Economics (DECON) is a scientific forum held annually and intended to share ideas, projects, research results, models and experiences associated with the complexity of behavioural decision processes and socio‐economic phenomena. DECON 2017 was held at the Polytechnic of Porto, ISEP, Portugal, as part of the 14th International Conference on Distributed Computing and Artificial Intelligence. For the second consecutive year, the Editors of this book have drawn inspiration from Herbert A. Simon’s immense body of work and argue that Simon precipitated something akin to a revolution in microeconomics focused on the concept of decision‐making. Further, it is worth noting that the recognition of relevant decision‐making takes place in a range of critical subject areas and research fields, including economics, finance, information systems, small and international business management, operations, and production. Therefore, decision‐making issues are of fundamental importance in all branches of economics addressed both deductively and inductively. Not surprisingly, the study of decision‐making has seen growing empirical research efforts in the economic literature over the last sixty years and, more recently, a variety of insightful cutting‐edge experimental, behavioural and computational approaches. Additionally, the awareness regarding generalizations and reductions to express economic concepts has led, on the one hand, to an increasing risk of spreading the language of mathematics as a rhetorical tool and, on the other hand, to an oversimplification and overlooking of some crucial details, especially when it comes to human decisions and, hence, economic behaviour. That awareness, however, has helped to produce an extraordinary volume of empirical research aimed at discovering how economic agents cope with complex decisions. In this sense, the international scientific community acknowledges Herbert A. Simon’s research endeavours to understand the processes involved in economic decision‐making and their implications for the advancement of economic professions. Within the field of decision‐making, indeed, Simon’s rejection of the standard decision‐making models used in neoclassical economics inspired social scientists worldwide to develop research programmes in order to study decision‐making empirically. The main achievements concern decision‐making for individuals, firms, markets, governments, institutions, and, last but not least, science and research.

Artificial intelligence --- Engineering. --- Artificial intelligence. --- Computational intelligence. --- Economic theory. --- Computational Intelligence. --- Economic Theory/Quantitative Economics/Mathematical Methods. --- Artificial Intelligence (incl. Robotics). --- Economic theory --- Political economy --- Social sciences --- Economic man --- Intelligence, Computational --- Soft computing --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Construction --- Industrial arts --- Technology --- Artificial Intelligence.