| computational social science degree: Computational and Mathematical Modeling in the Social Sciences Scott de Marchi, 2005-08-15 Offers an overview of mathematical modeling concentrating on game theory, statistics and computational modeling. |
| computational social science degree: Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas Zining Yang, Elizabeth von Briesen, 2021-10-02 This book presents the latest research into CSS methods, uses, and results, as presented at the 2019 annual conference of the CSSSA. This conference was held in Santa Fe, New Mexico, October 24 – 27, 2019, at the Drury Plaza Hotel. What follows is a diverse representation of new results and approaches for using the tools of CSS and agent-based modeling (ABM) for exploring complex phenomena across many different domains. Readers will therefore not only have the results of these specific projects on which to build, but will also gain a greater appreciation for the broad scope of CSS, and have a wealth of case-study examples that can serve as meaningful exemplars for new research projects and activities. The Computational Social Science Society of the Americas (CSSSA) is a professional society that aims to advance the field of CSS in all its areas, from fundamental principles to real-world applications, by holding conferences and workshops, promoting standards of scientific excellence in research and teaching, and publishing novel research findings. |
| computational social science degree: Computational Social Science Wei Luo, Maria Ciurea, Santosh Kumar, 2021-02-18 Selected papers from the International Conference on New Computational Social Science, focusing on the following five aspects: Big data acquisition and analysis, Integration of qualitative research and quantitative research, Sociological Internet experiment research, Application of ABM simulation method in Sociology Research, Research and development of new social computing tools. With the rapid development of information technology, especially sweeping progress in the Internet of things, cloud computing, social networks, social media and big data, social computing, as a data-intensive science, is an emerging field that leverages the capacity to collect and analyze data with an unprecedented breadth, depth and scale. It represents a new computing paradigm and an interdisciplinary field of research and application. A broad comprehension of major topics involved in social computing is important for both scholars and practitioners. This proceedings presents and discusses key concepts and analyzes the state-of-the-art of the field. The conference not only gave insights on social computing, but also affords conduit for future research in the field. Social computing has two distinct trends: One is on the social science issues, such as computational social science, computational sociology, social network analysis, etc; The other is on the use of computational techniques. Finally some new challenges ahead are summarized, including interdisciplinary cooperation and training, big data sharing for scientific data mashups, and privacy protect. |
| computational social science degree: Computational Social Psychology Robin R. Vallacher, Stephen J. Read, Andrzej Nowak, 2017-05-25 Computational Social Psychology showcases a new approach to social psychology that enables theorists and researchers to specify social psychological processes in terms of formal rules that can be implemented and tested using the power of high speed computing technology and sophisticated software. This approach allows for previously infeasible investigations of the multi-dimensional nature of human experience as it unfolds in accordance with different temporal patterns on different timescales. In effect, the computational approach represents a rediscovery of the themes and ambitions that launched the field over a century ago. The book brings together social psychologists with varying topical interests who are taking the lead in this redirection of the field. Many present formal models that are implemented in computer simulations to test basic assumptions and investigate the emergence of higher-order properties; others develop models to fit the real-time evolution of people’s inner states, overt behavior, and social interactions. Collectively, the contributions illustrate how the methods and tools of the computational approach can investigate, and transform, the diverse landscape of social psychology. |
| computational social science degree: Doing Computational Social Science John McLevey, 2021-12-15 Computational approaches offer exciting opportunities for us to do social science differently. This beginner’s guide discusses a range of computational methods and how to use them to study the problems and questions you want to research. It assumes no knowledge of programming, offering step-by-step guidance for coding in Python and drawing on examples of real data analysis to demonstrate how you can apply each approach in any discipline. The book also: Considers important principles of social scientific computing, including transparency, accountability and reproducibility. Understands the realities of completing research projects and offers advice for dealing with issues such as messy or incomplete data and systematic biases. Empowers you to learn at your own pace, with online resources including screencast tutorials and datasets that enable you to practice your skills and get up to speed. For anyone who wants to use computational methods to conduct a social science research project, this book equips you with the skills, good habits and best working practices to do rigorous, high quality work. |
| computational social science degree: Quantitative Social Science Kosuke Imai, Lori D. Bougher, 2021-03-16 Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a translation of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place-- |
| computational social science degree: Computational Social Science R. Michael Alvarez, 2016-03-07 Quantitative research in social science research is changing rapidly. Researchers have vast and complex arrays of data with which to work: we have incredible tools to sift through the data and recognize patterns in that data; there are now many sophisticated models that we can use to make sense of those patterns; and we have extremely powerful computational systems that help us accomplish these tasks quickly. This book focuses on some of the extraordinary work being conducted in computational social science - in academia, government, and the private sector - while highlighting current trends, challenges, and new directions. Thus, Computational Social Science showcases the innovative methodological tools being developed and applied by leading researchers in this new field. The book shows how academics and the private sector are using many of these tools to solve problems in social science and public policy. |
| computational social science degree: Complex Adaptive Systems John H. Miller, Scott E. Page, 2009-11-28 This book provides the first clear, comprehensive, and accessible account of complex adaptive social systems, by two of the field's leading authorities. Such systems--whether political parties, stock markets, or ant colonies--present some of the most intriguing theoretical and practical challenges confronting the social sciences. Engagingly written, and balancing technical detail with intuitive explanations, Complex Adaptive Systems focuses on the key tools and ideas that have emerged in the field since the mid-1990s, as well as the techniques needed to investigate such systems. It provides a detailed introduction to concepts such as emergence, self-organized criticality, automata, networks, diversity, adaptation, and feedback. It also demonstrates how complex adaptive systems can be explored using methods ranging from mathematics to computational models of adaptive agents. John Miller and Scott Page show how to combine ideas from economics, political science, biology, physics, and computer science to illuminate topics in organization, adaptation, decentralization, and robustness. They also demonstrate how the usual extremes used in modeling can be fruitfully transcended. |
| computational social science degree: Introduction to Computational Social Science Claudio Cioffi-Revilla, 2017-06-29 This textbook provides a comprehensive and reader-friendly introduction to the field of computational social science (CSS). Presenting a unified treatment, the text examines in detail the four key methodological approaches of automated social information extraction, social network analysis, social complexity theory, and social simulation modeling. This updated new edition has been enhanced with numerous review questions and exercises to test what has been learned, deepen understanding through problem-solving, and to practice writing code to implement ideas. Topics and features: contains more than a thousand questions and exercises, together with a list of acronyms and a glossary; examines the similarities and differences between computers and social systems; presents a focus on automated information extraction; discusses the measurement, scientific laws, and generative theories of social complexity in CSS; reviews the methodology of social simulations, covering both variable- and object-oriented models. |
| computational social science degree: Computational Frameworks for Political and Social Research with Python Josh Cutler, Matt Dickenson, 2020-04-22 This book is intended to serve as the basis for a first course in Python programming for graduate students in political science and related fields. The book introduces core concepts of software development and computer science such as basic data structures (e.g. arrays, lists, dictionaries, trees, graphs), algorithms (e.g. sorting), and analysis of computational efficiency. It then demonstrates how to apply these concepts to the field of political science by working with structured and unstructured data, querying databases, and interacting with application programming interfaces (APIs). Students will learn how to collect, manipulate, and exploit large volumes of available data and apply them to political and social research questions. They will also learn best practices from the field of software development such as version control and object-oriented programming. Instructors will be supplied with in-class example code, suggested homework assignments (with solutions), and material for practical lab sessions. |
| computational social science degree: Computational Social Science and Complex Systems J. Kertész, R.N. Mantegna, S. Miccichè, 2019-11-20 For many years, the development of large-scale quantitative social science was hindered by a lack of data. Traditional methods of data collection like surveys were very useful, but were limited. The situation has of course changed with the development of computing and information communication technology, and we now live in a world of data deluge, where the question has become how to extract important information from the plethora of data that can be accessed. Big Data has made it possible to study societal questions which were once impossible to deal with, but new tools and new multidisciplinary approaches are required. Physicists, together with economists, sociologists, computer scientists, etc. have played an important role in their development. This book presents the 9 lectures delivered at the CCIII Summer Course Computational Social Science and Complex Systems, held as part of the International School of Physics Enrico Fermi in Varenna, Italy, from 16-21 July 2018. The course had the aim of presenting some of the recent developments in the interdisciplinary fields of computational social science and econophysics to PhD students and young researchers, with lectures focused on recent problems investigated in computational social science. Addressing some of the basic questions and many of the subtleties of the emerging field of computational social science, the book will be of interest to students, researchers and advanced research professionals alike. |
| computational social science degree: Pathways Between Social Science and Computational Social Science Tamás Rudas, Gábor Péli, 2021-01-22 This volume shows that the emergence of computational social science (CSS) is an endogenous response to problems from within the social sciences and not exogeneous. The three parts of the volume address various pathways along which CSS has been developing from and interacting with existing research frameworks. The first part exemplifies how new theoretical models and approaches on which CSS research is based arise from theories of social science. The second part is about methodological advances facilitated by CSS-related techniques. The third part illustrates the contribution of CSS to traditional social science topics, further attesting to the embedded nature of CSS. The expected readership of the volume includes researchers with a traditional social science background who wish to approach CSS, experts in CSS looking for substantive links to more traditional social science theories, methods and topics, and finally, students working in both fields. |
| computational social science degree: Handbook of Computational Social Science, Volume 1 Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu, Lars E Lyberg, 2021-11-10 The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field but also encourages growth in new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientifi c and engineering sectors. |
| computational social science degree: Opportunities and Challenges for Computational Social Science Methods Abanoz, Enes, 2022-03-18 We are living in a digital era in which most of our daily activities take place online. This has created a big data phenomenon that has been subject to scientific research with increasingly available tools and processing power. As a result, a growing number of social science scholars are using computational methods for analyzing social behavior. To further the area, these evolving methods must be made known to sociological research scholars. Opportunities and Challenges for Computational Social Science Methods focuses on the implementation of social science methods and the opportunities and challenges of these methods. This book sheds light on the infrastructure that should be built to gain required skillsets, the tools used in computational social sciences, and the methods developed and applied into computational social sciences. Covering topics like computational communication, ecological cognition, and natural language processing, this book is an essential resource for researchers, data scientists, scholars, students, professors, sociologists, and academicians. |
| computational social science degree: The Fuzzy and the Techie Scott Hartley, 2018 A leading venture capitalist offers surprising revelations on who will be driving innovation in the years to come. |
| computational social science degree: Political Communication in China Wenfang Tang, Shanto Iyengar, 2013-09-13 It is widely recognised that the Chinese Communist Party (CCP) uses the media to set the agenda for political discourse, propagate official policies, monitor public opinion, and rally regime support. State agencies in China control the full spectrum of media programming, either through ownership or the power to regulate. Political Communication in China examines the two factors which have contributed to the rapid development of media infrastructure in China: technology and commercialization. Economic development led to technological advancement, which in turn brought about the rapid modernization of all forms of communication, from ‘old’ media such as television to the Internet, cell phones, and satellite communications. This volume examines how these recent developments have affected the relationship between the CCP and the mass media as well as the implications of this evolving relationship for understanding Chinese citizens’ media use, political attitudes, and behaviour. The chapters in this book represent a diverse range of research methods, from surveys, content analysis, and field interviews to the manipulation of aggregate statistical data. The result is a lively debate which creates many opportunities for future research into the fundamental question of convergence between political and media regimes. This book was originally published as a special issue of the journal Political Communication. |
| computational social science degree: Behavioral Computational Social Science Riccardo Boero, 2015-07-21 This book is organized in two parts: the first part introduces the reader to all the concepts, tools and references that are required to start conducting research in behavioral computational social science. The methodological reasons for integrating the two approaches are also presented from the individual and separated viewpoints of the two approaches.The second part of the book, presents all the advanced methodological and technical aspects that are relevant for the proposed integration. Several contributions which effectively merge the computational and the behavioral approaches are presented and discussed throughout |
| computational social science degree: Handbook of Computational Social Science, Volume 2 Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu, Lars Lyberg, 2021-11-10 The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors. |
| computational social science degree: Computational Social Science Dan Dobrota, Roshan Chitrakar, Maria Ciurea, Liliana Mâţă, Wei Luo, 2022-07-01 The proceedings publish selected papers from the 2nd International Conference on New Computational Social Science, focusing on the following five aspects: Big data acquisition and analysis, Integration of qualitative research and quantitative research, Sociological Internet experiment research, Application of ABM simulation method in Sociology Research, Research and development of new social computing tools. With the rapid development of information technology, especially sweeping progress in the Internet of things, cloud computing, social networks, social media and big data, social computing, as a data-intensive science, is an emerging field that leverages the capacity to collect and analyze data with an unprecedented breadth, depth and scale. It represents a new computing paradigm and an interdisciplinary field of research and application. A broad comprehension of major topics involved in social computing is important for both scholars and practitioners. This proceedings presents and discusses key concepts and analyzes the state-of-the-art of the field. The conference not only gave insights on social computing, but also affords conduit for future research in the field. Social computing has two distinct trends: One is on the social science issues, such as computational social science, computational sociology, social network analysis, etc; the other is on the use of computational techniques. Finally, some new challenges ahead are summarized, including interdisciplinary cooperation and training, big data sharing for scientific data mashups, and privacy protect. |
| computational social science degree: Sociological Foundations of Computational Social Science Yoshimichi Sato, |
| computational social science degree: Computational Social Science in the Age of Big Data Martin Welker, Cathleen M. Stützer, Marc Egger, 2018-02-19 Der Sammelband Computational Social Science in the Age of Big Data beschäftigt sich mit Konzepten, Methoden, Tools und Anwendungen (automatisierter) datengetriebener Forschung mit sozialwissenschaftlichem Hintergrund. Der Fokus des Bandes liegt auf der Etablierung der Computational Social Science (CSS) als aufkommendes Forschungs- und Anwendungsfeld. Es werden Beiträge international namhafter Autoren präsentiert, die forschungs- und praxisrelevante Themen dieses Bereiches besprechen. Die Herausgeber forcieren dabei einen interdisziplinären Zugang zum Feld, der sowohl Online-Forschern aus der Wissenschaft wie auch aus der angewandten Marktforschung einen Einstieg bietet. |
| computational social science degree: Computational Social Science Xiaogang Wu, Yongjun Zhang, Tianji Cai, 2024-06-07 This edited collection provides an overview of the recent developments in computational social science related to China studies and presents interdisciplinary empirical work from diverse scholars on culture, public opinion, and education using advanced computational methods and big data. The topics covered in this book include the surge of anti-China sentiment amid the COVID-19 pandemic, the nuances of E-governance, public opinion, authoritarian reactions, artistic innovation, and educational inequality. The chapters in this book provide important insights into how computational social science can be applied generally, but also underscore the importance of combining conventional sociological research with contemporary computational methods in the context of China studies. This cutting-edge volume will be valuable resource for researchers, scholars and practitioners of Sociology, China Studies and for those interested in computational approaches to the social sciences. The chapters in this book were originally published in Chinese Sociological Review. |
| computational social science degree: Proceedings of the 4th International Conference on New Computational Social Science (ICNCSS 2024) ROSHAN CHITRAKAR; MATA LILIANA; DAN DOBROTA; CHEW., Roshan Chitrakar, 2024 |
| computational social science degree: Trends in Computational Social Choice Ulle Endriss, 2017 Computational social choice is concerned with the design and analysis of methods for collective decision making. It is a research area that is located at the interface of computer science and economics. The central question studied in computational social choice is that of how best to aggregate the individual points of view of several agents, so as to arrive at a reasonable compromise. Examples include tallying the votes cast in an election, aggregating the professional opinions of several experts, and finding a fair manner of dividing a set of resources amongst the members of a group -- Back cover. |
| computational social science degree: The Science of Science Dashun Wang, Albert-László Barabási, 2021-03-25 This is the first comprehensive overview of the exciting field of the 'science of science'. With anecdotes and detailed, easy-to-follow explanations of the research, this book is accessible to all scientists, policy makers, and administrators with an interest in the wider scientific enterprise. |
| computational social science degree: The Future of Business Schools Rico J. Baldegger, Ayman El Tarabishy, David B. Audretsch, Dafna Kariv, Katia Passerini, Wee-Liang Tan, 2022-11-18 Are business schools on the wrong track? For many years, business schools enjoyed rising enrollments, positive media attention, and growing prestige in the business world. However, due to the disruption of Covid-19, many previously ignored issues relating to MBA programs resurfaced. As a result, MBA programs now face lower enrollments and intense criticism for being deficient in preparing future business leaders and ignoring essential topics like ethics, sustainability, and diversity and inclusion. The Future of Business Schools discusses these issues in the context of three critical areas: complexity, sustainability, and destiny |
| computational social science degree: Computational Modeling Charles S. Taber, Richard J. Timpone, 1996-03-21 In this introduction to computational modelling the authors provide a concise description of computational methods, including dynamic simulation, knowledge-based models and machine learning, as a single broad class of research tools. |
| computational social science degree: Advances in Computational Social Science Shu-Heng Chen, Takao Terano, Ryuichi Yamamoto, Chung-Ching Tai, 2014-05-22 This volume is a post-conference publication of the 4th World Congress on Social Simulation (WCSS), with contents selected from among the 80 papers originally presented at the conference. WCSS is a biennial event, jointly organized by three scientific communities in computational social science, namely, the Pacific-Asian Association for Agent-Based Approach in Social Systems Sciences (PAAA), the European Social Simulation Association (ESSA), and the Computational Social Science Society of the Americas (CSSSA). It is, therefore, currently the most prominent conference in the area of agent-based social simulation. The papers selected for this volume give a holistic view of the current development of social simulation, indicating the directions for future research and creating an important archival document and milestone in the history of computational social science. Specifically, the papers included here cover substantial progress in artificial financial markets, macroeconomic forecasting, supply chain management, bank networks, social networks, urban planning, social norms and group formation, cross-cultural studies, political party competition, voting behavior, computational demography, computational anthropology, evolution of languages, public health and epidemics, AIDS, security and terrorism, methodological and epistemological issues, empirical-based agent-based modeling, modeling of experimental social science, gaming simulation, cognitive agents, and participatory simulation. Furthermore, pioneering studies in some new research areas, such as the theoretical foundations of social simulation and categorical social science, also are included in the volume. |
| computational social science degree: Empty Suffering Domonkos Sik, 2021-11-19 Interdisciplinary in approach, this book combines philosophy, sociology, history and psychology in the analysis of contemporary forms of suffering. With attention to depression, anxiety, chronic pain and addiction, it examines both particular forms of suffering and takes a broad view of their common features, so as to offer a comprehensive and parallel view both of the various forms of suffering and the treatments commonly applied to them. Highlighting the challenges and distortions of the available treatments and identifying these as contributory factors to the overall problem of contemporary suffering, Empty Suffering promises to widen the horizon of therapeutic interventions and social policies. As such, it will appeal to scholars across the social sciences and humanities with interests in mental health and disorder, social theory and social pathologies. |
| computational social science degree: At the Crossroads: Lessons and Challenges in Computational Social Science Javier Borge-Holthoefer, Yamir Moreno, Taha Yasseri, 2016-11-29 The interest of physicists in economic and social questions is not new: for over four decades, we have witnessed the emergence of what is called nowadays “sociophysics” and “econophysics”, vigorous and challenging areas within the wider “Interdisciplinary Physics”. With tools borrowed from Statistical Physics and Complexity, this new area of study have already made important contributions, which in turn have fostered the development of novel theoretical foundations in Social Science and Economics, via mathematical approaches, agent-based modelling and numerical simulations. From these foundations, Computational Social Science has grown to incorporate as well the empirical component --aided by the recent data deluge from the Web 2.0 and 3.0--, closing in this way the experiment-theory cycle in the best tradition of Physics. |
| computational social science degree: Simulating Societal Change Peter Davis, 2019 This book presents a method for creating a working model of society, using data systems and simulation techniques, that can be used for testing propositions of scientific and policy nature. The model is based on the example of New Zealand, but will be applicable to other countries. It is expected that collaborators in other countries can emulate this example with their data systems for teaching and policy purposes, producing a cross-national collaboratory. This enterprise will evolve with, and to a degree independently of, the book itself, with a supporting website as well as teaching and scientific initiatives. Readers of this text will, for the first time, have a simulation-based working model of society that can be interrogated for policy and substantive purposes. This book will appeal to researchers and professionals from various disciplines working within the social sciences, particularly on matters of demography and public policy. |
| computational social science degree: Six Degrees: The Science of a Connected Age Duncan J. Watts, 2004-01-27 Watts, one of the principal architects of network theory, sets out to explain the innovative research that he and other scientists are spearheading to create a blueprint of this connected planet. |
| computational social science degree: Handbook of Research on Agent-Based Societies: Social and Cultural Interactions Trajkovski, Goran, Collins, Samuel G., 2009-02-28 This volume addresses a variety of issues, in particular the emergence of societal phenomena in the interactions of systems of agents (software, robot or human)--Provided by publisher. |
| computational social science degree: Leadership in Science and Technology: A Reference Handbook William Sims Bainbridge, 2011-10-20 This 2-volume set within the SAGE Reference Series on Leadership tackles issues relevant to leadership in the realm of science and technology. To encompass the key topics in this arena, this handbook features 100 topics arranged under eight headings. Volume 1 concentrates on general principles of science and technology leadership and includes sections on social-scientific perspectives on S&T leadership; key scientific concepts about leading and innovating in S&T; characteristics of S&T leaders and their environments; and strategies, tactics, and tools of S&T leadership. Volume 2 provides case studies of leadership in S&T, with sections considering leadership in informal communities of scientists and engineers; leadership in government projects and research initiatives; leadership in industry research, development, and innovation; and finally, leadership in education and university-based research. By focusing on key topics within 100 brief chapters, this unprecedented reference resource offers students more detailed information and depth of discussion than typically found in an encyclopedia entry but not as much jargon, detail or density as in a journal article or a research handbook chapter. Entries are written in language and style that is broadly accessible, and each is followed by cross-references and a brief bibliography and further readings. A detailed index and an online version of the work enhances accessibility for today′s student audience. |
| computational social science degree: Proceedings of the 2021 Conference of The Computational Social Science Society of the Americas Zining Yang, Elizabeth von Briesen, 2022-03-28 This book contains a selection of the latest research in the field of Computational Social Science (CSS) methods, uses, and results, as presented at the 2021 annual conference of the Computational Social Science Society of the Americas (CSSSA). Computational social science (CSS) is the science that investigates social and behavioral dynamics through social simulation, social network analysis, and social media analysis. The CSSSA is a professional society that aims to advance the field of computational social science in all areas, including basic and applied orientations, by holding conferences and workshops, promoting standards of scientific excellence in research and teaching, and publishing research findings and results. |
| computational social science degree: Handbook of Statistical Analysis and Data Mining Applications Ken Yale, Robert Nisbet, Gary D. Miner, 2017-11-09 Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. - Includes input by practitioners for practitioners - Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models - Contains practical advice from successful real-world implementations - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions - Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications |
| computational social science degree: Text as Data Justin Grimmer, Margaret E. Roberts, Brandon M. Stewart, 2022-03-29 A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry |
| computational social science degree: Artificial Intelligence with Python Prateek Joshi, 2017-01-27 Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application. |
| computational social science degree: Big Data and Social Science Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, Julia Lane, 2016-08-10 Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website. |
| computational social science degree: Handbook of Social Computing Peter A. Gloor, Francesca Grippa, Andrea Fronzetti Colladon, Aleksandra Przegalinska, 2024-03-14 Responding to the increasingly blurred boundaries between humans and technology, this innovative Handbook reveals the intricate patterns of interaction between individuals, machines, and organizations. Using cutting-edge data and analysis, expert contributors provide new insight into the rapidly growing digitalization of society. |
COMPUTATIONAL definition | Cambridge English Dictionary
COMPUTATIONAL meaning: 1. involving the calculation of answers, amounts, results, etc.: 2. using computers to study…. Learn more.
COMPUTATIONAL Definition & Meaning - Merriam-Webster
The meaning of COMPUTATION is the act or action of computing : calculation. How to use computation in a sentence.
Computation - Wikipedia
Mechanical or electronic devices (or, historically, people) that perform computations are known as computers. Computer science is an academic field that involves the …
Computational science - Wikipedia
Computational science, also known as scientific computing, technical computing or scientific computation (SC), is a division of science, and more specifically the Computer Sciences, which uses advanced computing …
Computational - Definition, Meaning & Synonyms - Vocabulary.com
Computational is an adjective referring to a system of calculating or "computing," or, more commonly today, work involving computers. …
COMPUTATIONAL definition | Cambridge English Dictionary
COMPUTATIONAL meaning: 1. involving the calculation of answers, amounts, results, etc.: 2. using computers to study…. Learn more.
COMPUTATIONAL Definition & Meaning - Merriam-Webster
The meaning of COMPUTATION is the act or action of computing : calculation. How to use computation in a sentence.
Computation - Wikipedia
Mechanical or electronic devices (or, historically, people) that perform computations are known as computers. Computer science is an academic field that involves the study of computation.
Computational science - Wikipedia
Computational science, also known as scientific computing, technical computing or scientific computation (SC), is a division of science, and more specifically the Computer Sciences, which …
Computational - Definition, Meaning & Synonyms - Vocabulary.com
Computational is an adjective referring to a system of calculating or "computing," or, more commonly today, work involving computers. Tasks with a lot of computational steps are best …
COMPUTATIONAL definition in American English - Collins Online …
Computational means using computers. Students may pursue research in any aspect of computational linguistics. Collins COBUILD Advanced Learner’s Dictionary. Copyright © …
Computational - definition of computational by ... - The Free …
Define computational. computational synonyms, computational pronunciation, computational translation, English dictionary definition of computational. n. 1. a. The act or process of …
COMPUTATIONAL - Definition & Translations | Collins English …
Discover everything about the word "COMPUTATIONAL" in English: meanings, translations, synonyms, pronunciations, examples, and grammar insights - all in one comprehensive guide.
What is computational thinking? - Introduction to computational …
Learn about the four cornerstones of computational thinking including decomposition, pattern recognition, abstraction and algorithms.
Computational Definition & Meaning - YourDictionary
Computational definition: Of or relating to
computation.
