Sociological Foundations of Computational Social Science
Produktdetaljer
Om bidragsyterne
Yoshimichi Sato is professor of sociology at the Faculty of Psychology, Kyoto University of Advanced Science. His research areas in sociology include the study of social capital, social inequality, and theories of social change. He applies game theory, agent-based models, and statistical models to these topics. He has been active in the international arena. He was a visiting scholar at the University of Chicago and Cornell University and served as a chair of Session on Rationality and Society of the American Sociological Association and as a president of Research Committee 45 (Rational Choice) and an executive committee member of the International Sociological Association. He was a co-editor of Sociology Section of International Encyclopedia of Social and Behavioral Sciences, 2nd Edition, which received PROSW awards in 2016.
Hiroki Takikawa is an Associate Professor at Graduate School of Humanities and Sociology, the University of Tokyo, Japan. He earned his PhD andBA in Sociology from the University of Tokyo. His research area includes mathematical sociology, social network analysis, and computational social science. He is currently studying the mechanism of social division and political polarization through large-scale data analysis. His research has been published in academic journals including PLOS ONE, Quality and Quantity and conferences proceedings such as IEEE Big data.
This book provides solid sociological foundations to computational social science (CSS). CSS is an emerging research field, and many books with those words in the title are on the market. However, CSS has not become mainstream in sociology, for which there are two reasons. First, CSS does not necessarily solve major research questions in sociology. Second, its sociological foundations are weak. These two reasons are interrelated—that is, CSS cannot solve major research questions because its sociological foundations are weak. Thus, even if it tries to solve those questions, its approaches seem to mainstream sociologists to miss the point. To resolve that shortcoming, this book fills the gap between CSS and sociology, shows that CSS can solve major research questions in sociology, and advances sociology by introducing to it theories and methodologies of CSS.
This book provides solid sociological foundations to computational social science (CSS). To resolve that shortcoming, this book fills the gap between CSS and sociology, shows that CSS can solve major research questions in sociology, and advances sociology by introducing to it theories and methodologies of CSS.
Produktdetaljer
Om bidragsyterne
Yoshimichi Sato is professor of sociology at the Faculty of Psychology, Kyoto University of Advanced Science. His research areas in sociology include the study of social capital, social inequality, and theories of social change. He applies game theory, agent-based models, and statistical models to these topics. He has been active in the international arena. He was a visiting scholar at the University of Chicago and Cornell University and served as a chair of Session on Rationality and Society of the American Sociological Association and as a president of Research Committee 45 (Rational Choice) and an executive committee member of the International Sociological Association. He was a co-editor of Sociology Section of International Encyclopedia of Social and Behavioral Sciences, 2nd Edition, which received PROSW awards in 2016.
Hiroki Takikawa is an Associate Professor at Graduate School of Humanities and Sociology, the University of Tokyo, Japan. He earned his PhD andBA in Sociology from the University of Tokyo. His research area includes mathematical sociology, social network analysis, and computational social science. He is currently studying the mechanism of social division and political polarization through large-scale data analysis. His research has been published in academic journals including PLOS ONE, Quality and Quantity and conferences proceedings such as IEEE Big data.