Proceedings of the 2020 Conference of the Computational Social Science Society of the Americas - Couverture rigide

Couverture rigide

ISBN 10 : 3030834174 ISBN 13 : 9783030834173

Editeur : Springer Nature Switzerland AG, 2022

Synopsis

<div><p>This book is comprised of the latest research into CSS methods, uses, and results, as presented at the 2020 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. </p>The above-mentioned conference was held virtually, October 8 – 11, 2020. What follows is a diverse representation of new results and approaches to using the tools of CSS and agent-based modeling (ABM) in exploring complex phenomena across many different domains.&nbsp; Readers will therefore not only have the results of these specific projects upon which to build, along with a wealth of case-study examples that can serve as meaningful exemplars for new research projects and activities, they will also gain a greater appreciation for the broad scope of CSS.<p></p><br></div>

A propos du livre

À propos de l'auteur

<p><b>Dr. Zining Yang</b> is Senior Manager at Southern California Edison. She also works as Clinical Professor at Claremont Graduate University and Associate Director at the TransResearch Consortium. She sits on the Board of the Computational Social Science Society of the Americas (CSSSA), and serves as Scientific Advisory Board Member for Human Factors and Simulations. Dr. Yang received her Ph.D. in Computational and Applied Mathematics and Political Economy from Claremont Graduate University in 2015. Her research interests include Data Analytics, Machine Learning, Modeling and Simulation, Complex Adaptive Systems, Agent-Based Models, and Network Analysis. Dr. Yang has been published numerous times in the fields of Computer Science, Economics, Public Policy, and Political Science. She has been identified as an outstanding researcher by the government, worked on a National Science Foundation-sponsored project, and won multiple awards from various organizations, including the Ministry of Education of the People’s Republic of China; International Social Computing, Behavioral Modeling and Prediction; and the International Institute of Informatics and Systemics.</p><p>&nbsp;<b>Dr. Elizabeth von Briesen</b> is an Assistant Professor of Computer Science at Elon University, and is a member of the board of the Computational Social Science Society of the Americas. She received her Ph.D. in Computing & Informatics from the University of North Carolina at Charlotte in 2020. Her research interests are focused on the study of complex adaptive systems using computational techniques, particularly with respect to social systems experiencing identity-based conflict. She primarily works with agent-based models, and performs data mining and sentiment analysis to inform those simulations. Finally, in her current position, Dr. von Briesen strives to contribute toward an evolving undergraduate computer science experience through research, service, and high-quality, innovative teaching.</p><p></p>

À propos de la quatrième de couverture

This book is comprised of the latest research into CSS methods, uses, and results, as presented at the 2020 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.

The above-mentioned conference was held virtually, October 8 - 11, 2020. What follows is a diverse representation of new results and approaches to using the tools of CSS and agent-based modeling (ABM) in exploring complex phenomena across many different domains. Readers will therefore not only have the results of these specific projects upon which to build, along with a wealth of case-study examples that can serve as meaningful exemplars for new research projects and activities, they will also gain a greater appreciation for the broad scope of CSS.

Détails de l'édition

• Éditeur: Springer Nature Switzerland AG
• Date d'édition: 2022
• Langue: anglais
• ISBN 10: 3030834174
• ISBN 13: 9783030834173
• Reliure: Relié
• Nombre de pages: 279
• Éditeur: Yang Zining, Von Briesen Elizabeth
• Coordonnées du fabricant: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Allemagne