Springer Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas (Springer Proceedings in Complexity)

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. From the Back Cover 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. About the Author Dr. Zining Yang 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. Dr. Elizabeth von Briesen 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