Proceedings of the 2023 International Conference of The Computational Social Science Society of the Americas

2024
ISBN: 978-3-031-64192-3
Verlag: Springer Nature Switzerland

Buch, Englisch, 316 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 656 g

Reihe: Springer Proceedings in Complexity

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 2023 annual conference of the Computational Social Science Society of the Americas (CSSSA). This conference is held in Santa Fe, New Mexico, November 2–5, 2023. 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.

Zielgruppe

Research

Autoren/Hrsg.

Krejci, Caroline

Yang, Zining

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Numerische Mathematik
• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Geistes- und Sozialwissenschaften
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftstheorie, Wirtschaftsphilosophie

Inhaltsverzeichnis

Understanding Emergence Through Agent-Based Computation.- Agent-based foraging or When imperfect unbiased knowledge yields systematic deviation from ideally rational outcomes.- The differential ‘wisdom of crowds’: Using ChatGPT and the Twitter API to interrogate entire cultures.- Statistical Discrimination with Affirmative Action using Agent-based Modeling.

Über Autor(innen)

Dr. Zining Yang is Senior Manager at Southern California Edison. She also works as Adjunct Professor at California State Polytechnic University, Pomona, 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. She received her Ph.D. in Computational and Applied Mathematics and Political Economy from Claremont Graduate University. Her research interests include modeling and simulation, complex adaptive systems, agent-based models, network analysis, data analytics, and machine learning. She has published numerous times in the fields of computer science, economics, public policy, and political science. She has been identified as 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 ofthe People’s Republic of China, International Social Computing, Behavioral Modeling and Prediction, and the International Institute of Informatics and Systemics.