Deep Generative Modeling In Network Science With Applications To Public Policy Research
Download Deep Generative Modeling In Network Science With Applications To Public Policy Research PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Deep Generative Modeling In Network Science With Applications To Public Policy Research book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Deep Generative Modeling in Network Science with Applications to Public Policy Research
Network data are increasingly being used in quantitative, data-driven public policy research. These are typically very rich datasets that contain complex correlations and inter-dependencies. This richness both promises to be quite useful for policy research, while at the same time posing a challenge for the useful extraction of information from these datasets - a challenge which calls for new data analysis methods. In this report, we formulate a research agenda of key methodological problems whose solutions would enable new advances across many areas of policy research. We then review recent advances in applying deep learning to network data, and show how these meth- ods may be used to address many of the methodological problems we identified. We particularly emphasize deep generative methods, which can be used to generate realistic synthetic networks useful for microsimulation and agent-based models capable of informing key public policy ques- tions. We extend these recent advances by developing a new generative framework which applies to large social contact networks commonly used in epidemiological modeling. For context, we also compare and contrast these recent neural network-based approaches with the more tradi- tional Exponential Random Graph Models. Lastly, we discuss some open problems where more progress is needed.
Complex Governance Networks
What are the roles of governments and other actors in solving, or alleviating, collective action problems in today’s world? The traditional conceptual frameworks of public administration and public policy studies have become less relevant in answering this question. This book critically assesses traditional conceptual frameworks and proposes an alternative: a complex governance networks (CGN) framework. Advocating that complexity theory should be systematically integrated with foundational concepts of public administration and public policy, Göktuğ Morçöl begins by clarifying the component concepts of CGN and then addresses the implications of CGN for key issues in public administration and policy studies: effectiveness, accountability, and democracy. He illustrates the applicability of the CGN concepts with examples for the COVID-19 pandemic and metropolitan governance, particularly the roles of business improvement districts in governance processes. Morçöl concludes by discussing the implications of CGN for the convergence of public administration and public policy education and offering suggestions for future studies using the CGN conceptualization. Complex Governance Networks is essential reading for both scholars and advanced students of public policy, public administration, public affairs, and related areas.
Proceedings of the 2023 International Conference of The Computational Social Science Society of the Americas
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.