| 書目名稱 | Towards Bayesian Model-Based Demography | | 副標(biāo)題 | Agency, Complexity a | | 編輯 | Jakub Bijak | | 視頻video | http://file.papertrans.cn/927/926899/926899.mp4 | | 概述 | Presents a ground-breaking agent-based simulation model of migration.Connects behavioural and social theory with modelling and statistics.This book is available open access and free to read online.Dis | | 叢書名稱 | Methodos Series | | 圖書封面 |  | | 描述 | .This open access book presents a ground-breaking approach to developing micro-foundations for demography and migration studies. It offers a unique and novel methodology for creating empirically grounded agent-based models of international migration – one of the most uncertain population processes and a top-priority policy area. The book discusses in detail the process of building a simulation model of migration, based on a population of intelligent, cognitive agents, their networks and institutions, all interacting with one another. The proposed model-based approach integrates behavioural and social theory with formal modelling, by embedding the interdisciplinary modelling process within a wider inductive framework based on the Bayesian statistical reasoning. Principles of uncertainty quantification are used to devise innovative computer-based simulations, and to learn about modelling the simulated individuals and the way they make decisions. The identified knowledge gaps are subsequently filled with information from dedicated laboratory experiments on cognitive aspects of human decision-making under uncertainty. In this way, the models are built iteratively, from the bottom up, f | | 出版日期 | Book‘‘‘‘‘‘‘‘ 2022 | | 關(guān)鍵詞 | Open access; Agent-based modelling; Bayesian demography; Migration modelling; Model-based approaches; Unc | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-83039-7 | | isbn_softcover | 978-3-030-83041-0 | | isbn_ebook | 978-3-030-83039-7Series ISSN 1572-7750 Series E-ISSN 2542-9892 | | issn_series | 1572-7750 | | copyright | The Editor(s) (if applicable) and The Author(s) 2022 |
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