Towards Bayesian Model-Based Demography: Agency, Complexity and Uncertainty in Migration Studies: 17 (Methodos Series) - Softcover

Bijak, Jakub

 
9783030830410: Towards Bayesian Model-Based Demography: Agency, Complexity and Uncertainty in Migration Studies: 17 (Methodos Series)
Softcover
ISBN 10: 3030830411 ISBN 13: 9783030830410
Verlag: Springer, 2021

Inhaltsangabe

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, filling an important epistemological gap in migration studies, and social sciences more broadly.

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Über die Autorin bzw. den Autor

Jakub Bijak is Professor of Statistical Demography at the University of Southampton. He has background in economics (PhD 2008, Warsaw School of Economics) and over 20 years’ work experience in academia and international civil service. His research focuses on demographic uncertainty, population and migration models and forecasts, and the demography of armed conflict. He has been awarded the Allianz European Demographer Award (2015) and the Jerzy Z Holzer Medal (2007) for work on migration modelling. Leader of a European Research Council project “Bayesian agent-based population studies” (www.baps-project.eu), and a Horizon 2020 project “QuantMig: Quantifying Migration Scenarios for Better Policy” (www.quantmig.eu).

Von der hinteren Coverseite

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, filling an important epistemological gap in migration studies, and social sciences more broadly.

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Verlag
Springer
Erscheinungsdatum
2021
Sprache
Englisch
ISBN 10 
3030830411
ISBN 13 
9783030830410
Einband
Tapa blanda
Auflage
1
Anzahl der Seiten
292
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Weitere beliebte Ausgaben desselben Titels

9783030830380: Towards Bayesian Model-Based Demography: Agency, Complexity and Uncertainty in Migration Studies: 17 (Methodos Series, 17)

Vorgestellte Ausgabe

ISBN 10:  3030830381 ISBN 13:  9783030830380
Verlag: Springer, 2022
Hardcover