Jakub Bijak

Towards Bayesian Model-Based Demography

Agency, Complexity and Uncertainty in Migration Studies
Mitarbeit:Higham, Philip A.; Hilton, Jason; Hinsch, Martin; Nurse, Sarah; Prike, Toby; Reinhardt, Oliver; Smith, Peter W.F.; Uhrmacher, Adelinde M.; Warnke, Tom

• Gebundenes Buch

Produktbeschreibung

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 aresubsequently 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.

Produktdetails

• Methodos Series 17
• Verlag: European Research Council / Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-83038-0
• 1st ed. 2022
• Seitenzahl: 292
• Erscheinungstermin: 10. Dezember 2021
• Englisch
• Abmessung: 241mm x 160mm x 22mm
• Gewicht: 606g
• ISBN-13: 9783030830380
• ISBN-10: 3030830381
• Artikelnr.: 62157157

Autorenporträt

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).

Inhaltsangabe

Part I: Preliminaries: Chapter 1. Introduction.- Chapter 2. Uncertainty and complexity: towards model-based demography.- Part II: Elements of the modelling process.- Chapter 3. Principles and state of the art of agent-based migration modelling.- Chapter 4. Building a knowledge base for the model.- Chapter 5. Uncertainty quantification, model calibration and sensitivity.- Chapter 6. The boundaries of cognition and decision making.- Chapter 7. Agent-based modelling and simulation with domain-specific languages.- Part III: Model results, applications, and reflections.- Chapter 8. Towards more realistic models.- Chapter 9. Bayesian model-based approach: impact on science and policy.- Chapter 10. Open science, replicability, and transparency in modelling.- Chapter 11. Conclusions: towards a Bayesian modelling process.

Rezensionen

"The material collected by Jakub Bijak and his team constitutes a valuable resource for scholars interested in modelling individual decisions, not necessarily restricted to migration processes. ... Researchers who already gained some experience in social simulation will receive many inspirations for improving their own research and rise to the next level. In this way, this book has the potential to advance the art of modelling in the social sciences." (Thomas Fent, European Journal of Population, Vol. 38, 2022)