TY  - BOOK
AU  - Bijak,Jakub
ED  - SpringerLink (Online service)
TI  - Towards Bayesian Model-Based Demography: Agency, Complexity and Uncertainty in Migration Studies 
T2  - Methodos Series, Methodological Prospects in the Social Sciences,
SN  - 9783030830397
AV  - HB848-3697 
U1  - 304.6 23
PY  - 2022///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Demography
KW  - Population
KW  - Social sciences
KW  - Statistical methods
KW  - Emigration and immigration
KW  - Population and Demography
KW  - Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
KW  - Human Migration
N1  - 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; Open Access
N2  - 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
UR  - https://doi.org/10.1007/978-3-030-83039-7
ER  -