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001 978-3-030-83039-7
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020 _a9783030830397
_9978-3-030-83039-7
024 7 _a10.1007/978-3-030-83039-7
_2doi
050 4 _aHB848-3697
072 7 _aJHBD
_2bicssc
072 7 _aSOC006000
_2bisacsh
072 7 _aJHBD
_2thema
082 0 4 _a304.6
_223
100 1 _aBijak, Jakub.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aTowards Bayesian Model-Based Demography
_h[electronic resource] :
_bAgency, Complexity and Uncertainty in Migration Studies /
_cby Jakub Bijak.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aXXV, 263 p. 46 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aMethodos Series, Methodological Prospects in the Social Sciences,
_x2542-9892 ;
_v17
505 0 _aPart 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.
506 0 _aOpen Access
520 _aThis 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.
650 0 _aDemography.
650 0 _aPopulation.
650 0 _aSocial sciences
_xStatistical methods.
650 0 _aEmigration and immigration.
650 1 4 _aPopulation and Demography.
650 2 4 _aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.
650 2 4 _aHuman Migration.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030830380
776 0 8 _iPrinted edition:
_z9783030830403
776 0 8 _iPrinted edition:
_z9783030830410
830 0 _aMethodos Series, Methodological Prospects in the Social Sciences,
_x2542-9892 ;
_v17
856 4 0 _uhttps://doi.org/10.1007/978-3-030-83039-7
912 _aZDB-2-SLS
912 _aZDB-2-SXS
912 _aZDB-2-SOB
999 _c37936
_d37936