When forecasting and foresight meet data and innovation: toward a taxonomy of anticipatory methods for migration policy

The various global refugee and migration events of the last few years underscore the need for advancing anticipatory strategies in migration policy. The struggle to manage large inflows (or outflows) highlights the demand for proactive measures based on a sense of the future. Anticipatory methods, r...

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Main Authors: Sara Marcucci, Stefaan Verhulst, María Esther Cervantes
Format: Article
Language:English
Published: Cambridge University Press 2025-01-01
Series:Data & Policy
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Online Access:https://www.cambridge.org/core/product/identifier/S2632324924000567/type/journal_article
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author Sara Marcucci
Stefaan Verhulst
María Esther Cervantes
author_facet Sara Marcucci
Stefaan Verhulst
María Esther Cervantes
author_sort Sara Marcucci
collection DOAJ
description The various global refugee and migration events of the last few years underscore the need for advancing anticipatory strategies in migration policy. The struggle to manage large inflows (or outflows) highlights the demand for proactive measures based on a sense of the future. Anticipatory methods, ranging from predictive models to foresight techniques, emerge as valuable tools for policymakers. These methods, now bolstered by advancements in technology and leveraging nontraditional data sources, can offer a pathway to develop more precise, responsive, and forward-thinking policies.
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spelling doaj-art-0ede78c6d9dd4689b7fdad5a80f4be4f2025-08-20T03:11:11ZengCambridge University PressData & Policy2632-32492025-01-01710.1017/dap.2024.56When forecasting and foresight meet data and innovation: toward a taxonomy of anticipatory methods for migration policySara Marcucci0https://orcid.org/0000-0003-4411-7158Stefaan Verhulst1https://orcid.org/0000-0002-0107-2984María Esther Cervantes2The GovLab, Rome, ItalyThe GovLab, New York, NY, USA; Center for Urban Science and Progress, New York University, New York; ISI Foundation, Turin, Italy: IMEC-SMIT, Vrije Universiteit Brussel, Brussels, Belgium; The Data Tank, Brussels, BelgiumThe GovLab, Vancouver, CanadaThe various global refugee and migration events of the last few years underscore the need for advancing anticipatory strategies in migration policy. The struggle to manage large inflows (or outflows) highlights the demand for proactive measures based on a sense of the future. Anticipatory methods, ranging from predictive models to foresight techniques, emerge as valuable tools for policymakers. These methods, now bolstered by advancements in technology and leveraging nontraditional data sources, can offer a pathway to develop more precise, responsive, and forward-thinking policies.https://www.cambridge.org/core/product/identifier/S2632324924000567/type/journal_articleanticipatory methodsmigrationnontraditional datapolicy makingtaxonomy
spellingShingle Sara Marcucci
Stefaan Verhulst
María Esther Cervantes
When forecasting and foresight meet data and innovation: toward a taxonomy of anticipatory methods for migration policy
Data & Policy
anticipatory methods
migration
nontraditional data
policy making
taxonomy
title When forecasting and foresight meet data and innovation: toward a taxonomy of anticipatory methods for migration policy
title_full When forecasting and foresight meet data and innovation: toward a taxonomy of anticipatory methods for migration policy
title_fullStr When forecasting and foresight meet data and innovation: toward a taxonomy of anticipatory methods for migration policy
title_full_unstemmed When forecasting and foresight meet data and innovation: toward a taxonomy of anticipatory methods for migration policy
title_short When forecasting and foresight meet data and innovation: toward a taxonomy of anticipatory methods for migration policy
title_sort when forecasting and foresight meet data and innovation toward a taxonomy of anticipatory methods for migration policy
topic anticipatory methods
migration
nontraditional data
policy making
taxonomy
url https://www.cambridge.org/core/product/identifier/S2632324924000567/type/journal_article
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