Machine learning in labor migration prediction

Tarasyev, Alexandr A.; Agarkov, Gavriil A.; Hosseini, Seyed Hasan

doi:10.1063/1.5044033

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…Tarasyev et al constructed a multi-regional migration-unemployment-wage model that uses an inductive ML approach to explore labor migration trends, based on the migrant distribution, age structure, income level, cost of migrating, labor market conditions, regional employment and unemployment information, climatic conditions, and the distance between the countries of origin and destination, among other variables [103].…”

Section: Classical Machine-learning Prediction Methods 1 Artificial N...mentioning

confidence: 99%

Transcending Time and Space: Survey Methods, Uncertainty, and Development in Human Migration Prediction

Huang

Yang

et al. 2023

Sustainability

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As a fundamental, holistic, and strategic issue facing human society, human migration is a key factor affecting the development of countries and cities, given the constantly changing population numbers. The fuzziness of the spatiotemporal attributes of human migration limits the pool of open-source data for human migration prediction, leading to a relative lag in human migration prediction algorithm research. This study expands the definition of human migration research, reviews the progress of research into human migration prediction, and classifies and compares human migration algorithms based on open-source data. It also explores the critical uncertainty factors restricting the development of human migration prediction. Based on the analysis, there is no “best” migration prediction model, and data are key to forecasting human migration. Social media’s popularity and its increase in data have enabled the application of artificial intelligence in population migration prediction, which has attracted the attention of researchers and government administrators. Future research will aim to incorporate uncertainty into the predictive analysis framework, and explore the characteristics of population migration behaviors and their interactions. The integration of machine-learning and traditional data-driven models will provide a breakthrough for this purpose.

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Section: Classical Machine-learning Prediction Methods 1 Artificial N...mentioning

confidence: 99%

Transcending Time and Space: Survey Methods, Uncertainty, and Development in Human Migration Prediction

Huang

Yang

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Tarasyev et al constructed a multi-regional migration-unemployment-wage model by selecting the distribution of migrants, age structure of migrants, wage level, cost of migrating, labor market conditions, regional employment and unemployment information, climatic conditions and distance between countries of origin and destination, etc [99]. An inductive machine learning approach is used to explore the trend of labour migration.…”

Section: Machine Learning Prediction Methodsmentioning

confidence: 99%

Transcending Time and Space: A Survey Methods, Uncertainty, and Development in Human Migration Prediction

Pu¹,

Huang²,

Yang³

et al. 2023

Preprint

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As a fundamental, overall, and strategic issue facing human society, human migration is a key factor affecting the development of countries and cities given constantly changing population numbers. The fuzziness of the spatiotemporal attributes of human migration limits the pool of open-source data for human migration prediction, leading to a relative lag in human migration prediction algorithm research. This study expands the definition of human migration research, reviews the progress of research into human migration prediction, and classifies and compares human migration algorithms based on open-source data. It also explores the critical uncertainty factors restricting the development of human migration prediction. Given the effect of human migration prediction, in combination with artificial intelligence and big data technology, the paper concludes with specific suggestions and countermeasures aimed at enhancing human migration prediction research results to serve economic and social development and national strategy.

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“…To that end, training machine learning classifiers is a data-hungry endeavor, which requires not just a greater number of events, but multiple reports of the same event for greater robustness check and data validity. In addition, greater data input can render decision and output chains more efficient in further iterations by triangulation, as results are more robust results incrementally train further iterations with greater precision (Tarasyev et al 2018;Quinn et al 2018).…”

Section: The 'Achilles Heel' Of Forecasting: Data Reliabilitymentioning

confidence: 99%

Using Social Media to Monitor Conflict-Related Migration: A Review of Implications for A.I. Forecasting

Ünver

2022

Social Sciences

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Following the large-scale 2015–2016 migration crisis that shook Europe, deploying big data and social media harvesting methods became gradually popular in mass forced migration monitoring. These methods have focused on producing ‘real-time’ inferences and predictions on individual and social behavioral, preferential, and cognitive patterns of human mobility. Although the volume of such data has improved rapidly due to social media and remote sensing technologies, they have also produced biased, flawed, or otherwise invasive results that made migrants’ lives more difficult in transit. This review article explores the recent debate on the use of social media data to train machine learning classifiers and modify thresholds to help algorithmic systems monitor and predict violence and forced migration. Ultimately, it identifies and dissects five prevalent explanations in the literature on limitations for the use of such data for A.I. forecasting, namely ‘policy-engineering mismatch’, ‘accessibility/comprehensibility’, ‘legal/legislative legitimacy’, ‘poor data cleaning’, and ‘difficulty of troubleshooting’. From this review, the article suggests anonymization, distributed responsibility, and ‘right to reasonable inferences’ debates as potential solutions and next research steps to remedy these problems.

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Machine learning in labor migration prediction

Cited by 11 publications

References 11 publications

Transcending Time and Space: Survey Methods, Uncertainty, and Development in Human Migration Prediction

Transcending Time and Space: Survey Methods, Uncertainty, and Development in Human Migration Prediction

Transcending Time and Space: A Survey Methods, Uncertainty, and Development in Human Migration Prediction

Using Social Media to Monitor Conflict-Related Migration: A Review of Implications for A.I. Forecasting

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