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Complexity in Migration

Complexity in Migration

Human migration has been empirically modeled across a number of domains such as geography, demography, economics and regional science. The primary purpose of migration models is to determine the behavior of migrants based on personal or household demographics and/or the characteristics of the migrant’s current location and those of potential destinations. These models attempt to find the determinants of a choice to migrate with an emphasis on job availability or wage maximization. Labor market imbalances are posited to induce migration flows, by attracting labor to cities with greater employment opportunities, and “repel” workers from places with fewer opportunities and lower wages.

More recently, given new possibilities from more detailed but also bigger data, the migrant has been modeled as an individual agent who chooses a city based on minimizing cost—often related to distance, or cost of living increases at the destination—and maximizing more general benefits—whether warmer weather for retirees, or a preponderance of single people, etc. Yet, these models still capture cities as an aggregation of census data that characterizes the city by numbers (e.g. average income), and sets of cities by the distance that lies between them.

With bigger data sets and more advanced computational methods, researchers can sidestep generalizing cities by parameters such as unemployment figures or average temperature, and instead, express cities as nominal, unique entities within a large spatial network of flows. This shift towards data-driven, systemic models of migration invites new ideas, new methods of analysis, and new research questions.
Although the methods may be innovative to the field of migration modeling, the objectives of their employment are the same: to show how people actually move and settle in certain places, and how the urban (county, neighborhood, country, etc.) network of migrants function and persist given variables of a city’s population size, distance, etc. Case studies may show how migration has changed over time, in correlation to external factors, such as the economic climate, political and regional fluxes, etc.

This thematic series publishes papers that use systems and network science principles to model systems of migrants from place to place.

Edited by:  Clio Andris and Luis Bettencourt

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