Multistate modelling extended by behavioural rules: An application to migration

Modelling and predicting forced migration

Migration models have evolved significantly during the last decade, most notably the so-called flow Fixed-Effects (FE) gravity models. Such models attempt to infer how human mobility may be driven by changing economy, geopolitics, and the environment among other things. They are also increasingly used for migration projections and forecasts. However, recent research shows that this class of models can neither explain, nor predict the temporal dynamics of human movement. This shortcoming is even more apparent in the context of forced migration, in which the processes and drivers tend to be heterogeneous and complex. In this article, we derived a Flow-Specific Temporal Gravity (FTG) model which, compared to the FE models, is theoretically similar (informed by the random utility framework), but empirically less restrictive. Using EUROSTAT data with climate, economic, and conflict indicators, we trained both models and compared their performances. The results suggest that the predictive power of these models is highly dependent on the length of training data. Specifically, as time-series migration data lengthens, FTG's predictions can be increasingly accurate, whereas the FE model becomes less predictive.

Demographic models of the reproductive process: Past, interlude, and future

After 30 years of active development, mechanistic models of the reproductive process nearly stopped attracting scholarly interest in the early 1980s. In the following decades, fertility research continued to thrive, relying on solid descriptive work and detailed analysis of micro-level data. The absence of systematic modelling efforts, however, has also made the field more fragmented, with empirical research, theory building, and forecasting advancing along largely disconnected channels. In this paper we outline some of the drivers of this process, from the popularization of user-friendly statistical software to the limitations of early family building models. We then describe a series of developments in computational modelling and statistical computing that can contribute to the emergence of a new generation of mechanistic models. Finally, we introduce a concrete example of this new kind of model, and show how they can be used to formulate and test theories coherently and make informed projections.

Has demography witnessed a data revolution? Promises and pitfalls of a changing data ecosystem

Over the past 25 years, technological improvements that have made the collection, transmission, storage, and analysis of data significantly easier and more cost efficient have ushered in what has been described as the 'big data' era or the 'data revolution'. In the social sciences context, the data revolution has often been characterized in terms of increased volume and variety of data, and much excitement has focused on the growing opportunity to repurpose data that are the by-products of the digitalization of social life for research. However, many features of the data revolution are not new for demographers, who have long used large-scale population data and been accustomed to repurposing imperfect data not originally collected for research. Nevertheless, I argue that demography, too, has been affected by the data revolution, and the data ecosystem for demographic research has been significantly enriched. These developments have occurred across two dimensions. The first involves the augmented granularity, variety, and opportunities for linkage that have bolstered the capabilities of 'old' big population data sources, such as censuses, administrative data, and surveys. The second involves the growing interest in and use of 'new' big data sources, such as 'digital traces' generated through internet and mobile phone use, and related to this, the emergence of 'digital demography'. These developments have enabled new opportunities and offer much promise moving forward, but they also raise important ethical, technical, and conceptual challenges for the field.

Moving towards the centre or the exit? Migration in population studies and in Population Studies 1996-2021

This paper examines the position of migration in population studies, focusing on the period 1996-2021. It considers the reasons why migration remains problematic for demographers, but also how approaches to migration have changed over the last 25 years. While it has arguably become more important to both demography and population studies because of the transition to low fertility and mortality, migration has metamorphosed into a complex field in its own right, almost independently from changes in demography. Both internal and international migration form the subject of this examination and four main themes are pursued: data and measurement; theories and approaches; migration and development; and migration and political demography. The papers published in the journal Population Studies are used to provide a mirror through which to view these changes over the last 25 years. This paper concludes by looking at likely future directions in migration studies, demography, and population studies.

Soft Randomized Machine Learning Procedure for Modeling Dynamic Interaction of Regional Systems

The paper suggests a randomized model for dynamic migratory interaction of regional systems. The locally stationary states of migration flows in the basic and immigration systems are described by corresponding entropy operators. A soft randomization procedure that defines the optimal probability density functions of system parameters and measurement noises is developed. The advantages of soft randomization with approximate empirical data balance conditions are demonstrated, which considerably reduces algorithmic complexity and computational resources demand. An example of migratory interaction modeling and testing is given.

A family affair: Evidence of chain migration during the mass emigration from the county of Halland in Sweden to the United States in the 1890s

This paper examines the influence of individual and household factors on an individual's propensity to emigrate from Halland, a region in south-west Sweden, to the United States during the era of mass migration in the late nineteenth century. The study has a case-control design, using individual-level longitudinal data for a group of emigrants (cases) and a group of non-emigrants (controls). Results indicate the importance of a family's emigration history; individuals whose relatives had previously moved to the United States were more likely to emigrate themselves. In addition, the results also show how this impact varied between groups and how other factors relating to the individual's life situation affected the migration decision. Thus, this paper shows how chain migration and migration networks play important roles during times of mass migration.

Modelling and simulating decision processes of linked lives: An approach based on concurrent processes and stochastic race

Individuals' decision processes play a central role in understanding modern migration phenomena and other demographic processes. Their integration into agent-based computational demography depends largely on suitable support by a modelling language. We are developing the Modelling Language for Linked Lives (ML3) to describe the diverse decision processes of linked lives succinctly in continuous time. The context of individuals is modelled by networks the individual is part of, such as family ties and other social networks. Central concepts, such as behaviour conditional on agent attributes, age-dependent behaviour, and stochastic waiting times, are tightly integrated in the language. Thereby, alternative decisions are modelled by concurrent processes that compete by stochastic race. Using a migration model, we demonstrate how this allows for compact description of complex decisions, here based on the Theory of Planned Behaviour. We describe the challenges for the simulation algorithm posed by stochastic race between multiple concurrent complex decisions.