Department

Digital and Computational Demography (Zagheni)

At a Glance Laboratories Publications Team

Detailed Description

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The Department of Digital and Computational Demography advances demographic science by developing innovative methods, data infrastructure, and theories to explain and predict population dynamics in a rapidly changing world. A central goal is to anticipate how populations adapt and respond to societal, environmental, and technological change. The overarching ambition is to develop a systemic framework for population dynamics, one that links fragmented population theories through shared mechanisms, and that is fueled by expanded data and computing capacity. 

The Department is organized around two complementary laboratories that focus on six main research areas. The Lab of Migration and Mobility expands our knowledge on migration measurement and modelling, high-skilled migration, and integration and segregation. The Lab of Population Dynamics and Sustainable Wellbeing advances our understanding of health and survival, family and life-course processes, and the interactions between populations and their natural and digital environments. Together, the two laboratories study fundamental demographic processes across the life course and across generational and spatial scales, recognizing the interconnected nature of the key determinants and components of population change.

The Department strengthens interdisciplinary research and collaboration across its units through shared scientific platforms. These cross-cutting initiatives address some of the defining challenges of the twenty-first century: (i) understanding the reciprocal relationships between climate change and population dynamics; (ii) assessing how digitalization and technological transformations reshape population research, demographic behavior, and inequalities; and (iii) evaluating the demographic consequences of armed conflicts, epidemics, environmental hazards, and how they intersect with structural inequalities. By integrating these themes into the study of migration, mortality, fertility, health, and family research, the Department develops more comprehensive models of population change under conditions of uncertainty, and in a comparative perspective.

Building on MPIDR's strengths in demographic methods, the Department has made important contributions to demographic estimation, forecasting, and computational modeling, while at the same time pioneering the use of digital trace data to study population processes. Across all research areas, methodological innovation has served the broader scientific purpose of strengthening the empirical foundations upon which demographic theory is built.

A relevant contribution of the Department has also been the development of innovative databases. Flagship initiatives such as the Human Migration Database, the Scholarly Migration Database, large-scale primary data collections like the COVID-19 Health Behavior Survey, as well as collections of simulated synthetic data and contextual environmental data, complement and expand MPIDR's internationally recognized portfolio of data infrastructure.

Looking ahead, the Department’s long-term vision is to consolidate classic demographic resources with the data infrastructure and computational tools of the digital age, in order to develop integrated demographic frameworks that jointly analyze the dynamics of migration, mortality, and fertility, including their determinants and consequences. By integrating advances in statistical learning with demographic theory, data, and computation, the Department seeks to strengthen demography as a more explanatory, predictive and computational science. In order to achieve its interdisciplinary aims, the Department nurtures collaborations with other research units at MPIDR, with Max Planck Institutes that specialize in computational sciences, and with a global network of population centers and scholars. In parallel, it maintains a strong commitment towards training the next generation of population scientists, including in the context of open science best practices.