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Bayesian Agent-Based Population Studies

Knowledge Platform

Bayesian Agent-Based Population Studies

International migration is one of the most uncertain components of population change and a top-priority area for policy. The aim of this project is to develop a ground-breaking, interdisciplinary simulation model of international migration, based on a population of intelligent, cognitive agents, their social networks and institutions, all interacting with one another. We want to change the way in which migration can be understood, predicted, and managed by effectively integrating behavioural and social theory with modelling.

To develop micro-foundations for migration studies, model design follows the state-of-the-art developments in demography, statistics, cognitive psychology and computer science. will also offer a pioneering environment for applying the findings in practice through a dedicated modelling language. Bayesian statistical principles are used to design innovative computer experiments and learn about modelling the simulated individuals, as well as the way they make decisions.

In the project, we collate available information for migration models; build and test the simulations by applying experimental design principles to enhance our knowledge of migration processes; collect information on the underpinning decision-making mechanisms through psychological experiments; and design software for implementing Bayesian agent-based models in practice. The project uses various information sources to build models bottom-up, filling an important epistemological gap in migration studies.

The summary of key project findings can be found in an open access book: Bijak J et al. (2021), Towards Bayesian Model-Based Demography. Agency, Complexity and Uncertainty in Migration Studies, Springer: Cham.


Project Date
Type of Project
Lead implementing organization/s
University of Southampton
European Research Council - ERC
Geographic Scope
Syrian Arab Republic
Sub Regions
West Asia
Workstream Output
Regional Review Process
Cross Cutting Theme
SDG.10 - Reduced Inequalities
SDG Indicators
Indicator 10.7.2
Agents, brokers, and other businesses in migration
Big data, migration and human mobility
Forced migration or displacement
Governmental institutions in travel & migration
Innovative data sources on migration
Means of travel and transportation
Migrant communities and networks
Migration data sources
Migration forecasting
Routes, hubs, and sites in travel & migration
agent-based models
Bayesian methods
migration data

*References to Kosovo shall be understood to be in the context of United Nations Security Council resolution 1244 (1999).