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Managing Migration: What Can We Learn From Simulation Experiments?

This policy brief presents several lessons from agent-based models of migrant route formation and persistence.

How can interdisciplinary science shed light on features of migration to help policy makers make better and more robust decisions? An appealing possibility is offered by computer simulations, including individual-level agent-based models. In such models, agents can represent various entities, such as migrants, other actors, or policy makers, all interacting with one another and with their environments. 

The models can be complex and can shed light on some of the complexity of migration processes, illuminating policy trade-offs and key areas of uncertainty. However, their design needs to be specific to answering well-defined research or policy questions. In this brief, several lessons from agent-based models of migrant route formation and persistence are presented.


Key Messages:

  • Migration flows can respond to policy changes in unpredictable ways, as a result of complex environments of the underlying drivers, agency of prospective migrants and the way in which they make decisions.  
  • Computer simulation modelling can help decision makers better understand the complexity of migration processes, as well as shed light on intended and unintended consequences of different policy options. 
  • Simulation models of migration route formation highlight the role of information and trust in shaping the ways in which migrants move across unknown terrain, and to identify promising areas for new data collection. 
  • To safeguard against misuse, the simulation models for policy need to be ethical by design: open and transparent about the modelling processes, and honest about the uncertainty of the results and the trade-offs involved. 


  • Bijak, J., Higham, P.  A., Hilton, J., Hinsch, M., Nurse, S., Prike, T., Reinhardt, O., Smith, P. W. F, Uhrmacher, A. M., and Warnke, T. (2021) Towards Bayesian Model-Based Demography. Agency, Complexity and Uncertainty in Migration Studies. Methodos Series, vol. 17. Cham: Springer. Open Access.
  • Castles, S. (2004) Why migration policies fail. Ethnic and Racial Studies, 27(2), 205–227.
  • Czaika, M., Bijak, J., and Prike, T. (2021) Migration Decision-Making and Its Key Dimensions. The Annals of the American Academy of Political and Social Science, 697(1), 15–31. Open Access.
  • Czaika, M., Bohnet, H., Zardo F., and Bijak J. (2022) European migration governance in the context of uncertainty. QuantMig Project Deliverable 1.5. Krems: University of Continuing Education. Available on 
  • Dunsch, F., Tjaden, J. D., and Quiviger, W. (2019) Migrants as Messengers: The Impact of Peer-to-Peer Communication on Potential Migrants in Senegal. Impact Evaluation Report, available online here. Geneva: IOM. 
  • Emmer, M., Richter, C., and Kunst, M. (2016) Flucht 2.0: Mediennutzung durch Flüchtlinge vor, während und nach der Flucht. Project Report, available online here. Berlin: Freie Universität.
  • Hinsch, M. and Bijak, J. (2023) The effects of information on the formation of migration routes and the dynamics of migration. Artificial Life. 29(1), 3–20. Open Access.
  • Nurse, S. and Bijak, J. (2021) Syrian Migration to Europe, 2011 – 21: Data Inventory. Online resource. 
  • Prike, T., Bijak, J., Higham, P. A., and Hilton, J. (2022) How Safe is this Trip? Judging Personal Safety in a Pandemic Based on Information from Different Sources. Journal of Experimental Psychology: Applied, 28(3), 509–524. Open Access.
  • Reinhardt, O., Prike, T., Hinsch, M., Bijak, J., Wilsdorf, P., and Uhrmacher, A.M. (2023) Simulation Studies of Social Systems – Telling the Story Based on Provenance Patterns. TechRxiv. Preprint, v2.0 (28 April 2023). DOI: 10.36227/techrxiv.20209844.v2


This work has received funding from the European Research Council, grant no. 725232 Bayesian Agent-based Population Studies.




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