Agent-Based Extensions to a Spatial Microsimulation Model of Demographic Change

  • Belinda M. Wu
  • Mark H. Birkin


New technologies and techniques now enable us to construct complex social models with more sophistication. In this paper we introduce an individual-based model, which combines the strengths of both microsimulation models and agent-based model approaches to project the UK population 30 years into the future. The hybrid modelling approach has been adopted to add flexibility and practicality in order to capture individual characteristics, especially in terms of individual movements, interactions and behaviours in the absence of suitable microdata. Such characteristics during the life courses of individuals are modelled through an event-driven model that simulates discrete processes that represent important demographic transitions.


Total Fertility Rate Demographic Process Heterogeneous Agent Microsimulation Model Demographic Behaviour 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research has been funded by the Economic and Social Research Council through the National Centre for e-Social Science (MoSeS RES-149-25-0034 and GENeSIS RES-149-25-1078).

Census output is Crown copyright and is reproduced with the permission of the Controller of HMSO and the Queen’s Printer for Scotland. 2001 Census, Output Area Boundaries. Crown copyright 2003.

The British Household Panel Study data were originally collected by the Economic and Social Research Council Research Centre on Micro-social Change at the University of Essex, now incorporated within the Institute for Social and Economic Research. Neither the original collectors of the data nor the UK Data Archive bear any responsibility for the analyses or interpretations presented here.

2001 Census: Special Licence Household Sample of Anonymised Records (SL-HSAR) were deposited by the University of Manchester, Cathie Marsh Centre for Census and Survey Research. Although all efforts are made to ensure the quality of the materials, neither the original data creators, depositors or copyright holders, the funders of the Data Collections, nor the UK Data Archive bear any responsibility for the accuracy or comprehensiveness of these materials.


  1. Aaberge, R., Colombino, U., Holmøy, E., Strøm, B., & Wennemo, T. (2007). Population ageing and fiscal sustainability: Integrating detailed labour supply models with CGE models. In A. Harding & A. Gupta (Eds.), Modelling our future: Population ageing, social security and taxation. Amsterdam: North-Holland.Google Scholar
  2. Axtell, R. (2000). Why agents? On the varied motivations for agent computing in the social sciences. In Proceedings of the workshop on agent simulation: Applications, models and tools. Argonne: Argonne National Laboratory.Google Scholar
  3. Billari, F., Ongaro, F., & Prskawetz, A. (2003). Agent-based computational demography: Using simulation to improve our understanding of demographic behavior. London/Heidelberg: Springer/Physica.Google Scholar
  4. Birkin, M. A., & Wu, B. (2012). A review of microsimulation and hybrid agent-based approaches. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds.), Agent-based models of geographical systems (pp. 51–68). Dordrecht: Springer.Google Scholar
  5. Bourguignon, F., & Spadaro, A. (2006). Microsimulation as a tool for evaluating redistribution policies. Working paper 2006–20, Society for the Study of Economic Inequality, (available from
  6. Champion, T., Fotheringham, S., Rees, P., Bramley, G., et al. (2002). Development of a migration model. London: Office of the Deputy Prime Minister and the Stationery Office.Google Scholar
  7. Crooks, A. T., & Heppenstall, A. J. (2012). Introduction to agent-based modelling. In A. J. Heppenstall, A. T. Crooks, L. M. See, & M. Batty (Eds.), Agent-based models of geographical systems (pp. 85–105). Dordrecht: Springer.Google Scholar
  8. Epstein, J. M. (1999). Agent-based computational models and generative social science. Complexity, 4(5), 41–60.CrossRefGoogle Scholar
  9. Espindola, A. L., Silveira, J. J., & Penna, T. J. P. (2006). A Harris-Todaro agent-based model to rural–urban migration. Brazilian Journal of Physics, 36(3A), 603–609.CrossRefGoogle Scholar
  10. Fredriksen, D., Heide, K. M., Holmøy, E., & Solli, I. F. (2007). Macroeconomic effects of proposal pension reforms in Norway. In A. Harding & A. Gupta (Eds.), Modelling our future: Population ageing, social security and taxation. Amsterdam: North-Holland.Google Scholar
  11. Gampe, J., Zinn, S., Willekens, F., Gagg, N., van der, de Beer, J., Himmelspach, J., & Uhrmacher, A. (2009). The microsimulation tool of the MicMac-Project. In Proceedings of the 2nd general conference of the international microsimulation association, Ottawa, 8–10 June 2009.Google Scholar
  12. Gustafsson, L., & Sternad, M. (2007). Bringing consistency to simulation of population models: Poisson simulation as a bridge between micro and macro simulation. Mathematical Biosciences, 209(2), 361–385.CrossRefGoogle Scholar
  13. Makowsky, M., Tavares, J., Makany, T., & Meier, P. (2006). An agent-based model of crisis-driven migration. In Proceedings of the complex systems summer school 2006. Santa Fe: Santa Fe Institute.Google Scholar
  14. Norman, P., Boyle, P., & Rees, P. (2004). Selective migration, health and deprivation: A longitudinal analysis. Social Science and Medicine, 60(12), 2755–2771.Google Scholar
  15. O’Donoghue, C. (2001). Dynamic microsimulation: A methodological survey. Brazilian Electronic Journal of Economics, 4(2).Google Scholar
  16. Rees, P. (2009). Population: Demography. In N. Thrift & R. Kitchen (Eds.), International encyclopedia of human geography. Oxford: Elsevier. Publication in summer 2009.Google Scholar
  17. Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1, 143–186.CrossRefGoogle Scholar
  18. Siegel, J. (2002). Applied demography. London: Academic.Google Scholar
  19. Turchin, P. (2003). Complex population dynamics: A theoretical/empirical synthesis. Princeton, NJ: Princeton University Press.Google Scholar
  20. van Imhoff, E., & Post, W. (1998). Microsimulation methods for population projection. Population: An English Selection, 10, 97–138.Google Scholar
  21. Willekens, F. (2005). Biographic forecasting: Bridging the micro–macro gap in population forecasting. New Zealand Population Review, 31(1), 77–124.Google Scholar
  22. Wilson, T., & Rees, P. (2005). Recent developments in population projection methodology: A review. Population, Space and Place, 11, 337–360.CrossRefGoogle Scholar
  23. Wittenberg, R., Pickard, L., Comas-Herrera, A., Davies, B., & Darton, R. (1998). Demand for long-term care: Projections of long-term care finance for elderly people. London: PRRRU.Google Scholar
  24. Wolf, D. A. (2001). The role of microsimulation in longitudinal data analysis. Canadian Studies in Population, 28, 165–179.Google Scholar
  25. Wu, B. M., Birkin, M. H., & Rees, P. H. (2008). A spatial microsimulation model with student agents. Computers, Environment and Urban Systems, 32, 440–453.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.School of GeographyUniversity of LeedsLeedsUK

Personalised recommendations