The Role of Social Interactions in Demography: An Agent-Based Modelling Approach

  • Alexia PrskawetzEmail author
Part of the The Springer Series on Demographic Methods and Population Analysis book series (PSDE, volume 41)


Individual demographic behaviour cannot be understood in isolation from the social network one is linked to. However, formal models of demographic behaviour lag behind the empirical evidence. In this chapter we demonstrate how agent-based models can be applied to investigate the role of social interactions to explain macro-level demographic patterns like the age-at-marriage curve, age-specific fertility rates and the role of family policies for fertility. Based on these three examples we discuss the various steps that need to be followed when building up an agent-based model. These include the choice of the characteristics and rules of the agents together with the definition of how agents may interact and how macroeconomic behaviour may feed back on the micro-level decision processes.


Social Network Social Pressure Potential Partner Fertility Behaviour Family Policy 
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.



I would like to thank Bernhard Rengs and Thomas Fent for their comments and suggestions and Werner Richter for proof reading.


  1. Åberg, Y. (2003). Social interaction. Studies of contextual effects and endogenous processes. Stockholm studies on social mechanisms. Department of Sociology, Stockholm University.Google Scholar
  2. Aparicio Diaz, B., Fent, T., Prskawetz, A., & Bernardi, L. (2011). Transition to parenthood: The role of social interaction and endogenous network. Demography, 48(2), 559–579.CrossRefGoogle Scholar
  3. Axelrod, R. (1997). The complexity of cooperation: Agent-based models of competition and collaboration. Princeton: Princeton University Press.Google Scholar
  4. Axelrod, R., & Tesfatsion, L. (2006). A guide for newcomers to agent-based modeling in the social sciences. In Handbook of computational economics (vol. 2, pp. 1649–1659). Amsterdam: Elsevier.Google Scholar
  5. Axtell, R. L., Epstein, J. M., Dean, J. S., Gumerman, G. J., Swedlund, A. C., Harburger, J., Chakravarty, S., Hammond, R., Parker, J., & Parker, M. (2002). Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley. Proceedings of the National Academy of Sciences of the United States of America, 99(suppl. 3), 7275–7279.CrossRefGoogle Scholar
  6. Baroni, E., Hallberg, D., Eklöf, M., Lindh, T., & Z̆amac, J. (2009). Fertility decisions – Simulation in an agent-based model (IFSIM). In A. Zaidi, A. Harding, & P. Williamson (Eds.), New frontiers in microsimulation modelling, part III, (pp. 265–286). Farrnham: Asghate.Google Scholar
  7. Billari, F. C., & Prskawetz, A. (2003). Agent-based computational demography: Using simulation to improve our understanding of demographic behaviour. Heidelberg: Physica Verlag.CrossRefGoogle Scholar
  8. Billari, F. C., Prskawetz, A., Aparicio Diaz, B., & Fent, T. (2007). The “Wedding Ring”: An agent-based marriage model based on social interaction. Demographic Research, 17, 59–82.CrossRefGoogle Scholar
  9. Dixon, R. B. (1971). Explaining cross-cultural variations in age at marriage and proportions never marrying. Population Studies, 25(2), 215–233.CrossRefGoogle Scholar
  10. Entwisle, B., Malanson, G., Rindfuss, R. R., & Walsh, J. (2008). An agent-based model of household dynamics and land use change. Journal of Land Use Science, 3(1), 73–93.CrossRefGoogle Scholar
  11. Fent, T., Aparicio Diaz, B., & Prskawetz, A. (2013). Family policies in the context of low fertility and social structure. Demographic Research, 29, 963–998.CrossRefGoogle Scholar
  12. Feyrer, J., Sacerdote, B., & Stern, A. D. (2008). Will the stork return to Europe and Japan? Understanding fertility within developed nations. Journal of Economic Perspectives, 22(3), 3–22.CrossRefGoogle Scholar
  13. Gauthier, A. H. (2007). The impact of family policies on fertility in industrialized countries: A review of the literature. Population Research and Policy Review, 26(3), 323–346.CrossRefGoogle Scholar
  14. Gilbert, N. (2008), Agent-based models (Quantitative applications in the social sciences). Thousand Oaks: Sage Publications.CrossRefGoogle Scholar
  15. Goldenberg, J., Libai, B., Moldovan, S., & Muller, E. (2007). The NPV of bad news. International Journal of Research in Marketing, 24(3), 186–200.CrossRefGoogle Scholar
  16. Granovetter, M. (1978). Threshold models of collective behavior. The American Journal of Sociology, 83(6), 1420–1443.CrossRefGoogle Scholar
  17. Hernes, G. (1972). The process of entry into first marriage. American Sociological Review, 37, 47–82.CrossRefGoogle Scholar
  18. Kohler, H. P. (2001). Fertility and social interaction: An economic perspective. Oxford: Oxford University Press.CrossRefGoogle Scholar
  19. Macy, M. W., & Willer, R. (2002). From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology, 28, 143–166.CrossRefGoogle Scholar
  20. Montgomery, M. R., & Casterline, J. B. (1996). Social learning, social influence, and new models of fertility. Population and Development Review, 22, 151–175.CrossRefGoogle Scholar
  21. Palloni, A. (1998). Theories and models of diffusion in sociology. CDE Working Paper No. 98–11, Madison, Center for Demography and Ecology, University of Wisconsin.Google Scholar
  22. Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1(2), 143–186.CrossRefGoogle Scholar
  23. Schelling, T. C. (1978). Micromotives and macrobehavior. New York: Norton.Google Scholar
  24. Statistik Austria. (2007). Statistisches Jahrbuch Österreich 2007.Google Scholar
  25. Watkins, S. (1987). The fertility transition: Europe and the third world compared. Sociological Forum, 2(4), 645–673.CrossRefGoogle Scholar
  26. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393, 440–442.CrossRefGoogle Scholar
  27. Watts, D. J., Dodds, P. S., & Newman, M. E. J. (2002). Identity and search in social networks. Science, 296(5571), 1302–1305.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Institute of Statistics and Mathematical Methods in EconomicsVienna University of TechnologyViennaAustria
  2. 2.Wittgenstein Centre for Demography and Global Human Capital (IIASA, VID/ÖAW, WIC)ViennaAustria

Personalised recommendations