Introduction: Agent-Based Computational Demography

  • Francesco C. Billari
  • Fausta Ongaro
  • Alexia Prskawetz
Part of the Contributions to Economics book series (CE)


Originating from developments in computer science (also applied to natural sciences), a computational approach to the study of human behavior has developed that has gathered impetus in the literature during the 1990s. Agent-based computational models have become more and more used in the social sciences (i.e. in economics with the idea of Agent-based Computational Economics (ACE), and in sociology with the idea of Social Simulation). Different to the approach based on statistical analysis of behavioral data that aims to understand why specific rules are applied by humans, agent-based computational models presuppose (realistic) rules of behavior and try to challenge the validity of these rules by showing whether they can or cannot explain macroscopic regularities. In this introductory chapter, we argue that in order to study human populations, agent-based approaches are particularly useful from various theoretical perspectives. We urge demographers and other scholars interested in population studies to look at Agent-Based Computational Demography (ABCD) as a promising stream of research, which can improve our understanding of demographic behavior. We review and use the chapters of this book to substantiate our argumentations.


Partnership Formation Primate Society Family Demography Artificial Society Vacant Dwelling 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Axelrod, R. (1997): The Complexity of Cooperation. Agent-Based Models of Competition and Collaboration. Prince ton University Press, PrincetonGoogle Scholar
  2. 2.
    Axtell, R. (2000): Why Agents ? On the Varied Motivations for Agent Computing in the Social Sciences. CSED Working Paper No. 17. Available online at: http: // Scholar
  3. 3.
    Axtell, R.L., Epstein, J.M., Dean, J.S., Gumeran, 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. PNAS 99, suppl. 3, 7275–7279CrossRefGoogle Scholar
  4. 4.
    Benenson, I., Omer, I., Hatna, E. (2003): Agent-Based Modeling of Householders’ Migration Behavior and its Consequences. This book, 97–115Google Scholar
  5. 5.
    Billari, F.C. (2000): Searching for Mates Using ‘Fast and Frugal’ Heuristics: a Demographic Perspective. In: Ballot G., Weisbuch G. (Eds.): Applications of Simulation to Social Sciences, 53–65, Hermes Science Publications, OxfordGoogle Scholar
  6. 6.
    Billari, F.C., Prskawetz, A., Fürnkranz J. (2003): On the Cultural Evolution of Age-at-Marriage Norms. This book, 139–157Google Scholar
  7. 7.
    Burch, T.K. (1996): Icons, Straw Men and Precision: Reflections on Demographic Theories of Fertility Decline. The Sociological Quarterly 37(1), 59–81CrossRefGoogle Scholar
  8. 8.
    Burch, T.K. (2003): Data, Models, Theory and Reality. The Structure of Demographic Knowledge. This book, 19–40Google Scholar
  9. 9.
    Chattoe, E. (2003): The Role of Agent-Based Modelling in Demographic Explanation. This book, 41–54Google Scholar
  10. 10.
    Conte, R. (2000): The Necessity of Intelligent Agents for Social Simulation. In: Ballot G., Weisbuch G. (Eds.):Applications of Simulation to Social Sciences, 19–38, Hermes Science Publications, OxfordGoogle Scholar
  11. 11.
    Conte R., Castelfranchi C. (1995): Understanding the functions of norms in social groups through simulation. In: Gilbert N., Conte R. (Eds.): Artificial societies. The computer simulation of social life, 252–267, UCL Press, LondonGoogle Scholar
  12. 12.
    Courgeau, D. (1995): Migration theories and behavioral models. International Journal of Population Geography 1 (1), 19–27CrossRefGoogle Scholar
  13. 13.
    Dean, J.S., Gumerman, G.J., Epstein, J.M., Axtell, R.L., Swedlund, A.C., Parker, M.T., McCarroll, S. (2000): Understanding Anasazi Culture Change Through Agent-Based Modeling. In: Kohler, T.A., Gumerman, G.J. (Eds.): Dynamics in Human and Primate Societies. Agent-Based Modeling of Social and Spatial Processes, 179–206, Santa Fe Institute, New York, Oxford University PressGoogle Scholar
  14. 14.
    Epstein, J.M., Axtell, R. (1996): Growing Artificial Societies. Social Science from the Bottom Up, Brookings Institutions Press, Washington, DC.Google Scholar
  15. 15.
    Ewert, U.C.; Roehl, M., Uhrmacher, A. (2003): Consequences of Mortality Crises in Pre-Modern European Towns: A Multiagent-Based Simulation Approach. This book, 175–196Google Scholar
  16. 16.
    Gigerenzer, G., Todd, P.M., the ABC Research Group (1999): Simple heuristics that make us smart. Oxford University Press, New YorkGoogle Scholar
  17. 17.
    Gilbert, N. Troitzsch, K.G. (1999): Simulation for the Social Scientist. Open University Press, Buckingham-PhiladelphiaGoogle Scholar
  18. 18.
    Halpin, B. (1999): Simulation in Society. American Behavioral Scientist 42(10), 1488–1508Google Scholar
  19. 19.
    Hammel, E.A., McDaniel, C.K., Wachter, K.W. (1979): Demographic Consequences of Incest Tabus: A Microsimulation Analysis. Science 205, 972–977CrossRefGoogle Scholar
  20. 20.
    Hammel, E.A., Wachter, K.W. (1996): Evaluating the Slavonian Census of 1698. Part II: A microsimulation test and extension of the evidence. European Journal of Population 12 (4), 295–326CrossRefGoogle Scholar
  21. 21.
    Hedström, P., Swedberg, R. (Eds.) (1998): Social Mechanisms. Cambridge University Press, CambridgeGoogle Scholar
  22. 22.
    Heiland, F. (2003): The Collapse of the Berlin Wall — Simulating State-Level East to West German Migration Patterns. This book, 73–96Google Scholar
  23. 23.
    Hobcraft, J. (2000): Moving Beyond Elaborate Description: Towards Understanding Choices About Parenthood. Paper presented at the FFS Flagship Conference, Brussels, May 29–31Google Scholar
  24. 24.
    Jager, W., Janssen, M.A. (2003): Diffusion Processes in Demographic Transitions: A Prospect on Using Multi Agent Simulation to Explore the Role of Cognitive Strategies and Social Interactions. This book, 55–72Google Scholar
  25. 25.
    Johnson, P.E. (1999): Simulation Modeling in Political Science. American Behavioral Scientist 42(10), 1509–1530Google Scholar
  26. 26.
    Kohler, T.A., Gumerman, G.J. (Eds.) (2000): Dynamics in Human and Primate Societies. Agent-Based Modeling of Social and Spatial Processes. Santa Fe Institute, New York, Oxford University PressGoogle Scholar
  27. 27.
    König, A., Möhring, M., Troitzsch, K.G. (2003): Agents, Hierarchies and Sustainability. This book, 197–210Google Scholar
  28. 28.
    Land, K.C. (1986): Methods for national population forecasts: a review. Journal of the American Statistical Association 81, 888–901CrossRefGoogle Scholar
  29. 29.
    Lesthaeghe, R. (2000): Fertility and partnership change: FFS contributions and requirement s for the future. Paper presented at the FFS Flagship Conference, Brussels, May 29–31Google Scholar
  30. 30.
    Macy, M.W., Willer, R. (2002): From Factors to Actors: Computational Sociology and Agent-Based Modeling. Annual Review of Sociology 28, 143–166CrossRefGoogle Scholar
  31. 31.
    Murphy, M. (2003): Bringing behavior back into micro-simulation: Feedback mechanisms in demographic models. This book, 159–174Google Scholar
  32. 32.
    Nakazawa, M., Ohtsuka, R. (1997): Analysis of completed parity using microsimulation modeling. Mathematical Population Studies 6(3), 173–186CrossRefGoogle Scholar
  33. 33.
    Palloni, A. (2000): Demographic analysis: new theories, new methods and new data. In: De Sandre P., Ongaro F. (Eds.): Demografia: presente e futuro, 61–88, Cleup, PadovaGoogle Scholar
  34. 34.
    Riley, J., Sheps, M. (1966): An analytic simulation model of human reproduction with demographic and biological components. Population Studies 19, 297–310Google Scholar
  35. 35.
    Ruggles, S. (1993): Confessions of a microsimulator. Historical methods 26(4), 161–169CrossRefGoogle Scholar
  36. 36.
    Saam, N.J. (1999): Simulating the micro-macro link: new approaches to an old problem and an application to military coups. Sociological Methodology 29, 43–79CrossRefGoogle Scholar
  37. 37.
    Schelling, T.C. (1971): Dynamic models of segregation. Journal of Mathematical Sociology 1, 143–186CrossRefGoogle Scholar
  38. 38.
    Tesfatsion, L. (Eds.) (2001): Special Issue of Agent-based Computational Economics. Journal of Economic Dynamics & Control 25, 281–654Google Scholar
  39. 39.
    Todd, P.M. (1997): Searching for the next best mate. In: Conte R., Hegselmann R., Terna P. (Eds.): Simulating Social Phenomena, 419–436, Springer-Verlag, BerlinGoogle Scholar
  40. 40.
    Todd, P.M., Billari, F.C. (2002): Population-Wide Marriage Pattern s Produced by Individual Mate-Search Heuristics. This book, 117–137Google Scholar
  41. 41.
    Tomassini, C., Wolf, D. (2000): Shrinking Kin Networks in Italy Due to Sustained Low Fertility. European Journal of Population 16, 353–372CrossRefGoogle Scholar
  42. 42.
    Van Imhoff, E., Post, W.J. (1998): Microsimulation methods for population projection. Population: An English Selection 10(1) [Special issue on “New Methodological Approaches in the Social Sciences”], 97–138Google Scholar
  43. 43.
    Vaupel, J.W., Carey, J.R., Christensen, K., Johnson, TE., Yashin, A.I., Holm, N.V., Iachine, I.A., Khazaeli, A.A., Liedo, P., Longo, Y.D., Zeng, Y., Manton, K.G., Curtsinger, J.W. (1998): Biodemographic Trajectories of Longevity. Science 280(5365), 855–860CrossRefGoogle Scholar
  44. 44.
    Wachter, K.W. (1987): Microsimulation of household cycles. In: Bongaarts, J., Burch, T.K., Wachter, K.W. (Eds.): Family demography. Methods and their application, Clarendon Press, OxfordGoogle Scholar
  45. 45.
    Wilson, C. (1999): Evolutionary Theory and Historical Fertility Change. Population and Development Review 25, 531–541CrossRefGoogle Scholar
  46. 46.
    Wolf, D. (2001): The Role of Microsimulation in Longitudinal Data Analysis. Canadian Studies in Population 28, 165–179Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Francesco C. Billari
    • 1
  • Fausta Ongaro
    • 2
  • Alexia Prskawetz
    • 3
  1. 1.Institute of Quantitative MethodsBocconi UniversityMilanoItaly
  2. 2.Department of Statistical SciencesUniversity of PadovaPadovaItaly
  3. 3.Max Planck Institute for Demographic ResearchRostockGermany

Personalised recommendations