Model-Based Demography: Towards a Research Agenda

  • Daniel Courgeau
  • Jakub Bijak
  • Robert Franck
  • Eric Silverman
Chapter
Part of the The Springer Series on Demographic Methods and Population Analysis book series (PSDE, volume 41)

Abstract

This chapter aims to contribute to the debate on the role of model-based approaches, such as agent-based modelling, in the future of demography. First we call attention to the developments of the discipline since the seventeenth century, and we describe its four successive paradigms related to the period, cohort, event-history and multilevel perspectives. We argue that these paradigms are complementary and that demography, since its beginnings, has subscribed to the classical scientific research programme launched by the promoters of modern science. Next, we examine how simulation modelling developing in population sciences recently, may help to respond to three main challenges: how to overcome complexity in social research; how to reduce its uncertainty; and how to reinforce its theoretical foundations. We sketch a model-based research programme for demography, looking specifically at interactions between various population systems. We then show how this approach might conform to the classical scientific research programme, in order to take advantage of its benefits.

References

  1. Aalen, O. O. (1975). Statistical inference for a family of counting processes. PhD thesis, University of California, Berkeley.Google Scholar
  2. Ahlburg, D. A. (1995). Simple versus complex models: Evaluation, accuracy and combining. Mathematical Population Studies, 5(3), 281–290.CrossRefGoogle Scholar
  3. Alho, J. M., & Spencer, B. D. (2005). Statistical demography and forecasting. Berlin/Heidelberg: Springer.Google Scholar
  4. Alkema, L., Raftery, A. E., & Clark, S. J. (2007). Probabilistic projections of HIV prevalence using Bayesian melding. Annals of Applied Statistics, 1(1), 229–248.CrossRefGoogle Scholar
  5. Aparicio Diaz, B., Fent, T., Prskawetz, A., & Bernardi, L. (2011). Transition to parenthood: The role of social interaction and endogenous networks. Demography, 48(2), 559–579.CrossRefGoogle Scholar
  6. Axtell, R., Epstein, J., Dean, J., Gumerman, G., Swedlund, A., 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
  7. Bacon, F. (1620). Novum Organum. London: J. Bill. English translation: Spedding, J., Ellis, R. L., & Heath, D. D. (1863). The works (Vol. VIII). Boston: Taggard and Thompson.Google Scholar
  8. Bassu, S. (2009). Metretique, éthique et politique: le Protagoras et le Politique de Platon. Dissertatio, 29, 85–114.Google Scholar
  9. Bassu, S. (2011). Ordre et mesure, kosmos et metron de la pensée archaïque à la philosophie platonicienne. In S. Alexandre & E. Rogan (Eds.), Actes du colloque “Ordres et désordres”, Université Paris 1 and Université Paris Ouest, Nanterre-La Défense, 4–5 June 2010. Available via: Zetesis, vol. 2 [Online], https://f.hypotheses.org/wp-content/blogs.dir/3211/files/2015/04/2Bassu.pdf.
  10. Bayes, T. R. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418.CrossRefGoogle Scholar
  11. Bijak, J. (2010). Forecasting international migration in Europe: A Bayesian view (Springer Series on Demographic Methods and Population Analysis, Vol. 24). Dordrecht: Springer.Google Scholar
  12. Bijak, J., & Bryant, J. (2016). Bayesian demography 250 years after Bayes. Population Studies, 70(1), 1–19.CrossRefGoogle Scholar
  13. Bijak, J., Hilton, J., Silverman, E., & Cao, V. (2013). Reforging the wedding ring: Exploring a semi-artificial model of population for the United Kingdom with Gaussian process emulators. Demographic Research, 29(27), 729–766.CrossRefGoogle Scholar
  14. Billari, F., & Prskawetz, A. (Eds.). (2003). Agent-based computational demography. Using simulation to improve our understanding of demographic behaviour. New York: Physica-Verlag.Google Scholar
  15. Blayo, C. (1995). La condition d’homogeneite en analyse demographique et en analyse statistique des biographies. Population, 50(6), 1501–1518.CrossRefGoogle Scholar
  16. Boudon, R. (1977). Effet pervers et ordre social. Paris: Presses Universitaires de France.Google Scholar
  17. Brenner, T., & Werker, C. (2007). A taxonomy of inference in simulation models. Computational Economics, 30(3), 227–244.CrossRefGoogle Scholar
  18. Bullock, S., & Silverman, E. (2008). Levins and the legitimacy of artificial worlds. A Cross-Disciplinary Workshop “Epistemological Perspectives on Simulation”, Lisbon, 2–3 October 2008.Google Scholar
  19. Burch, T. (2003a). Data, models, theory and reality: The structure of demographic knowledge. In F. Billari & A. Prskawetz (Eds.), Agent-based computational demography. Using simulation to improve our understanding of demographic behaviour (pp. 19–40). Heidelberg/New York: Physica-Verlag.CrossRefGoogle Scholar
  20. Burch, T. (2003b). Demography in a new key: A theory of population theory. Demographic Research, 9(11), 263–284.CrossRefGoogle Scholar
  21. Casini, L., Illari, P. M., Russo, F., & Williamson, J. (2011). Models for prediction, explanation and control: Recursive Bayesian networks. Theoria, 26(1), 5–33.Google Scholar
  22. Charbit, Y., & Petit, V. (2011). Towards a comprehensive demography: Rethinking the research agenda on change and response. Population and Development Review, 37(2), 219–239.CrossRefGoogle Scholar
  23. Chattoe, E. (2003). The role of agent-based models in demographic explanation. In F. Billari & A. Prskawetz (Eds.), Agent-based computational demography. Using simulation to improve our understanding of demographic behaviour (pp. 41–54). Heidelberg/New York: Physica-Verlag.CrossRefGoogle Scholar
  24. Clark, S. J., Thomas, J. R., & Bao, L. (2012). Estimates of age-specific reductions in HIV prevalence in Uganda: Bayesian melding estimation and probabilistic population forecast with an HIV-enabled cohort component projection model. Demographic Research, 27(26), 743–774.CrossRefGoogle Scholar
  25. Conte, R., Gilbert, N., Bonelli, G., Cioffi-Revilla, C., Deffuant, G., Kertesz, Loreto, V., Moat, S., Nadal, J.-P., Sanchez, A., Nowak, A., Flache, A., San Miguel, M., & Helbing, D. (2012). Manifesto of computational social science. European Physical Journal Special Topics, 214(1), 325–346.CrossRefGoogle Scholar
  26. Courgeau, D. (2007). Multilevel synthesis. From the group to the individual. Dordrecht: Springer.Google Scholar
  27. Courgeau, D. (2012). Probability and social science. Methodological relationships between the two approaches (Methodos Series 10). Dordrecht: Springer.Google Scholar
  28. Courgeau, D. (2013). La mesure dans les sciences de la population. Cahiers Philosophiques, 135(4), 51–74.CrossRefGoogle Scholar
  29. Courgeau, D., & Franck, R. (2007). Demography, a fully formed science or a science in the making? An outline programme, Population-E, 62 (1), pp. 39–45. (La démographie, science constituée ou en voie de constitution? Esquisse d’un programme. Population, 62(1), 39–45).Google Scholar
  30. Courgeau, D., & Lelièvre, E. (1992). Event history analysis in demography. Oxford: Clarendon.Google Scholar
  31. Courgeau, D., Bijak, J., Franck, R., & Silverman, E. (2014). Are the four Baconian Idols still alive in demography? Revue Quetelet/Quetelet Journal, 2(2), 31–59.CrossRefGoogle Scholar
  32. Di Paolo, E. A., Noble, J., & Bullock, S. (2000). Simulation models as opaque thought experiments. In M. Bedau, J. McCaskill, N. Packard, & S. Rasmussen (Eds.), Proceedings of the 7th international conference on artificial life (pp. 497–506). Cambridge, MA: MIT Press.Google Scholar
  33. Doob, J. L. (1953). Stochastic processes. New York/Chichester: Wiley.Google Scholar
  34. Ducheyne, S. (2005). Bacon’s idea and Newton’s practice of induction. Philosophica, 76, 115–128.Google Scholar
  35. Durkheim, E. (1897). Le suicide. Paris: Alcan.Google Scholar
  36. Epstein, J. M. (2008). Why model? Journal of Artificial Societies and Social Simulation, 11(4), article 12. http://jasss.soc.surrey.ac.uk/11/4/12.html.
  37. Franck, R. (Ed.). (2002a). The explanatory power of models. Bridging the gap between empirical and theoretical research in the social sciences (Methodos series, Vol. 1). Boston/Dordrecht/London: Kluwer Academic Publishers.Google Scholar
  38. Franck, R. (2002b). Computer simulation and the reverse engineering method. Conclusions of part II. In R. Franck (Ed.), The explanatory power of models (Methodos series, Vol. 1, pp. 141–146). Dordrecht/Boston/London: Kluwer Academic Publishers.CrossRefGoogle Scholar
  39. Geard, N., McCaw, J. M., Dorin, A., Korb, K. B., & McVernon, J. (2013). Synthetic population dynamics: A model of household demography. Journal of Artificial Societies and Social Simulation, 16(1), article 8. http://jasss.soc.surrey.ac.uk/16/1/8.html.
  40. Godfrey-Smith, P. (2006). The strategy of model-based science. Biology and Philosophy, 21(5), 725–740.CrossRefGoogle Scholar
  41. Goldstein, H. (1987). Multilevel models in educational and social research. London: Arnold.Google Scholar
  42. Graunt, J. (1662). Natural and political observations mentioned in a following index, and made upon the bills of mortality. London: Tho. Roycroft.Google Scholar
  43. Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., Goss-Custard, J., Grand, T., Heinz, S., Huse, G., Huth, A., Jepsen, J. U., Jørgensen, C., Mooij, W. M., Müller, B., Pe’er, G., Piou, C., Railsback, S. F., Robbins, A. M., Robbins, M. M., Rossmanith, E., Rüger, N., Strand, E., Souissi, S., Stillman, R. A., Vabø, R., Visser, U., & DeAngelis, D. L. (2006). A standard protocol for describing individual-based and agent-based models. Ecological Modelling, 198(1–2), 115–126.Google Scholar
  44. Henry, L. (1959). D’un problème fondamental de l’analyse démographique. Population, 14(1), 9–32.CrossRefGoogle Scholar
  45. Hirschman, C. (2008). The future of demography. Asian Population Studies, 4(3), 233–234.CrossRefGoogle Scholar
  46. Holland, J. H. (1995). Hidden order. Reading: Addison-Wesley.Google Scholar
  47. Huneman, P. (2014). Mapping an expanding territory: Computer simulations in evolutionary biology. History and Philosophy of the Life Sciences, 36(1), 60–89.CrossRefGoogle Scholar
  48. Huyghens, C. (1657). De ratiociniis in ludo aleae. Leyde: Elzevier.Google Scholar
  49. Ibrahim, J. G., Chen, M.-H., & Sinha, D. (2001). Bayesian survival analysis. New York: Springer.CrossRefGoogle Scholar
  50. IUSSP [International Union for the Scientific Study of the Populations]. (1982). Multilingual demographic dictionary (2nd ed.). Liège: Ordina.Google Scholar
  51. Kennedy, M., & O’Hagan, T. (2001). Bayesian calibration of computer models. Journal of the Royal Statistical Society, Series B, 63(3), 425–464.CrossRefGoogle Scholar
  52. Keyfitz, N. (1971). Models. Demography, 8(4), 571–580.CrossRefGoogle Scholar
  53. Klüver, J., Stoica, C., & Schmidt, J. (2003). Formal models, social theory and computer simulations: Some methodical reflections. Journal of Artificial Societies and Social Simulation, 6(2), article 8, http://jasss.soc.surrey.ac.uk/6/2/8.html.
  54. Kniveton, D., Smith, C., & Wood, S. (2011). Agent-based model simulations of future changes in migration flows for Burkina Faso. Global Environmental Change, 21(Suppl. 1), S34–S40.CrossRefGoogle Scholar
  55. Kuhn, T. (1962). The structure of scientific revolutions. Chicago/London: The University of Chicago Press.Google Scholar
  56. Laplace, P. S. (1774). Mémoire sur la probabilité des causes par les événements. Mémoires de l’Académie Royale des Sciences de Paris, Tome, VI, 621–656.Google Scholar
  57. Laplace, P. S. (1812). Théorie analytique des Probabilités (Vol. 2). Paris: Courcier Imprimeur.Google Scholar
  58. Levins, R. (1966). The strategy of model building in population biology. American Scientist, 54(4), 421–431.Google Scholar
  59. Lutz, W. (2012). Demographic metabolism: A predictive theory of socio-economic change. Population and Development Review, 38(Supplement), 283–301.Google Scholar
  60. Mannheim, K. (1928). Das Problem der Generationen. Kölner Vierteljahreshefte für Soziologie, 7(2), 309–330.Google Scholar
  61. Mason, W. M., Wong, G. W., & Entwistle, B. (1983). Contextual analysis through the multilevel linear model. In S. Leinhart (Ed.), Sociological methodology 1983–1984 (pp. 72–103). San Francisco: Jossey-Bass.Google Scholar
  62. McCulloch, W. S., & Pitts, W. H. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5(4), 115–133.CrossRefGoogle Scholar
  63. Mill, J. S. (1843). A system of logic, ratiocinative and inductive: Being a connected view of the principles of evidence, and the methods of scientific investigation (Vol. I). London: Harrison.Google Scholar
  64. Morgan, S. P., & Lynch, S. M. (2001). Success and future of demography. The role of data and methods. Annals of the New York Academy of Sciences, 954, 35–51.CrossRefGoogle Scholar
  65. Moss, S., & Edmonds, B. (2005). Towards good social science. Journal of Artificial Societies and Social Simulation, 8(4), article 13. http://jasss.soc.surrey.ac.uk/8/4/13.html.
  66. NRC [National Research Council]. (2000). Beyond six billion: Forecasting the world’s population. Washington, DC: National Academies Press.Google Scholar
  67. Oakley, J., & O’Hagan, A. (2002). Bayesian inference for the uncertainty distribution of computer model outputs. Biometrika, 89(4), 769–784.CrossRefGoogle Scholar
  68. Pascal, B. (1665). Traité du triangle arithmétique, avec quelques autres traités sur le même sujet. Paris: Guillaume Desprez.Google Scholar
  69. Petit, V., & Charbit, Y. (2012). The French school of demography: Contextualising demographic analysis. Population and Development Review, 38(supplement), 322–333.Google Scholar
  70. Petty, W. (1690). Political arithmetick. London: Robert Clavel & Hen. Mortlock.Google Scholar
  71. Polhill, J. G., Sutherland, L.-A., & Gotts, N. M. (2010). Using qualitative evidence to enhance an agent-based modelling system for studying land use change. Journal of Artificial Societies and Social Simulation, 13(2), art. 10. http://jasss.soc.surrey.ac.uk/13/2/10.html.
  72. Poole, D., & Raftery, A. E. (2000). Inference for deterministic simulation models: The Bayesian melding approach. Journal of the American Statistical Association, 95(452), 1244–1255.CrossRefGoogle Scholar
  73. Raftery, A. E. (1995). Bayesian model selection in social research. Sociological Methodology, 25, 111–163.CrossRefGoogle Scholar
  74. Raftery, A. E., Li, N., Ševčíková, H., Gerland, P., & Heilig, G. K. (2012). Bayesian probabilistic population projections for all countries. Proceedings of the National Academy of Sciences, 109, 13915–13921.CrossRefGoogle Scholar
  75. Rogers, A. (1975). Introduction to multiregional mathematical demography. New York: Wiley.Google Scholar
  76. Ryder, N. B. (1951). The cohort approach. Essays in the measurement of temporal variations in demographic behaviour. PhD thesis, Princeton University, New York.Google Scholar
  77. Silverman, E., & Bryden, J. (2007). From artificial societies to new social science theory. In F. Almeida e Costa, L. M. Rocha, E. Costa, I. Harvey, & A. Coutinho (Eds.), Advances in artificial life, 9th European conference, ECAL 2007 proceedings (pp. 645–654). Berlin/Heidelberg: Springer.Google Scholar
  78. Silverman, E., Bijak, J., & Noble, J. (2011). Feeding the beast: Can computational demographic models free us from the tyranny of data? In T. Lenaerts, M. Giacobini, H. Bersini, P. Bourgine, M. Dorigo, & R. Doursat (Eds.), Advances in artificial life, ECAL 2011: Proceedings of the eleventh European conference on the synthesis and simulation of living systems (pp. 747–754). Cambridge, MA: MIT Press.Google Scholar
  79. Silverman, E., Bijak, J., Hilton, J., Cao, V., & Noble, J. (2013). When demography met social simulation: A tale of two modelling approaches. Journal of Artificial Societies and Social Simulation, 16(4), article 9. http://jasss.soc.surrey.ac.uk/16/4/9.html.
  80. Smith, S. K. (1997). Further thoughts on simplicity and complexity in population projection models. International Journal of Forecasting, 13(4), 557–565.CrossRefGoogle Scholar
  81. Tabutin, D. (2007). Whither demography? Strengths and weaknesses of the discipline over fifty years of change. Followed by a debate on the future of the discipline, by G. Caselli & V. Egidi, D. Courgeau & R. Franck, J. Hobcraft, & J. Hoem. Population-E, 62(1), 13–56.Google Scholar
  82. Thagard, P. (1993). Computational philosophy of science. Cambridge, MA: MIT Press.Google Scholar
  83. Whelpton, P. (1949). Cohort analysis of fertility. American Sociological Review, 14(6), 735–749.CrossRefGoogle Scholar
  84. Willekens, F. (2005). Biographic forecasting: Bridging the micro-macro gap in population forecasting. New Zealand Population Review, 31(1), 77–124.Google Scholar
  85. Willekens, F. (2012). Migration: A perspective from complexity science. Paper for the Complexity Science for the Real World workshop on migration, Chilworth. 16 Feb 2012.Google Scholar
  86. Xie, Y. (2000). Demography: Past, present and future. Journal of the American Statistical Association, 95(450), 670–673.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Daniel Courgeau
    • 1
  • Jakub Bijak
    • 2
  • Robert Franck
    • 3
  • Eric Silverman
    • 4
  1. 1.Institut national d’études démographiquesParisFrance
  2. 2.Department of Social Statistics and DemographyUniversity of SouthamptonSouthamptonUK
  3. 3.Université catholique de LouvainLouvain-la-NeuveBelgium
  4. 4.School of ComputingTeesside UniversityMiddlesbroughUK

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