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SociaLab: A Dynamic Microsimulation Model

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Simulating Societal Change

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Abstract

At the heart of our project on representing and understanding societal change in New Zealand since 1981 is the construction of a computer model. The key parameters of this model are estimated from the data sources outlined in the previous chapter, and the model reproduces the patterns of societal change over the period in question. What is the purpose of constructing such a representation of societal change in New Zealand since 1981 when we already have the source data that seems entirely and authoritatively descriptive of that process of change, courtesy of the Census? The purpose of this very substantial technical effort is to create a flexible model of that process the Census describes, a model that we will be able to work with for the sake of social and policy inquiry. In this chapter, we briefly outline the techniques available in this field, before embarking on a much more detailed discussion of our tool of choice – microsimulation modelling – and then going on to pick out the key features of the model we have constructed for our current purpose, SociaLab.

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References

  • Bakker, C. (2014). Valuing the census. Wellington, New Zealand: Statistics New Zealand. Available at http://archive.stats.govt.nz/methods/research-papers/topss/valuing-census.aspx

    Google Scholar 

  • Ballas, D., Clarke, G., Hynes, S., Lennon, J., Morrissey, K., & O’Donoghue, C. (2013). A review of microsimulation for policy analysis. In O’Donoghue, Cathal Ballas, Dmitris Clarke, Graham Hynes, Stephen Morrissey, Karyn (Eds.), Spatial microsimulation for rural policy analysis (pp. 35–54). Berlin/Heidelberg, Germany: Springer.

    Chapter  Google Scholar 

  • Bruch, E., & Atwell, J. (2015). Agent-based models in empirical social research. Sociological Methods & Research, 44(2), 186–221.

    Article  Google Scholar 

  • Buckner, L., Croucher, K., Fry, G., & Jasinska, M. (2013). The impact of demographic change on the infrastructure for housing, health and social care in the north of England. Applied Spatial Analysis and Policy, 6(2), 123–142.

    Article  Google Scholar 

  • Conte, R., Gilbert, N., Bonelli, G., Cioffi-Revilla, C., Deffuant, G., Kertesz, J., … Helbing, D. (2012). Manifesto of computational social science. European Physical Journal-Special Topics, 214, 325–346.

    Article  Google Scholar 

  • Diez Roux, A. V. (2012). Conceptual approaches to the study of health disparities. Annual Review of Public Health, 33, 41–58.

    Article  Google Scholar 

  • Elder, G. H., Jr., & George, L. K. (2016). Age, cohorts, and the life course. In M. J. Shanahan, J. T. Mortimer, & M. K. Johnson (Eds.), Handbook of the life course (pp. 59–85). Cham, Switzerland: Springer.

    Chapter  Google Scholar 

  • Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W. J., … Rummukainen, M. (2013). Evaluation of climate models. In T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, & P. M. Midgley (Eds.), Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change (pp. 741–866). Cambridge, UK: Cambridge University Press.

    Google Scholar 

  • Gilbert, N. (1999). Simulation: A new way of doing social science. American Behavioral Scientist, 42(10), 1485–1487.

    Google Scholar 

  • Hagestad, G. O., & Dykstra, P. A. (2016). Structuration of the life course: Some neglected aspects. In M. J. Shanahan, J. T. Mortimer, & M. K. Johnson (Eds.), Handbook of the life course (pp. 131–157). Cham, Switzerland: Springer.

    Chapter  Google Scholar 

  • Hansen, J., Stephensen, P., & Kristensen, J. (2013). Modeling household formation and housing demand in Denmark – The dynamic microsimulation model SMILE. Copenhagen, Denmark: Danish Rational Economic Agents Model, DREAM. Available at http://www.dreammodel.dk/pdf/HousingDemand2013.pdf

    Google Scholar 

  • Li, J., & O’Donoghue, C. (2013). A survey of dynamic microsimulation models: Uses, model structure and methodology. International Journal of Microsimulation, 6(2), 3–55. Available at https://www.microsimulation.org/IJM/V6_2/2_IJM_6_2_2013_Li_Odonoghue.pdf

    Google Scholar 

  • Li, J., O’Donoghue, C., Loughrey, J., & Harding, A. (2014). Static models. In C. O’Donoghue (Ed.), Handbook of microsimulation modelling, Contributions to economic analysis (Vol. 293, pp. 47–75). Bingley, UK: Emerald Group Publishing Limited.

    Chapter  Google Scholar 

  • Mahamoud, A., Roche, B., & Homer, J. (2013). Modelling the social determinants of health and simulating short-term and long-term intervention impacts for the city of Toronto, Canada. Social Science & Medicine, 93, 247–255.

    Article  Google Scholar 

  • Meyer, M., Lorscheid, I., & Troitzsch, K. G. (2009). The development of social simulation as reflected in the first ten years of JASSS: A citation and co-citation analysis. Journal of Artificial Societies and Social Simulation, 12(4), 12. Available at http://jasss.soc.surrey.ac.uk/12/4/12.html

    Google Scholar 

  • Navicke, J., Rastrigina, O., & Sutherland, H. (2014). Nowcasting indicators of poverty risk in the European Union: A microsimulation approach. Social Indicators Research, 119(1), 101–119.

    Article  Google Scholar 

  • Smith, S. K., Tayman, J., & Swanson, D. A. (2013). Structural and microsimulation models. In A practitioner’s guide to state and local population projections (pp. 215–249). Dordrecht, Netherlands: Springer.

    Chapter  Google Scholar 

  • Solar, O., & Irwin, A. A. (2010). A conceptual framework for action on the social determinants of health. Social determinants of health discussion paper 2. Geneva, Switzerland: World Health Organization. Available at http://www.who.int/sdhconference/resources/ConceptualframeworkforactiononSDH_eng.pdf

    Google Scholar 

  • Sutherland, H., & Figari, F. (2013). EUROMOD: The European Union tax-benefit microsimulation model. International Journal of Microsimulation, 6(1), 4–26. Available at https://microsimulation.org/IJM/V6_1/2_IJM_6_1_Sutherland_Figari.pdf

    Google Scholar 

  • Zucchelli, E., Jones, A. M., & Rice, N. (2012). The evaluation of health policies through dynamic microsimulation methods. International Journal of Microsimulation, 5(1), 2–20. Available at http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=C82437628BC5F75D0F5484639C37F2CD?doi=10.1.1.829.5253&rep=rep1&type=pdf

    Google Scholar 

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Davis, P., Lay-Yee, R. (2019). SociaLab: A Dynamic Microsimulation Model. In: Simulating Societal Change. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-04786-3_3

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  • DOI: https://doi.org/10.1007/978-3-030-04786-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04785-6

  • Online ISBN: 978-3-030-04786-3

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