Abstract
In his seminal 1957 paper, Orcutt foresaw that microsimulation models comprising “various sorts of interacting units which receive inputs and generate outputs” (Orcutt, 1957, p. 117) could be built and used to test “what would happen given specified external conditions and governmental actions” (Orcutt, 1957, p. 122). More recently Keuschnigg, Lovsjo, and Hedstrom (2018) have drawn attention to the affinities between analytical sociology and computational social science, thus making the connection between the earlier insight of Orcutt and the modern discipline of sociology. In a similar fashion we see the potential of computational techniques for sociology; thus, microsimulation is a methodological approach located at this intersection where social mechanisms can be computationally modelled using extensive microdata within a virtual environment.
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References
Baekgaard, H. (2002). Micro-macro linkage and the alignment of transition processes: Some issues, techniques and examples. Technical paper No. 25. Canberra, Australia: National Centre for Social and Economic Modelling, University of Canberra.
Calder, M., Craig, C., Culley, D., de Cani, R., Donnelly, C. A., Douglas, R., … Wilson, A. (2018). Computational modelling for decision-making: Where, why, what, who and how. Royal Society Open Science, 5, 172096. https://doi.org/10.1098/rsos.172096
Campaner, R. (2011). Mechanistic causality and counterfactual-manipulative causality: Recent insights from philosophy and science. Journal of Epidemiology and Community Health, 65, 1070–1074.
Davis, P. (Ed.). (2014). Data inference in observational settings (Vol. 1–4). London, UK: Sage.
Davis, P., Lay-Yee, R., & Pearson, J. (2010). Using micro-simulation to create a synthesised data set and test policy options: The case of health service effects under demographic ageing. Health Policy, 97, 267–274.
Davis, P., Lay-Yee, R., Chang, K., & von Randow, M. (2018). Shiny application: New Zealand as a social laboratory. Available from https://compassnz.shinyapps.io/SociaLabShiny/
Keuschnigg, M., Lovsjo, N., & Hedstrom, P. (2018). Analytical sociology and computational social science. Journal of Computational Social Science, 1(1), 3–14.
Lay-Yee, R., Milne, B., Davis, P., Pearson, J., & McLay, J. (2015). Determinants and disparities: A simulation approach to the case of child health care. Social Science and Medicine, 128, 202–211.
Lay-Yee, R., Milne, B., Shackleton, N., Chang, K., & Davis, P. (2018). Preventing youth depression: Simulating the impact of parenting interventions. Advances in Life Course Research, 37, 15–22.
Lay-Yee, R., Pearson, J., Davis, P., von Randow, M., Kerse, N., & Brown, L. (2017). Changing the balance of social care for older people: Simulating scenarios under demographic ageing in New Zealand. Health & Social Care in the Community, 25(3), 962–974.
Levy, R., & Buhlmann, F. (2016). A socio-structural framework for life course analysis. Advances in Life Course Research, 30, 30–42.
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
Li, J., & O’Donoghue, C. (2014). Evaluating binary alignment methods in microsimulation models. Journal of Artificial Societies and Social Simulation, 17(1), 15. Available at http://jasss.soc.surrey.ac.uk/17/1/15.html
Milne, B. J., Lay-Yee, R., Thomas, J., Tobias, M., Tuohy, P., Armstrong, A., … Mannion, O. (2014). A collaborative approach to bridging the research-policy gap through the development of policy advice software. Evidence and Policy, 10(1), 127–136.
Orcutt, G. (1957). A new type of socio-economic system. Review of Economics and Statistics, 39(2), 116–123.
Percival, R. (2007). APPSIM—Software selection and data structures. Working Paper. Canberra, Australia: National Centre for Social and Economic Modelling, University of Canberra.
R Development Core Team. (2018). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
R Studio. (2018). Available from https://shiny.rstudio.com/
Scott, A. (2003). A computing strategy for SAGE: 2. Programming considerations. Technical Note no. 3. London, UK: Citeseer.
Solar, O., & Irwin, A. (2010). A conceptual framework for action on the social determinants of health. Social determinants of health discussion paper 2 (Policy and practice). Geneva, Switzerland: World Health Organisation. Available at http://www.who.int/sdhconference/resources/ConceptualframeworkforactiononSDH_eng.pdf
Spielauer, M. (2011). What is social science microsimulation? Social Science Computer Review, 29(1), 9–20.
Thieme, N. (2018). R generation. Significance, 15(4), 14–19.
Troitzsch, K. G., Mueller, U., Gilbert, G. N., & Doran, J. E. (Eds.). (1996). Social science microsimulation. Berlin, Germany: Springer.
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Davis, P., Lay-Yee, R. (2019). Simulation. In: Simulating Societal Change. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-04786-3_7
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