IDEAS home Printed from
   My bibliography  Save this article

Social Modelling and Public Policy: Application of Microsimulation Modelling in Australia



This paper provides an overview of social modelling and in particular a general introduction to and insight into the potential role and usefulness of micro-simulation in contributing to public policy. Despite having made a major contribution to the development of tax and cash transfer policies, there are many important areas of government policy to which microsimulation has not yet been applied or only slow progress has been made. The paper starts with a brief review of some of the main distinguishing characteristics of social models. This provides a contextual background to the main discussion on recent microsimulation modelling developments at the National Centre for Social and Economic Modelling (NATSEM) in Canberra, Australia, and how these models are being used to inform social and economic policy in Australia. Examples include: NATSEM’s static tax and cash transfer model (STINMOD); modelling the Australian Pharmaceutical Benefits Scheme; application of dynamic modelling for assessing future superannuation and retirement incomes; and the development of a regional microsimulation model (SYNAGI). Various technical aspects of the modelling are highlighted in order to illustrate how these types of socio-economic models are constructed and implemented. The key to effective social modelling is to recognise what type of model is required for a given task and to build a model that will meet the purposes for which it is intended. The potential of microsimulation models in the social security, welfare and health fields is very significant. However, it is important to recognise that policy decisions are going to involve value judgements - policies are created and implemented within a political environment. The aim is for social modelling, and in particular policy simulations, to contribute to a more rational analysis and informed debate. In this context, microsimulation models can make a significant contribution to the evaluation and implementation of ‘just and fair’ public policy.

Suggested Citation

  • Laurie Brown & Ann Harding, 2002. "Social Modelling and Public Policy: Application of Microsimulation Modelling in Australia," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(4), pages 1-6.
  • Handle: RePEc:jas:jasssj:2002-33-1

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Fagerberg, Jan, 1994. "Technology and International Differences in Growth Rates," Journal of Economic Literature, American Economic Association, vol. 32(3), pages 1147-1175, September.
    2. Eliasson, Gunnar, 1977. "Competition and Market Processes in a Simulation Model of the Swedish Economy," American Economic Review, American Economic Association, vol. 67(1), pages 277-281, February.
    3. Silverberg, Gerald & Verspagen, Bart, 1994. "Collective Learning, Innovation and Growth in a Boundedly Rational, Evolutionary World," Journal of Evolutionary Economics, Springer, vol. 4(3), pages 207-226, September.
    4. Sargent, Thomas J., 1993. "Bounded Rationality in Macroeconomics: The Arne Ryde Memorial Lectures," OUP Catalogue, Oxford University Press, number 9780198288695, June.
    5. Akerlof, George A & Yellen, Janet L, 1985. "Can Small Deviations from Rationality Make Significant Differences to Economic Equilibria?," American Economic Review, American Economic Association, vol. 75(4), pages 708-720, September.
    6. Erol Taymaz & G, rard Ballot, 1997. "The dynamics of firms in a micro-to-macro model: The role of training, learning and innovation," Journal of Evolutionary Economics, Springer, vol. 7(4), pages 435-457.
    7. Kenneth J. Arrow, 1962. "The Economic Implications of Learning by Doing," Review of Economic Studies, Oxford University Press, vol. 29(3), pages 155-173.
    8. Holland, John H & Miller, John H, 1991. "Artificial Adaptive Agents in Economic Theory," American Economic Review, American Economic Association, vol. 81(2), pages 365-371, May.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Linping Xiong & Xiuqiang Ma, 2007. "Forecasting China's Medical Insurance Policy for Urban Employees Using a Microsimulation Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(1), pages 1-8.
    2. M. Esteban Muñoz H. & Ivan Dochev & Hannes Seller & Irene Peters, 2016. "Constructing a Synthetic City for Estimating Spatially Disaggregated Heat Demand," International Journal of Microsimulation, International Microsimulation Association, vol. 9(3), pages 66-88.
    3. Mueller, Michel G. & de Haan, Peter, 2009. "How much do incentives affect car purchase? Agent-based microsimulation of consumer choice of new cars--Part I: Model structure, simulation of bounded rationality, and model validation," Energy Policy, Elsevier, vol. 37(3), pages 1072-1082, March.
    4. Annie Abello & Sharyn Lymer & Laurie Brown & Ann Harding & Ben Phillips, 2008. "Enhancing the Australian National Health Survey Data for Use in a Microsimulation Model of Pharmaceutical Drug Usage and Cost," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(3), pages 1-2.
    5. Davis, Peter & Lay-Yee, Roy & Pearson, Janet, 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, Elsevier, vol. 97(2-3), pages 267-274, October.
    6. Oliver Mannion & Roy Lay-Yee & Wendy Wrapson & Peter Davis & Janet Pearson, 2012. "JAMSIM: a Microsimulation Modelling Policy Tool," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(1), pages 1-8.


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jas:jasssj:2002-33-1. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Flaminio Squazzoni). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.