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JAMSIM: a Microsimulation Modelling Policy Tool

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Abstract

JAMSIM (JAva MicroSIMulation) is an innovative synthesis of open source packages that provides an environment and set of features for the creation of dynamic discrete-time microsimulation models that are to be executed, manipulated and interrogated by non-technical, policy-oriented users. Combining the leading open source statistical package R and one of the foremost agent-based modelling (ABM) graphical tools Ascape, JAMSIM is available as an open source tool, for public reuse and modification. Here we describe microsimulation, our functional requirements, a review of tools used by other micro-simulators and an evaluation of existing software, followed by the architecture, features and use of JAMSIM.

Suggested Citation

  • 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.
    • Handle: RePEc:jas:jasssj:2011-26-2
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    1. Thomas Sauerbier, 2002. "UMDBS - a New Tool for Dynamic Microsimulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(2), pages 1-5.
    2. Figari, Francesco & Paulus, Alari & Sutherland, Holly & Mantovani, Daniela & Lelkes, Orsolya & Levy, Horacio & Lietz, Christine, 2008. "Improving the capacity and usability of EUROMOD: final report," EUROMOD Working Papers EM4/08, EUROMOD at the Institute for Social and Economic Research.
    3. 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.
    4. Cynthia Nikolai & Gregory Madey, 2009. "Tools of the Trade: A Survey of Various Agent Based Modeling Platforms," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(2), pages 1-2.
    5. Roberto Leombruni & Matteo Richiardi, 2006. "LABORsim: An Agent-Based Microsimulation of Labour Supply – An Application to Italy," Computational Economics, Springer;Society for Computational Economics, vol. 27(1), pages 63-88, February.
    6. Miles Parker, 2001. "What is Ascape and Why Should You Care?," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 4(1), pages 1-5.
    7. Cathal O'Donoghue & John Lennon & Stephen Hynes, 2009. "The Life-Cycle Income Analysis Model (LIAM): a study of a flexible dynamic microsimulation modelling computing framework," International Journal of Microsimulation, International Microsimulation Association, vol. 2(1), pages 16-31.
    8. 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.
    9. O'Donoghue, Cathal & Sutherland, Holly & Immervoll, Herwig, 1999. "An introduction to EUROMOD," EUROMOD Working Papers EM0/99, EUROMOD at the Institute for Social and Economic Research.
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    Cited by:

    1. Melanie N Tomintz & Bernhard Kosar & Victor M García-Barrios, 2017. "simSALUD: Design and Implementation of an Open-source Wizard based Spatial Microsimulation Framework," International Journal of Microsimulation, International Microsimulation Association, vol. 10(2), pages 118-143.
    2. Matteo Richiardi & Ross E. Richardson, 2017. "JAS-mine: A new platform for microsimulation and agent-based modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 10(1), pages 106-134.
    3. Martin Spielauer & Thomas Horvath & Marian Fink, 2020. "microWELT: A Dynamic Microsimulation Model for the Study of Welfare Transfer Flows in Ageing Societies from a Comparative Welfare State Perspective," WIFO Working Papers 609, WIFO.
    4. Sabine Zinn, 2014. "The MicSim Package of R: An Entry-Level Toolkit for Continuous-Time Microsimulation," International Journal of Microsimulation, International Microsimulation Association, vol. 7(3), pages 3-32.
    5. Joachim Merz & Lars Rusch, 2015. "MICSIM-4j - A General Microsimulation Model User Guide (Version 1.1)," FFB-Discussionpaper 100, Research Institute on Professions (Forschungsinstitut Freie Berufe (FFB)), LEUPHANA University Lüneburg.
    6. Lay-Yee, Roy & Milne, Barry & Davis, Peter & Pearson, Janet & McLay, Jessica, 2015. "Determinants and disparities: A simulation approach to the case of child health care," Social Science & Medicine, Elsevier, vol. 128(C), pages 202-211.
    7. Jinjing Li & Cathal O'Donoghue, 2013. "A survey of dynamic microsimulation models: uses, model structure and methodology," International Journal of Microsimulation, International Microsimulation Association, vol. 6(2), pages 3-55.
    8. repec:ijm:journl:v109:y:2017:i:1:p:106-134 is not listed on IDEAS
    9. Jan Pablo Burgard & Hanna Dieckmann & Joscha Krause & Hariolf Merkle & Ralf Münnich & Kristina M. Neufang & Simon Schmaus, 2020. "A generic business process model for conducting microsimulation studies," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 191-211, August.

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