IDEAS home Printed from https://ideas.repec.org/a/jas/jasssj/2017-143-2.html
   My bibliography  Save this article

Application Independent Heuristic Data Merging Methodology for Sample-Free Agent Population Synthesis

Author

Listed:
  • Bhagya N. Wickramasinghe

Abstract

This work proposes a novel application independent heuristics specifying framework and a household structures construction process, for sample-free population synthesis. The framework decouples heuristics and the algorithm by defining a set of generic constructs to specify heuristics on relationships and household structures. The algorithm uses Iterative Proportional Fitting, Monte Carlo sampling and combinatorial optimisation to synthesise the population. Decoupled nature of the system allows it to be used in different applications relatively easily by changing the heuristics. We demonstrate that this is a robust technique capable of producing synthetic agent populations highly consistent to input data distributions using two case studies. Apart from contributing to synthetic population reconstruction, this work will form one of the building blocks for integrating independently developed models to build complex new agent based models.

Suggested Citation

  • Bhagya N. Wickramasinghe, 2019. "Application Independent Heuristic Data Merging Methodology for Sample-Free Agent Population Synthesis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 22(1), pages 1-5.
    • Handle: RePEc:jas:jasssj:2017-143-2
    as

    Download full text from publisher

    File URL: https://www.jasss.org/22/1/5/5.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrew Smith & Robin Lovelace & Mark Birkin, 2017. "Population Synthesis with Quasirandom Integer Sampling," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(4), pages 1-14.
    2. Eric Silverman & Jakub Bijak & Jason Hilton & Viet Dung Cao & Jason Noble, 2013. "When Demography Met Social Simulation: A Tale of Two Modelling Approaches," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 16(4), pages 1-9.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Valery Leonidovich Makarov & Albert Raufovich Bakhtizin & Elena Davidovna Sushko, 0. "Agent-Based Models as a Means of Testing of Management Solutions," Administrative Consulting, Russian Presidential Academy of National Economy and Public Administration. North-West Institute of Management., issue 12.
    2. Guillaume Marois & Ekaterina Zhelenkova & Balhasan Ali, 2022. "Labour Force Projections in India Until 2060 and Implications for the Demographic Dividend," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(1), pages 477-497, November.
    3. Jakub Bijak & Jason D. Hilton & Eric Silverman & Viet Dung Cao, 2013. "Reforging the Wedding Ring," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(27), pages 729-766.
    4. Danilo Liuzzi & Aymeric Vié, 2022. "Staring at the Abyss: a neurocognitive grounded agent-based model of collective-risk social dilemma under the threat of environmental disaster," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 17(2), pages 613-637, April.
    5. André Grow & Jan Van Bavel, 2015. "Assortative Mating and the Reversal of Gender Inequality in Education in Europe: An Agent-Based Model," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-24, June.

    Corrections

    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:2017-143-2. See general information about how to correct material in RePEc.

    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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Francesco Renzini (email available below). General contact details of provider: .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.