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Population Synthesis with Quasirandom Integer Sampling

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Abstract

Established methods for synthesising a population from geographically aggregated data are robust and well understood. However, most rely on the potentially detrimental process of integerisation if a whole individual population is required, e.g. for use in agent-based modelling (ABM). This paper describes and investigates the use of quasirandom sequences to sample populations from known marginal constraints whilst preserving those marginal distributions. We call this technique Quasirandom Integer Without-replacement Sampling (QIWS) and show that the statistical properties of quasirandomly sampled populations to be superior to those of pseudorandomly sampled ones in that they tend to yield entropies much closer to populations generated using the entropy-maximising iterative proportional fitting (IPF) algorithm. The implementation is extremely efficient, easily outperforming common IPF implementations. It is freely available as an open source R package called humanleague. Finally, we suggest how the current limitations of the implementation can be overcome, providing a direction for future work.

Suggested Citation

  • 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.
    • Handle: RePEc:jas:jasssj:2017-45-3
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    1. Gargiulo, Floriana & Lenormand, Maxime & Huet, Sylvie & Baqueiro Espinosa, Omar, 2012. "Commuting network models: Getting the essentials," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 15(2).
    2. Lenormand, Maxime & Bassolas, Aleix & Ramasco, José J., 2016. "Systematic comparison of trip distribution laws and models," Journal of Transport Geography, Elsevier, vol. 51(C), pages 158-169.
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    Cited by:

    1. 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.
    2. 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.

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