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Streamlining Simulation Experiments with Agent-Based Models in Demography

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Abstract

In the last decade, the uptake of agent-based modeling in demography and other population sciences has been slowly increasing. Still, in such areas, where traditional data-driven, statistical approaches prevail, the hypothesis-driven design of agent-based models leads to questioning the validity of these models. Consequently, suitable means to increase the confidence into models and simulation results are required. To that end, explicit, replicable simulation experiments play a central role in model design and validation. However, the analysis of more complex models implies executing various experiments, each of which combines various methods. To streamline these experimentation processes a flexible computational simulation environment is necessary. With a new binding between SESSL -- an internal domain-specific language for simulation experiments -- and ML3 -- a simulator for linked lives designed specifically for agent-based demographic models -- we cater for these objectives and provide a powerful simulation tool. The proposed approach can serve as a foundation for current efforts of employing advanced and statistical model analysis of agent-based demographic models, as part of a wider process of iterative model building. We demonstrate its potential in specifying and executing different experiments with a simple model of return migration and a more complex model of social care.

Suggested Citation

  • Oliver Reinhardt & Jason Hilton & Tom Warnke & Jakub Bijak & Adelinde M. Uhrmacher, 2018. "Streamlining Simulation Experiments with Agent-Based Models in Demography," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(3), pages 1-9.
  • Handle: RePEc:jas:jasssj:2017-156-3
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    1. Jonathan Gray & Jason Hilton & Jakub Bijak, 2017. "Choosing the choice: Reflections on modelling decisions and behaviour in demographic agent-based models," Population Studies, Taylor & Francis Journals, vol. 71(0), pages 85-97, October.
    2. Belinda Diaz & Thomas Fent & Alexia Prskawetz & Laura Bernardi, 2011. "Transition to Parenthood: The Role of Social Interaction and Endogenous Networks," Demography, Springer;Population Association of America (PAA), vol. 48(2), pages 559-579, May.
    3. Dustmann, Christian & Fadlon, Itzhak & Weiss, Yoram, 2011. "Return migration, human capital accumulation and the brain drain," Journal of Development Economics, Elsevier, vol. 95(1), pages 58-67, May.
    4. Jakob Grazzini & Matteo G. Richiardi, 2013. "Consistent Estimation of Agent-Based Models by Simulated Minimum Distance," LABORatorio R. Revelli Working Papers Series 130, LABORatorio R. Revelli, Centre for Employment Studies.
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    2. Liukun Wang & Chunjie Qi & Peng Jiang & Si Xiang, 2022. "The Impact of Blockchain Application on the Qualification Rate and Circulation Efficiency of Agricultural Products: A Simulation Analysis with Agent-Based Modelling," IJERPH, MDPI, vol. 19(13), pages 1-17, June.

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