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International Conference on Computational Science

ICCS 2021: Computational Science – ICCS 2021 pp 269–283Cite as

An Attempt to Replace System Dynamics with Discrete Rate Modeling in Demographic Simulations

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12745))

Abstract

The usefulness of simulation in demographic research has been repeatedly confirmed in the literature. The most common simulation approach to model population trends is system dynamic (SD). Difficulties in a reliable mapping of population changes with SD approach have been however reported by some authors. Another simulation approach, i.e. discrete rate modeling (DRM), had not yet been used in population dynamics modelling, despite examples of this approach being used in the modelling of processes with similar internal dynamics. The purpose of our research is to verify if DRM can compete with the SD approach in terms of accuracy in simulating population changes and the complexity of the model. The theoretical part of the work describes the principles of the DRM approach and provides an overview of the applications of the DRM approach versus other simulation methods. The experimental part permits the conclusion that DRM approach does not match the SD in terms of comprehensive accuracy in mapping the behavior of cohorts of the complex populations. We have been however able to identify criteria for population segmentation that may lead to better results of DRM simulation against SD.

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References

  1. Ansah, J.P., Eberlein, R.L., Love, S.R., Bautista, M.A., et al.: Implications of long-term care capacity response policies for an aging population: a simulation analysis. Health Policy 116(1), 105–113 (2014). https://doi.org/10.1016/j.healthpol.2014.01.006

  2. Bechard, V., Cote, N.: Simulation of mixed discrete and continuous systems: an iron ore terminal example. In: Proceedings of the 2013 Winter Simulation Conference - Simulation: Making Decisions in a Complex World, WSC 2013, pp. 1167–1178 (2013)

    Google Scholar 

  3. Best, A.F., Haozous, E.A., Berrington de Gonzalez, A., Chernyavskiy, P., Freedman, N.D. et al.: Premature mortality projections in the USA through 2030: a modelling study. Lancet Public Heal. 3(8), e374–e384 (2018). https://doi.org/10.1016/S2468-2667(18)30114-2

  4. Colacelli, M., Corugedo, E.F.: Macroeconomic effects of Japan’s demographics: can structural reforms reverse them? IMF Working Paper (2018)

    Google Scholar 

  5. Damiron, C., Nastasi, A.: Discrete rate simulation using linear programming. In: Mason, S.J. et al. (eds.) Proceedings of the 40th Conference on Winter Simulation. pp. 740–749 Winter Simulation Conference (2008). https://doi.org/10.1109/WSC.2008.4736136

  6. Eberlein, R.L., Thompson, J.P.: Precise modeling of aging populations. Syst. Dyn. Rev. 29(2), 87–101 (2013). https://doi.org/10.1002/sdr.1497

    Article  Google Scholar 

  7. Eggink, E., Ras, M., Woittiez, I.: Dutch long-term care use in an ageing population. J. Econ. Ageing. 9, 63–70 (2017). https://doi.org/10.1016/j.jeoa.2016.08.001

    Article  Google Scholar 

  8. Forrester, J.W.: Urban Dynamics. Productivity Press, Portland (1969)

    Google Scholar 

  9. Giesecke, J., Meagher, G.A.: Population ageing and structural adjustment. Aust. J. Labour Econ. 11(3), 227–247 (2008)

    Google Scholar 

  10. Han, F.: Demographics and the Natural Rate of Interest in Japan. (2019). https://doi.org/10.5089/9781484396230.001

  11. Keating, E.K.: Issues to Consider While Developing a System Dynamics Model. Technical report, Northwest. Univ. (1999)

    Google Scholar 

  12. Kingston, A., Comas-Herrera, A., Jagger, C.: Forecasting the care needs of the older population in England over the next 20 years: estimates from the Population Ageing and Care Simulation (PACSim) modelling study. Lancet Public Heal. 3(9), e447–e455 (2018). https://doi.org/10.1016/S2468-2667(18)30118-X

    Article  Google Scholar 

  13. Krahl, D.: ExtendSim advanced technology: discrete rate simulation. In: Rossetti, M.D., Hill, R.R., Johansson, B., Dunkin, A. R.G.I. (ed.) Proceedings - Winter Simulation Conference. pp. 333–338 IEEE (2009). https://doi.org/10.1109/WSC.2009.5429340

  14. Lauf, S., Haase, D., Kleinschmit, B.: The effects of growth, shrinkage, population aging and preference shifts on urban development—a spatial scenario analysis of Berlin. Germany. Land Use Pol. 52, 240–254 (2016). https://doi.org/10.1016/j.landusepol.2015.12.017

    Article  Google Scholar 

  15. McGrattan, E., et al.: On financing retirement, health, and long-term care in Japan. IMF Work. Pap. 18(249), 1–43 (2018). https://doi.org/10.5089/9781484384718.001

    Article  Google Scholar 

  16. Meadows, D.L., et al.: Dynamics of Growth in a Finite World. Wright Allen Press, Cambridge (1974)

    Google Scholar 

  17. Mielczarek, B., Zabawa, J., Dobrowolski, W.: The impact of demographic trends on future hospital demand based on a hybrid simulation model. In: 2018 Winter Simulation Conference (WSC), pp. 1476–1487 (2018). https://doi.org/10.1109/WSC.2018.8632317

  18. Mielczarek, B., Zabawa, J.: Modelling demographic changes using simulation: supportive analyses for socioeconomic studies. Socioecon. Plann. Sci. 100938 (2020). https://doi.org/10.1016/j.seps.2020.100938

  19. Mielczarek, B., Zabawa, J.: Modelling population growth, shrinkage and aging using a hybrid simulation approach: application to healthcare. In: SIMULTECH 2016 - Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (2016)

    Google Scholar 

  20. Mielczarek, B., Zabawa, J.: Simulation model for studying impact of demographic, temporal, and geographic factors on hospital demand. In: Proceedings - Winter Simulation Conference, pp. 4498–4500 Institute of Electrical and Electronics Engineers Inc. (2017). https://doi.org/10.1109/WSC.2017.8248178

  21. Muravjovs, A. et al.: The use of discrete rate simulation paradigm to build models of inventory control systems. In: Proceedings - 2nd International Symposium on Stochastic Models in Reliability Engineering, Life Science, and Operations Management, SMRLO 2016, pp. 650–655. IEEE (2016). https://doi.org/10.1109/SMRLO.2016.115

  22. Onggo, B.S.S.: Parallel discrete-event simulation of population dynamics. In: Mason, S.J., Hill, R.R., Mönch, L., Rose, O., Jefferson, T., Fowler, J.W. (ed.) Proceedings - Winter Simulation Conference, Miami, FL, USA, pp. 1047–1054. IEEE Press, Piscataway (2008). https://doi.org/10.1109/WSC.2008.4736172

  23. Papetti, A.: Demographics and the natural real interest rate: historical and projected paths for the euro area. ECB Work. Pap. 2258 (2019). https://doi.org/10.2866/865031

  24. Siprelle, A.J., Phelps, R.A.: Simulation of bulk flow and high speed operations. In: Andradóttir, S., et al. (eds.) Winter Simulation Conference Proceedings, pp. 706–710, Atlanta, GE (1997). https://doi.org/10.1145/268437.268612

  25. van Sonsbeek, J.M.: Micro simulations on the effects of ageing-related policy measures. Econ. Model. 27(5), 968–979 (2010). https://doi.org/10.1016/j.econmod.2010.05.004

    Article  Google Scholar 

  26. Sung, H., Siegel, R.L., Rosenberg, P.S., Jemal, A.: Emerging cancer trends among young adults in the USA: analysis of a population-based cancer registry. Lancet Public Heal. 4(3), e137–e147 (2019). https://doi.org/10.1016/S2468-2667(18)30267-6

    Article  Google Scholar 

  27. Terlunen, S,. et al.: Adaption of the discrete rate-based simulation paradigm for tactical supply chain decisions. In: A., T. et al. (eds.) Proceedings - Winter Simulation Conference, Savannah, GA, pp. 2060–2071. IEEE (2015). https://doi.org/10.1109/WSC.2014.7020051

  28. Yu, W., Li, M., Ge, Y., Li, L. et al.: Transformation of potential medical demand in China: a system dynamics simulation model (2015). https://doi.org/10.1016/j.jbi.2015.08.015

  29. Zabawa, J., Radosiński, E.: Comparison of discrete rate modeling and discrete event simulation. Methodological and performance aspects. In: Świątek, J., Wilimowska, Z., Borzemski, L., Grzech, A. (eds.) Information Systems Architecture and Technology: Proceedings of 37th International Conference on Information Systems Architecture and Technology—ISAT 2016—Part III. AISC, vol. 523, pp. 153–164. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-46589-0_12

    Chapter  Google Scholar 

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Acknowledgments

This project was financed by a grant from the National Science Centre Poland, titled Simulation modeling of the demand for healthcare services. It was awarded based on Decision 2015/17/B/HS4/00306.

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Authors and Affiliations

  1. Department of Operations Research and Business Intelligence, Wrocław University of Science and Technology, 50-370, Wrocław, Poland

    Jacek Zabawa & Bożena Mielczarek

Authors
  1. Jacek Zabawa
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  2. Bożena Mielczarek
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Correspondence to Jacek Zabawa .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Zabawa, J., Mielczarek, B. (2021). An Attempt to Replace System Dynamics with Discrete Rate Modeling in Demographic Simulations. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12745. Springer, Cham. https://doi.org/10.1007/978-3-030-77970-2_21

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  • DOI: https://doi.org/10.1007/978-3-030-77970-2_21

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  • Online ISBN: 978-3-030-77970-2

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