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Dynamic hybrid modelling: Switching between AB and SD designs of a predator-prey model

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  • Wallentin, Gudrun
  • Neuwirth, Christian

Abstract

Entities and processes in complex systems are of diverse nature and operate at various spatial and temporal scales. Hybrid agent-based (AB) and system dynamics (SD) models have been suggested to capture the essence of these systems in a natural and computationally efficient way. However, the integration of the equation-based SD and individual-based AB models is not least challenged by considerable conceptual differences between these models. Examples of tightly integrated and dynamically switching hybrid models are rare. The aim of this paper is to expand on theoretical frameworks of hybrid agent-based and system dynamics models in ecology to support the model design process of dynamically switching hybrid models. We suggested six alternative model designs that switched between the two modelling paradigms. By the example of a fish-plankton lake ecosystem we demonstrated that a well-designed switching hybrid model can be a performant modelling approach that retains relevant spatial and attributive information. Important findings with respect to optimising computational versus predictive performance were (1) the most plausible results were produced by a spatially explicit design based on spatial plankton stocks and fish switching between individual agents and aggregate school-agents, (2) higher levels of aggregation did not necessarily result in higher computational performance, and (3) adaptive, emergence-based triggers for the paradigm switches minimised information loss and could connect hierarchical and spatial scales. In conclusion, we argue to reach beyond efficiency-oriented considerations and use emergent super-individuals as structural elements of dynamically switching hybrid models.

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  • Wallentin, Gudrun & Neuwirth, Christian, 2017. "Dynamic hybrid modelling: Switching between AB and SD designs of a predator-prey model," Ecological Modelling, Elsevier, vol. 345(C), pages 165-175.
    • Handle: RePEc:eee:ecomod:v:345:y:2017:i:c:p:165-175
    DOI: 10.1016/j.ecolmodel.2016.11.007
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    References listed on IDEAS

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    1. Vincenot, Christian Ernest & Giannino, Francesco & Rietkerk, Max & Moriya, Kazuyuki & Mazzoleni, Stefano, 2011. "Theoretical considerations on the combined use of System Dynamics and individual-based modeling in ecology," Ecological Modelling, Elsevier, vol. 222(1), pages 210-218.
    2. Dowlatabadi, Hadi, 1995. "Integrated assessment models of climate change : An incomplete overview," Energy Policy, Elsevier, vol. 23(4-5), pages 289-296.
    3. Parry, Hazel R. & Evans, Andrew J., 2008. "A comparative analysis of parallel processing and super-individual methods for improving the computational performance of a large individual-based model," Ecological Modelling, Elsevier, vol. 214(2), pages 141-152.
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    Cited by:

    1. Takuro Uehara & Mateo Cordier & Bertrand Hamaide, 2018. "Fully Dynamic Input-Output/System Dynamics Modeling for Ecological-Economic System Analysis," Sustainability, MDPI, vol. 10(6), pages 1-22, May.
    2. Wallentin, Gudrun, 2017. "Spatial simulation: A spatial perspective on individual-based ecology—a review," Ecological Modelling, Elsevier, vol. 350(C), pages 30-41.
    3. Małgorzata Latuszynska & Shivan Fate, 2019. "A Hybrid Simulation Approach to Modelling the Impact of Public Interventions on Poverty," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 347-363.
    4. Takuro Uehara & Mateo Cordier & Bertrand Hamaide, 2018. "Fully dynamic input-output/system dynamics modeling for ecological-economic system analysis," ULB Institutional Repository 2013/277116, ULB -- Universite Libre de Bruxelles.
    5. Justin Pence & Zahra Mohaghegh, 2020. "A Discourse on the Incorporation of Organizational Factors into Probabilistic Risk Assessment: Key Questions and Categorical Review," Risk Analysis, John Wiley & Sons, vol. 40(6), pages 1183-1211, June.
    6. Khamdamov, T., 2022. "A brief overview of the evolution of computer simulations in economic research," Journal of the New Economic Association, New Economic Association, vol. 54(2), pages 189-207.
    7. Takuro Uehara & Mateo Cordier & Bertrand Hamaide, 2018. "Fully Dynamic Input-Output/System Dynamics Modeling for Ecological-Economic System Analysis," Post-Print hal-02862512, HAL.
    8. Dianat, Fateme & Khodakarami, Vahid & Hosseini, Seyed-Hossein & Shakouri G, Hamed, 2022. "Combining game theory concepts and system dynamics for evaluating renewable electricity development in fossil-fuel-rich countries in the Middle East and North Africa," Renewable Energy, Elsevier, vol. 190(C), pages 805-821.
    9. Droz, Michel & Pękalski, Andrzej, 2019. "Tolerance-fecundity trade-off on a homogeneous habitat," Ecological Modelling, Elsevier, vol. 411(C).
    10. Wang, Jidong & Wu, Jiahui & Che, Yanbo, 2019. "Agent and system dynamics-based hybrid modeling and simulation for multilateral bidding in electricity market," Energy, Elsevier, vol. 180(C), pages 444-456.
    11. Joana Cunha & Vasco Reis & Paulo Teixeira, 2022. "Development of an agent-based model for railway infrastructure project appraisal," Transportation, Springer, vol. 49(6), pages 1649-1681, December.

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