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Large population limits of Markov processes on random networks

Author

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  • Lücke, Marvin
  • Heitzig, Jobst
  • Koltai, Péter
  • Molkenthin, Nora
  • Winkelmann, Stefanie

Abstract

We consider time-continuous Markovian discrete-state dynamics on random networks of interacting agents and study the large population limit. The dynamics are projected onto low-dimensional collective variables given by the shares of each discrete state in the system, or in certain subsystems, and general conditions for the convergence of the collective variable dynamics to a mean-field ordinary differential equation are proved. We discuss the convergence to this mean-field limit for a continuous-time noisy version of the so-called “voter model” on Erdős–Rényi random graphs, on the stochastic block model, and on random regular graphs. Moreover, a heterogeneous population of agents is studied.

Suggested Citation

  • Lücke, Marvin & Heitzig, Jobst & Koltai, Péter & Molkenthin, Nora & Winkelmann, Stefanie, 2023. "Large population limits of Markov processes on random networks," Stochastic Processes and their Applications, Elsevier, vol. 166(C).
    • Handle: RePEc:eee:spapps:v:166:y:2023:i:c:s0304414923001849
    DOI: 10.1016/j.spa.2023.09.007
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    References listed on IDEAS

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    1. Adri'an Carro & Ra'ul Toral & Maxi San Miguel, 2016. "The noisy voter model on complex networks," Papers 1602.06935, arXiv.org, revised Apr 2016.
    2. F. Slanina & H. Lavicka, 2003. "Analytical results for the Sznajd model of opinion formation," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 35(2), pages 279-288, September.
    3. Sven Banischa & Ricardo Lima & Tanya Araújo, 2012. "Agent based models and opinion dynamics as markov chains," Working Papers Department of Economics 2012/10, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    4. Keliger, Dániel & Horváth, Illés & Takács, Bálint, 2022. "Local-density dependent Markov processes on graphons with epidemiological applications," Stochastic Processes and their Applications, Elsevier, vol. 148(C), pages 324-352.
    5. Bhamidi, Shankar & Budhiraja, Amarjit & Wu, Ruoyu, 2019. "Weakly interacting particle systems on inhomogeneous random graphs," Stochastic Processes and their Applications, Elsevier, vol. 129(6), pages 2174-2206.
    6. Jan-Hendrik Niemann & Stefan Klus & Christof Schütte, 2021. "Data-driven model reduction of agent-based systems using the Koopman generator," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-23, May.
    7. Kurtz, Thomas G., 1978. "Strong approximation theorems for density dependent Markov chains," Stochastic Processes and their Applications, Elsevier, vol. 6(3), pages 223-240, February.
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