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Integrating simulation and signal processing in tracking complex social systems

Author

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  • Fan Yang

    (University at Buffalo)

  • Wen Dong

    (University at Buffalo)

Abstract

Data that continuously track the dynamics of large populations have the potential to revolutionize how we study complex social systems. However, coping with massive, noisy, unstructured, and disparate data streams is not easy. In this paper, we describe a particle filter algorithm that integrates signal processing and simulation modeling to study complex social systems using massive, noisy, unstructured data. This integration enables researchers to specify and track the dynamics of real-world complex social systems by building a simulation model. To show the effectiveness of this algorithm, we infer city-scale traffic dynamics from the observed trajectories of a small number of probe vehicles uniformly sampled from the system. The results show that our model can not only track and predict human mobility, but also explain how traffic is generated through the movements of individual vehicles. The algorithm and its application point to a new way of bringing together modelers and data miners to turn the real world into a living lab.

Suggested Citation

  • Fan Yang & Wen Dong, 2020. "Integrating simulation and signal processing in tracking complex social systems," Computational and Mathematical Organization Theory, Springer, vol. 26(1), pages 1-22, March.
    • Handle: RePEc:spr:comaot:v:26:y:2020:i:1:d:10.1007_s10588-018-9276-6
    DOI: 10.1007/s10588-018-9276-6
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    References listed on IDEAS

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    1. Sebastiano A. Delre & Wander Jager & Marco A. Janssen, 2007. "Diffusion dynamics in small-world networks with heterogeneous consumers," Computational and Mathematical Organization Theory, Springer, vol. 13(2), pages 185-202, June.
    2. Francis Heylighen, 1999. "Collective Intelligence and its Implementation on the Web: Algorithms to Develop a Collective Mental Map," Computational and Mathematical Organization Theory, Springer, vol. 5(3), pages 253-280, October.
    3. Stephen P. Borgatti, 2006. "Identifying sets of key players in a social network," Computational and Mathematical Organization Theory, Springer, vol. 12(1), pages 21-34, April.
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