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Resource sharing in technologically defined social networks

Author

Listed:
  • Hirokazu Shirado

    (Yale University
    Yale University)

  • George Iosifidis

    (Trinity College Dublin
    SFI Research Centre CONNECT)

  • Leandros Tassiulas

    (Yale University
    Yale University)

  • Nicholas A. Christakis

    (Yale University
    Yale University
    Yale University)

Abstract

Technologically enabled sharing-economy networks are changing the way humans trade and collaborate. Here, using a novel ‘Wi-Fi sharing’ game, we explored determinants of human sharing strategy. Subjects (N = 1,950) participated in a networked game in which they could choose how to allocate a limited, but personally not usable, resource (representing unused Wi-Fi bandwidth) to immediate network neighbors. We first embedded N = 600 subjects into 30 networks, experimentally manipulating the range over which subjects could connect. We find that denser networks decrease any wealth inequality, but that this effect saturates. Individuals’ benefit is shaped by their network position, with having many partners who in turn have few partners being especially beneficial. We propose a new, simplified “sharing centrality” metric for quantifying this. Further experiments (N = 1,200) confirm the robustness of the effect of network structure on sharing behavior. Our findings suggest the possibility of interventions to help more evenly distribute shared resources over networks.

Suggested Citation

  • Hirokazu Shirado & George Iosifidis & Leandros Tassiulas & Nicholas A. Christakis, 2019. "Resource sharing in technologically defined social networks," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-08935-2
    DOI: 10.1038/s41467-019-08935-2
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    Cited by:

    1. Li Xiong & Chengwen Wang & Zhaoran Xu, 2022. "Supply and demand matching model of P2P sharing accommodation platforms considering fairness," Electronic Commerce Research, Springer, vol. 22(3), pages 951-978, September.
    2. Guo, H. & Jia, D. & Sendiña-Nadal, I. & Zhang, M. & Wang, Z. & Li, X. & Alfaro-Bittner, K. & Moreno, Y. & Boccaletti, S., 2021. "Evolutionary games on simplicial complexes," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    3. Kevin R. McKee & Andrea Tacchetti & Michiel A. Bakker & Jan Balaguer & Lucy Campbell-Gillingham & Richard Everett & Matthew Botvinick, 2023. "Scaffolding cooperation in human groups with deep reinforcement learning," Nature Human Behaviour, Nature, vol. 7(10), pages 1787-1796, October.
    4. Lucas, Benjamin & Francu, R. Elena & Goulding, James & Harvey, John & Nica-Avram, Georgiana & Perrat, Bertrand, 2021. "A Note on Data-driven Actor-differentiation and SDGs 2 and 12: Insights from a Food-sharing App," Research Policy, Elsevier, vol. 50(6).

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