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The Speed of Innovation Diffusion in Social Networks

Author

Listed:
  • Itai Arieli
  • Yakov Babichenko
  • Ron Peretz
  • H. Peyton Young

Abstract

New ways of doing things often get started through the actions of a few innovators, then diffuse rapidly as more and more people come into contact with prior adopters in their social network. Much of the literature focuses on the speed of diffusion as a function of the network topology. In practice, the topology may not be known with any precision, and it is constantly in flux as links are formed and severed. Here, we establish an upper bound on the expected waiting time until a given proportion of the population has adopted that holds independently of the network structure. Kreindler and Young (2014) demonstrated such a bound for regular networks when agents choose between two options: the innovation and the status quo. Our bound holds for directed and undirected networks of arbitrary size and degree distribution, and for multiple competing innovations with different payoffs.

Suggested Citation

  • Itai Arieli & Yakov Babichenko & Ron Peretz & H. Peyton Young, 2020. "The Speed of Innovation Diffusion in Social Networks," Econometrica, Econometric Society, vol. 88(2), pages 569-594, March.
    • Handle: RePEc:wly:emetrp:v:88:y:2020:i:2:p:569-594
    DOI: 10.3982/ECTA17007
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    References listed on IDEAS

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    4. Rehse, Dominik & Tremöhlen, Felix, 2022. "Fostering participation in digital contact tracing," Information Economics and Policy, Elsevier, vol. 58(C).
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    7. Delia Coculescu & M'ed'eric Motte & Huy^en Pham, 2023. "Opinion dynamics in communities with major influencers and implicit social influence via mean-field approximation," Papers 2306.16553, arXiv.org.
    8. Julian Hidalgo & Michelle Sovinsky, 2023. "Internet (Power) to the People: How to Bridge the Digital Divide," CRC TR 224 Discussion Paper Series crctr224_2023_461, University of Bonn and University of Mannheim, Germany.
    9. Ryoji Sawa, 2022. "Statistical Inference in Evolutionary Dynamics," Working Papers e170, Tokyo Center for Economic Research.
    10. Gong, Qingbin & Diao, Xundi, 2023. "The impacts of investor network and herd behavior on market stability: Social learning, network structure, and heterogeneity," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1388-1398.
    11. Marcel Fafchamps & Måns Söderbom & Monique van den Boogart, 2022. "Adoption with Social Learning and Network Externalities," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(6), pages 1259-1282, December.
    12. Vincent Leon & S. Rasoul Etesami & Rakesh Nagi, 2022. "Limited-Trust in Diffusion of Competing Alternatives over Social Networks," Papers 2206.06318, arXiv.org, revised Oct 2023.
    13. Rehse, Dominik & Tremöhlen, Felix, 2020. "Fostering participation in digital public health interventions: The case of digital contact tracing," ZEW Discussion Papers 20-076, ZEW - Leibniz Centre for European Economic Research.

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