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The speed of innovation diffusion in social networks

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  • Arieli, Itai
  • Babichenko, Yakov
  • Peretz, Ron
  • Young, H. Peyton

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

  • Arieli, Itai & Babichenko, Yakov & Peretz, Ron & Young, H. Peyton, 2020. "The speed of innovation diffusion in social networks," LSE Research Online Documents on Economics 102538, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:102538
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    File URL: http://eprints.lse.ac.uk/102538/
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    References listed on IDEAS

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    1. Timothy Conley & Udry Christopher, 2001. "Social Learning Through Networks: The Adoption of New Agricultural Technologies in Ghana," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 668-673.
    2. Itai Arieli & H. Peyton Young, 2016. "Stochastic Learning Dynamics and Speed of Convergence in Population Games," Econometrica, Econometric Society, vol. 84, pages 627-676, March.
    3. Foster, Andrew D & Rosenzweig, Mark R, 1995. "Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture," Journal of Political Economy, University of Chicago Press, vol. 103(6), pages 1176-1209, December.
    4. Blume Lawrence E., 1993. "The Statistical Mechanics of Strategic Interaction," Games and Economic Behavior, Elsevier, vol. 5(3), pages 387-424, July.
    5. Benjamin Golub & Matthew O. Jackson, 2012. "How Homophily Affects the Speed of Learning and Best-Response Dynamics," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(3), pages 1287-1338.
    6. Burke, Mary A. & Fournier, Gary M. & Prasad, Kislaya, 2010. "Geographic variations in a model of physician treatment choice with social interactions," Journal of Economic Behavior & Organization, Elsevier, vol. 73(3), pages 418-432, March.
    7. Hans-Peter Kohler & Jere Behrman & Susan Watkins, 2001. "The density of social networks and fertility decisions: evidence from south nyanza district, kenya," Demography, Springer;Population Association of America (PAA), vol. 38(1), pages 43-58, February.
    8. Arieli, Itai & Young, H. Peyton, 2016. "Stochastic learning dynamics and speed of convergence in population games," LSE Research Online Documents on Economics 68715, London School of Economics and Political Science, LSE Library.
    9. Vega-Redondo,Fernando, 2007. "Complex Social Networks," Cambridge Books, Cambridge University Press, number 9780521674096, November.
    10. Munshi, Kaivan, 2004. "Social learning in a heterogeneous population: technology diffusion in the Indian Green Revolution," Journal of Development Economics, Elsevier, vol. 73(1), pages 185-213, February.
    11. Mary A. Burke & Gary M. Fournier & Kislaya Prasad, 2007. "The Diffusion of a Medical Innovation: Is Success in the Stars?," Southern Economic Journal, John Wiley & Sons, vol. 73(3), pages 588-603, January.
    12. Amitabh Chandra & Douglas O. Staiger, 2007. "Productivity Spillovers in Health Care: Evidence from the Treatment of Heart Attacks," Journal of Political Economy, University of Chicago Press, vol. 115(1), pages 103-140.
    13. Vega-Redondo,Fernando, 2007. "Complex Social Networks," Cambridge Books, Cambridge University Press, number 9780521857406, November.
    14. Ellison, Glenn & Fudenberg, Drew & Imhof, Lorens A., 2016. "Fast convergence in evolutionary models: A Lyapunov approach," Journal of Economic Theory, Elsevier, vol. 161(C), pages 1-36.
    15. Munshi, Kaivan & Myaux, Jacques, 2006. "Social norms and the fertility transition," Journal of Development Economics, Elsevier, vol. 80(1), pages 1-38, June.
    16. Elchanan Mossel & Allan Sly & Omer Tamuz, 2015. "Strategic Learning and the Topology of Social Networks," Econometrica, Econometric Society, vol. 83(5), pages 1755-1794, September.
    17. H. Peyton Young, 2009. "Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning," American Economic Review, American Economic Association, vol. 99(5), pages 1899-1924, December.
    18. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    19. Jackson, Matthew O. & Watts, Alison, 2002. "The Evolution of Social and Economic Networks," Journal of Economic Theory, Elsevier, vol. 106(2), pages 265-295, October.
    20. ,, 2013. "A general framework for rational learning in social networks," Theoretical Economics, Econometric Society, vol. 8(1), January.
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    3. Sawa, Ryoji & Wu, Jiabin, 2023. "Statistical inference in evolutionary dynamics," Games and Economic Behavior, Elsevier, vol. 137(C), pages 294-316.
    4. Chellig, Jordan & Durbac, Calina & Fountoulakis, Nikolaos, 2022. "Best response dynamics on random graphs," Games and Economic Behavior, Elsevier, vol. 131(C), pages 141-170.
    5. Ruoxi Ma & Shangguang Yang, 2023. "The Effect of Social Network on Controlled-Release Fertilizer Use: Evidence from Rice Large-Scale Farmers in Jiangsu Province, China," Sustainability, MDPI, vol. 15(4), pages 1-16, February.
    6. Rehse, Dominik & Tremöhlen, Felix, 2022. "Fostering participation in digital contact tracing," Information Economics and Policy, Elsevier, vol. 58(C).
    7. Ryoji Sawa, 2022. "Statistical Inference in Evolutionary Dynamics," Working Papers e170, Tokyo Center for Economic Research.
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    9. 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.
    10. 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.
    11. Srinivas Arigapudi & Yuval Heller & Amnon Schreiber, 2023. "Heterogeneous Noise and Stable Miscoordination," Papers 2305.10301, arXiv.org, revised Jan 2025.
    12. Trieste, Leopoldo & Geisler, Elie & Turchetti, Giuseppe, 2022. "Columbus' egg and the engineer's effect in forecasting solutions adoption," Technological Forecasting and Social Change, Elsevier, vol. 183(C).

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    More about this item

    Keywords

    Innovation diffusion; social networks; speed of equilibrium convergence;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics

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