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Dynamic increasing returns and innovation diffusion: bringing Polya Urn processes to the empirical data

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  • Giovanni Dosi
  • Alessio Moneta
  • Elena Stepanova
Abstract
The patterns of innovation diffusion are well approximated by the logistic curves. This is the robust empirical fact confirmed by many studies in innovations dynamics. Here, we show that the logistic pattern of innovation diffusion can be replicated by the time-dependent stochastic process with positive feedbacks along the diffusion trajectory. The dynamic increasing returns process is modelled by Polya Urns. So far, Urn models have been mostly used to study the [path-dependent] limit properties. On the contrary, this work focuses on the transient [finite time] properties studying the conditions under which urn models capture the logistic trajectories which often track empirical diffusion process. As examples, we calibrate the process to match several cases of diffusion of motor ships in European countries.

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  • Giovanni Dosi & Alessio Moneta & Elena Stepanova, 2019. "Dynamic increasing returns and innovation diffusion: bringing Polya Urn processes to the empirical data," Industry and Innovation, Taylor & Francis Journals, vol. 26(4), pages 461-478, April.
    • Handle: RePEc:taf:indinn:v:26:y:2019:i:4:p:461-478
    DOI: 10.1080/13662716.2018.1444978
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    1. Francesco Lamperti & Giovanni Dosi & Mauro Napoletano & Andrea Roventini & Alessandro Sapio, 2018. "And then he wasn't a she : Climate change and green transitions in an agent-based integrated assessment model," Working Papers hal-03443464, HAL.
    2. Fontanelli, Luca & Guerini, Mattia & Napoletano, Mauro, 2023. "International trade and technological competition in markets with dynamic increasing returns," Journal of Economic Dynamics and Control, Elsevier, vol. 149(C).
    3. Lamperti, F. & Dosi, G. & Napoletano, M. & Roventini, A. & Sapio, A., 2020. "Climate change and green transitions in an agent-based integrated assessment model," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    4. Sanjit Dhami & Paolo Zeppini, 2024. "Green Technology Adoption under Uncertainty, Increasing Returns, and Complex Adaptive Dynamics," GREDEG Working Papers 2024-20, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    5. Joseph Hickey, 2023. "Simple model of market share dynamics based on clients' firm-switching decisions," Papers 2304.08727, arXiv.org, revised Nov 2023.
    6. Hickey, Joseph, 2024. "Simple model of market share dynamics based on clients’ firm-switching decisions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).
    7. Leonardo Ciambezi & Mattia Guerini & Mauro Napoletano & Andrea Roventini, 2023. "Accounting for the Multiple Sources of Inflation: an Agent-Based Model Investigation," GREDEG Working Papers 2023-14, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France, revised Jun 2024.
    8. Matthijs J. Janssen & Koen Frenken & Elena M. Tur & Alexander S. Alexiev, 2022. "The perils of pleasing: Innovation-stifling effects of customized service provision," Journal of Evolutionary Economics, Springer, vol. 32(4), pages 1231-1264, September.
    9. Giovanni Dosi & Anna Snaidero, 2024. "The nature and the strength of agglomeration drivers and their technological specificities," LEM Papers Series 2024/07, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

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