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Ising-like agent-based technology diffusion model: Adoption patterns vs. seeding strategies

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  • Laciana, Carlos E.
  • Rovere, Santiago L.

Abstract

The well-known Ising model used in statistical physics was adapted to a social dynamics context to simulate the adoption of a technological innovation. The model explicitly combines (a) an individual’s perception of the advantages of an innovation and (b) social influence from members of the decision-maker’s social network. The micro-level adoption dynamics are embedded into an agent-based model that allows exploration of macro-level patterns of technology diffusion throughout systems with different configurations (number and distributions of early adopters, social network topologies). In the present work we carry out many numerical simulations. We find that when the gap between the individual’s perception of the options is high, the adoption speed increases if the dispersion of early adopters grows. Another test was based on changing the network topology by means of stochastic connections to a common opinion reference (hub), which resulted in an increment in the adoption speed. Finally, we performed a simulation of competition between options for both regular and small world networks.

Suggested Citation

  • Laciana, Carlos E. & Rovere, Santiago L., 2011. "Ising-like agent-based technology diffusion model: Adoption patterns vs. seeding strategies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1139-1149.
  • Handle: RePEc:eee:phsmap:v:390:y:2011:i:6:p:1139-1149
    DOI: 10.1016/j.physa.2010.11.006
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    References listed on IDEAS

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    1. Vega-Redondo,Fernando, 2007. "Complex Social Networks," Cambridge Books, Cambridge University Press, number 9780521857406, December.
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    Citations

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    Cited by:

    1. Lin, Ying-Ting & Han, Xiao-Pu & Wang, Bing-Hong, 2014. "Dynamics of human innovative behaviors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 74-81.
    2. Markus Brede, 2019. "How Does Active Participation Affect Consensus: Adaptive Network Model of Opinion Dynamics and Influence Maximizing Rewiring," Complexity, Hindawi, vol. 2019, pages 1-16, June.
    3. Laciana, Carlos E. & Rovere, Santiago L. & Podestá, Guillermo P., 2013. "Exploring associations between micro-level models of innovation diffusion and emerging macro-level adoption patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(8), pages 1873-1884.
    4. Laciana, Carlos E. & Oteiza-Aguirre, Nicolás, 2014. "An agent based multi-optional model for the diffusion of innovations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 254-265.
    5. Bodo, Peter, 2016. "MADness in the method: On the volatility and irregularity of technology diffusion," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 2-11.
    6. Robinson, Scott A. & Rai, Varun, 2015. "Determinants of spatio-temporal patterns of energy technology adoption: An agent-based modeling approach," Applied Energy, Elsevier, vol. 151(C), pages 273-284.
    7. Laciana, C.E. & Gual, G. & Kalmus, D. & Oteiza-Aguirre, N. & Rovere, S.L., 2014. "Diffusion of two brands in competition: Cross-brand effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 104-115.
    8. Peres, Renana, 2014. "The impact of network characteristics on the diffusion of innovations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 330-343.

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