IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v390y2011i6p1139-1149.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437110009532
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2010.11.006?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Venkatesh Bala & Sanjeev Goyal, 1998. "Learning from Neighbours," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 595-621.
    2. Gérard Weisbuch & Gérard Boudjema, 1999. "Dynamical Aspects in the Adoption of Agri-Environmental Measures," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 2(01), pages 11-36.
    3. Vega-Redondo,Fernando, 2007. "Complex Social Networks," Cambridge Books, Cambridge University Press, number 9780521674096, September.
    4. Galam, Serge, 1997. "Rational group decision making: A random field Ising model at T = 0," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 238(1), pages 66-80.
    5. Vega-Redondo,Fernando, 2007. "Complex Social Networks," Cambridge Books, Cambridge University Press, number 9780521857406, September.
    6. Grabowski, Andrzej, 2009. "Opinion formation in a social network: The role of human activity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(6), pages 961-966.
    7. Grabowski, A. & Kosiński, R.A., 2006. "Ising-based model of opinion formation in a complex network of interpersonal interactions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 361(2), pages 651-664.
    8. Delre, S.A. & Jager, W. & Bijmolt, T.H.A. & Janssen, M.A., 2007. "Targeting and timing promotional activities: An agent-based model for the takeoff of new products," Journal of Business Research, Elsevier, vol. 60(8), pages 826-835, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Carlos E. Laciana & Santiago L. Rovere, 2010. "Ising-like agent-based technology diffusion model: adoption patterns vs. seeding strategies," Papers 1011.3834, arXiv.org, revised Jan 2013.
    2. 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.
    3. Jackson, Matthew O. & Zenou, Yves, 2015. "Games on Networks," Handbook of Game Theory with Economic Applications,, Elsevier.
    4. Giovanni Pegoretti & Francesco Rentocchini & Giuseppe Vittucci Marzetti, 2012. "An agent-based model of innovation diffusion: network structure and coexistence under different information regimes," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 7(2), pages 145-165, October.
    5. H Peyton Young & Itai Arieli & Yakov Babichenko & Ron Peretz, 2019. "The Speed of Innovation Diffusion in Social Networks," Economics Series Working Papers 884, University of Oxford, Department of Economics.
    6. 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.
    7. Opolot, Daniel, 2012. "Social interactions and complex networks," MERIT Working Papers 2012-014, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    8. Itai Arieli & Yakov Babichenko & Ron Peretz & H. Peyton Young, 2019. "The Speed of Innovation Diffusion in Social Networks," Economics Papers 2019-W07, Economics Group, Nuffield College, University of Oxford.
    9. Nikolas Tsakas, 2014. "Optimal influence under observational learning," Gecomplexity Discussion Paper Series 4, Action IS1104 "The EU in the new complex geography of economic systems: models, tools and policy evaluation", revised Nov 2014.
    10. Ruiz Palazuelos, Sofía, 2021. "Network Perception in Network Games," MPRA Paper 115212, University Library of Munich, Germany, revised 21 Jun 0022.
    11. 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.
    12. De Masi, G. & Giovannetti, G. & Ricchiuti, G., 2013. "Network analysis to detect common strategies in Italian foreign direct investment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1202-1214.
    13. Andrea Galeotti & Brian W. Rogers, 2013. "Strategic Immunization and Group Structure," American Economic Journal: Microeconomics, American Economic Association, vol. 5(2), pages 1-32, May.
    14. Bargigli, Leonardo & Gallegati, Mauro, 2011. "Random digraphs with given expected degree sequences: A model for economic networks," Journal of Economic Behavior & Organization, Elsevier, vol. 78(3), pages 396-411, May.
    15. Konno, Tomohiko, 2013. "An imperfect competition on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5453-5460.
    16. Arcaute, E. & Dyagilev, K. & Johari, R. & Mannor, S., 2013. "Dynamics in tree formation games," Games and Economic Behavior, Elsevier, vol. 79(C), pages 1-29.
    17. Cabrales, Antonio & Calvó-Armengol, Antoni & Zenou, Yves, 2011. "Social interactions and spillovers," Games and Economic Behavior, Elsevier, vol. 72(2), pages 339-360, June.
    18. Chakrabarti, Anindya S., 2015. "Stochastic Lotka-Volterra equations: A model of lagged diffusion of technology in an interconnected world," IIMA Working Papers WP2015-08-05, Indian Institute of Management Ahmedabad, Research and Publication Department.
    19. Arbex, Marcelo & Caetano, Sidney & O’Dea, Dennis, 2016. "The implications of labor market network for business cycles," Economics Letters, Elsevier, vol. 144(C), pages 37-40.
    20. Zenou, Yves, 2007. "Social Interactions and Labour Market Outcomes in Cities," CEPR Discussion Papers 6129, C.E.P.R. Discussion Papers.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:390:y:2011:i:6:p:1139-1149. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.