IDEAS home Printed from https://ideas.repec.org/p/cte/wsrepe/ws104830.html
   My bibliography  Save this paper

Networks and collective action

Author

Listed:
  • Flores Díaz, Ramón Jesús
  • Koster, Maurice
  • Lindner, Ines
  • Molina, Elisenda

Abstract

Given a social network, we are interested in the problem of measuring the influence of a group of agents to lead the society to adopt their behavior. Motivated by the description of terrorist movements, we provide a markovian dynamical model for non-symmetric societies, which takes into account two special features: the hard core terrorist group cannot be influenced, and the remaining agents may change from active to non-active and vice versa during the process. In this setting, we interpret the absorption time of the model, which measures how quickly the terrorist movement achieve the support of all society, as a group measure of power. In some sense, our model generalizes the classical approach of DeGroot to consensus formation

Suggested Citation

  • Flores Díaz, Ramón Jesús & Koster, Maurice & Lindner, Ines & Molina, Elisenda, 2010. "Networks and collective action," DES - Working Papers. Statistics and Econometrics. WS ws104830, Universidad Carlos III de Madrid. Departamento de Estadística.
  • Handle: RePEc:cte:wsrepe:ws104830
    as

    Download full text from publisher

    File URL: https://e-archivo.uc3m.es/rest/api/core/bitstreams/e3796c1e-b4ae-48fa-b3f6-287642e64c00/content
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. repec:oxf:wpaper:303 is not listed on IDEAS
    2. H Peyton Young, 2000. "The Diffusion of Innovations in Social Networks," Economics Working Paper Archive 437, The Johns Hopkins University,Department of Economics.
    3. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    4. Dunia López-Pintado & Duncan J. Watts, 2008. "Social Influence, Binary Decisions and Collective Dynamics," Rationality and Society, , vol. 20(4), pages 399-443, November.
    5. 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.
    6. Brown, Jacqueline Johnson & Reingen, Peter H, 1987. "Social Ties and Word-of-Mouth Referral Behavior," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(3), pages 350-362, December.
    7. Conlisk, John, 1982. "The law of supply and demand as a law of markov chains," Journal of Economic Theory, Elsevier, vol. 26(1), pages 1-16, February.
    8. Schelling, Thomas C, 1969. "Models of Segregation," American Economic Review, American Economic Association, vol. 59(2), pages 488-493, May.
    9. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    10. Stephen P. Borgatti, 2006. "Identifying sets of key players in a social network," Computational and Mathematical Organization Theory, Springer, vol. 12(1), pages 21-34, April.
    Full references (including those not matched with items on IDEAS)

    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. H Peyton Young & Lucas Merrill Brown, 2016. "The Diffusion of a Social Innovation: Executive Stock Options from 1936," Economics Series Working Papers 777, University of Oxford, Department of Economics.
    2. Sharad Goel & Ashton Anderson & Jake Hofman & Duncan J. Watts, 2016. "The Structural Virality of Online Diffusion," Management Science, INFORMS, vol. 62(1), pages 180-196, January.
    3. Florian Probst & Laura Grosswiele & Regina Pfleger, 2013. "Who will lead and who will follow: Identifying Influential Users in Online Social Networks," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 5(3), pages 179-193, June.
    4. Michael Braun & André Bonfrer, 2011. "Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes," Marketing Science, INFORMS, vol. 30(3), pages 513-531, 05-06.
    5. Fontecha, John E. & Walteros, Jose L. & Nikolaev, Alexander, 2021. "Reach maximization for social lotteries," Omega, Elsevier, vol. 105(C).
    6. 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.
    7. William Rand & Christian Stummer, 2021. "Agent‐based modeling of new product market diffusion: an overview of strengths and criticisms," Annals of Operations Research, Springer, vol. 305(1), pages 425-447, October.
    8. Pradelski, Bary S.R., 2023. "Social influence: The Usage History heuristic," Mathematical Social Sciences, Elsevier, vol. 123(C), pages 105-113.
    9. Tuan Q. Phan & David Godes, 2018. "The Evolution of Influence Through Endogenous Link Formation," Marketing Science, INFORMS, vol. 37(2), pages 259-278, March.
    10. Sergio Currarini & Carmen Marchiori & Alessandro Tavoni, 2016. "Network Economics and the Environment: Insights and Perspectives," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 65(1), pages 159-189, September.
    11. Sgrignoli, P. & Agliari, E. & Burioni, R. & Schianchi, A., 2015. "Instability and network effects in innovative markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 108(C), pages 260-271.
    12. Edouard Civel & Marc Baudry, 2018. "The Fate of Inventions. What can we learn from Bayesian learning in strategic options model of adoption ?," EconomiX Working Papers 2018-47, University of Paris Nanterre, EconomiX.
    13. Norman Braun, 1995. "Individual Thresholds and Social Diffusion," Rationality and Society, , vol. 7(2), pages 167-182, April.
    14. Mercure, Jean-François, 2018. "Fashion, fads and the popularity of choices: Micro-foundations for diffusion consumer theory," Structural Change and Economic Dynamics, Elsevier, vol. 46(C), pages 194-207.
    15. Michel Grabisch & Agnieszka Rusinowska, 2015. "Lattices in Social Networks with Influence," International Game Theory Review (IGTR), World Scientific Publishing Co. Pte. Ltd., vol. 17(01), pages 1-18.
    16. Bouveret, Géraldine & Mandel, Antoine, 2021. "Social interactions and the prophylaxis of SI epidemics on networks," Journal of Mathematical Economics, Elsevier, vol. 93(C).
    17. Andrea Ellero & Annamaria Sorato & Giovanni Fasano, 2011. "A new model for estimating the probability of information spreading with opinion leaders," Working Papers 13, Department of Management, Università Ca' Foscari Venezia.
    18. Christoph Engel & Alon Klement & Karen Weinshall Margel, 2017. "Diffusion of Legal Innovations: The Case of Israeli Class Actions," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2017_11, Max Planck Institute for Research on Collective Goods, revised Jan 2018.
    19. Claus, Bart & Geyskens, Kelly & Millet, Kobe & Dewitte, Siegfried, 2012. "The referral backfire effect: The identity-threatening nature of referral failure," International Journal of Research in Marketing, Elsevier, vol. 29(4), pages 370-379.
    20. Cantono, Simona, 2012. "Unveiling diffusion dynamics: an autocatalytic percolation model of environmental innovation diffusion and the optimal dynamic path of adoption subsidies," Department of Economics and Statistics Cognetti de Martiis LEI & BRICK - Laboratory of Economics of Innovation "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio 201222, University of Turin.

    More about this item

    Keywords

    Collective action;

    JEL classification:

    • C79 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Other
    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles
    • D71 - Microeconomics - - Analysis of Collective Decision-Making - - - Social Choice; Clubs; Committees; Associations

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:cte:wsrepe:ws104830. 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: Ana Poveda (email available below). General contact details of provider: http://portal.uc3m.es/portal/page/portal/dpto_estadistica .

    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.