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Networks and Collective Action

Author

Listed:
  • Ramon Flores

    (Universidad Carlos III de Madrid)

  • Maurice Koster

    (University of Amsterdam)

  • Ines Lindner

    (VU University Amsterdam)

  • Elisenda Molina

    (Universidad Carlos III de Madrid)

Abstract

This discussion paper resulted in a publication in Social Networks (2012). Vol. 34(4), pages 161-179. This paper proposes a new measure for a group's ability to lead society to adopt their standard of behavior, which in particular takes account of the time the group takes to convince the whole society to adopt their position. This notion of a group's power to initiate action is computed as the reciprocal of the resistance against it, which is in turn given by the expected absorption time of a related finite state partial Markov chain that captures the social dynamics. The measure is applicable and meaningful in a variety of models where interaction between agents is formalized through (weighted) binary relations. Using Percolation Theory, it is shown that the group power is monotonic as a function of groups of agents. We also explain the differences between our measure and those discussed in the literature on Graph Theory, and illustrate all these concerns by a thorough analysis of two particular cases: the Wolfe Primate Data and the 11S hijackers' network.

Suggested Citation

  • Ramon Flores & Maurice Koster & Ines Lindner & Elisenda Molina, 2012. "Networks and Collective Action," Tinbergen Institute Discussion Papers 12-032/1, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20120032
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    References listed on IDEAS

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    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.
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    More about this item

    Keywords

    Collective action; Social networks; Influence and diffusion models; Network intervention; Group centrality measures;
    All these keywords.

    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

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