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Leaving us in tiers: can homophily be used to generate tiering effects?

Author

Listed:
  • Brian R. Hirshman

    (Carnegie Mellon University)

  • Jesse Charles

    (Carnegie Mellon University)

  • Kathleen M. Carley

    (Carnegie Mellon University)

Abstract

Substantial evidence indicates that our social networks are divided into tiers in which people have a few very close social support group, a larger set of friends, and a much larger number of relatively distant acquaintances. Because homophily—the principle that like seeks like—has been suggested as a mechanism by which people interact, it may also provide a mechanism that generates such frequencies and distributions. However, our multi-agent simulation tool, Construct, suggests that a slight supplement to a knowledge homophily model—the inclusion of several highly salient personal facts that are infrequently shared—can more successfully lead to the tiering behavior often observed in human networks than a simplistic homophily model. Our findings imply that homophily on both general and personal facts is necessary in order to achieve realistic frequencies of interaction and distributions of interaction partners. Implications of the model are discussed, and recommendations are provided for simulation designers seeking to use homophily models to explain human interaction patterns.

Suggested Citation

  • Brian R. Hirshman & Jesse Charles & Kathleen M. Carley, 2011. "Leaving us in tiers: can homophily be used to generate tiering effects?," Computational and Mathematical Organization Theory, Springer, vol. 17(4), pages 318-343, November.
  • Handle: RePEc:spr:comaot:v:17:y:2011:i:4:d:10.1007_s10588-011-9088-4
    DOI: 10.1007/s10588-011-9088-4
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    References listed on IDEAS

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    1. Cowan, Robin & Jonard, Nicolas, 2004. "Network structure and the diffusion of knowledge," Journal of Economic Dynamics and Control, Elsevier, vol. 28(8), pages 1557-1575, June.
    2. Wong, Ling Heng & Pattison, Philippa & Robins, Garry, 2006. "A spatial model for social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 360(1), pages 99-120.
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    Cited by:

    1. Davide Secchi & Raffaello Seri, 2017. "Controlling for false negatives in agent-based models: a review of power analysis in organizational research," Computational and Mathematical Organization Theory, Springer, vol. 23(1), pages 94-121, March.
    2. Xiao Xue & Shufang Wang & Baoyun Lu, 2015. "Computational Experiment Approach to Controlled Evolution of Procurement Pattern in Cluster Supply Chain," Sustainability, MDPI, vol. 7(2), pages 1-26, January.
    3. Liang Chen & Guy G. Gable & Haibo Hu, 2013. "Communication and organizational social networks: a simulation model," Computational and Mathematical Organization Theory, Springer, vol. 19(4), pages 460-479, December.
    4. Luis Almeida Costa & Joao Amaro de Matos, 2013. "Attitude change in arbitrarily large organizations," Nova SBE Working Paper Series wp579, Universidade Nova de Lisboa, Nova School of Business and Economics.
    5. Lei Xu & Ronggui Ding & Lei Wang, 2022. "How to facilitate knowledge diffusion in collaborative innovation projects by adjusting network density and project roles," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1353-1379, March.
    6. Luis Almeida Costa & João Amaro Matos, 2014. "Attitude change in arbitrarily large organizations," Computational and Mathematical Organization Theory, Springer, vol. 20(3), pages 219-251, September.

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