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Does Homophily Predict Consensus Times? Testing a Model of Network Structure via a Dynamic Process

Author

Listed:
  • Golub Benjamin

    (Harvard University and Massachusetts Institute of Technology)

  • Jackson Matthew O.

    (Stanford University, Santa Fe Institute, and CIFAR)

Abstract
We test theoretical results from Golub and Jackson (2012a), which are based on a random network model, regarding time to convergence of a learning/behavior-updating process. In particular, we see how well those theoretical results match the process when it is simulated on empirically observed high school friendship networks. This tests whether a parsimonious random network model mimics real-world networks with regard to predicting properties of a class of behavioral processes. It also tests whether our theoretical predictions for asymptotically large societies are accurate when applied to populations ranging from thirty to three thousand individuals. We find that the theoretical results account for more than half of the variation in convergence times on the real networks. We conclude that a simple multi-type random network model with types defined by simple observable attributes (age, sex, race) captures aspects of real networks that are relevant for a class of iterated updating processes.

Suggested Citation

  • Golub Benjamin & Jackson Matthew O., 2012. "Does Homophily Predict Consensus Times? Testing a Model of Network Structure via a Dynamic Process," Review of Network Economics, De Gruyter, vol. 11(3), pages 1-31, September.
    • Handle: RePEc:bpj:rneart:v:11:y:2012:i:3:n:9
    DOI: 10.1515/1446-9022.1367
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    References listed on IDEAS

    as
    1. Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2009. "Identification of peer effects through social networks," Journal of Econometrics, Elsevier, vol. 150(1), pages 41-55, May.
    2. Sergio Currarini & Matthew O. Jackson & Paolo Pin, 2009. "An Economic Model of Friendship: Homophily, Minorities, and Segregation," Econometrica, Econometric Society, vol. 77(4), pages 1003-1045, July.
    3. Jadbabaie, Ali & Molavi, Pooya & Sandroni, Alvaro & Tahbaz-Salehi, Alireza, 2012. "Non-Bayesian social learning," Games and Economic Behavior, Elsevier, vol. 76(1), pages 210-225.
    4. Corazzini, Luca & Pavesi, Filippo & Petrovich, Beatrice & Stanca, Luca, 2012. "Influential listeners: An experiment on persuasion bias in social networks," European Economic Review, Elsevier, vol. 56(6), pages 1276-1288.
    5. Antoni Calvó-Armengol & Eleonora Patacchini & Yves Zenou, 2009. "Peer Effects and Social Networks in Education," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(4), pages 1239-1267.
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    Cited by:

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    4. Juli'an Chitiva & Xavier Venel, 2024. "Continuous Social Networks," Papers 2407.11710, arXiv.org.
    5. Andrea Galeotti & Benjamin Golub & Sanjeev Goyal & Rithvik Rao, 2021. "Discord and Harmony in Networks," Papers 2102.13309, arXiv.org.

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