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Preferences, Homophily, and Social Learning

Author

Listed:
  • Ilan Lobel

    () (New York University Stern School of Business)

  • Evan Sadler

    () (New York University Stern School of Business)

Abstract

We study a model of social learning in networks where agents have heterogeneous preferences, and neighbors tend to have similar preferences---a phenomenon known as homophily. Using this model, we resolve a puzzle in the literature: theoretical models predict that preference diversity helps learning, and homophily slows learning, while empirical work suggests the opposite. We find that the density of network connections determines the impact of preference diversity and homophily on learning. When connections are sparse, diverse preferences are harmful to learning, and homophily may lead to substantial improvements. In a dense network, preference diversity is beneficial. The conflicting findings in prior work result from a focus on networks with different densities; theory has focused on dense networks, while empirical papers have studied sparse networks. Our results suggest that in complex networks containing both sparse and dense components, diverse preferences and homophily play complementary, beneficial roles.

Suggested Citation

  • Ilan Lobel & Evan Sadler, 2013. "Preferences, Homophily, and Social Learning," Working Papers 13-01, NET Institute.
  • Handle: RePEc:net:wpaper:1301
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    File URL: http://www.netinst.org/Lobel_Sadler_13-01.pdf
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    References listed on IDEAS

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    1. Gale, Douglas & Kariv, Shachar, 2003. "Bayesian learning in social networks," Games and Economic Behavior, Elsevier, vol. 45(2), pages 329-346, November.
    2. Celen, Bogachan & Kariv, Shachar, 2004. "Observational learning under imperfect information," Games and Economic Behavior, Elsevier, vol. 47(1), pages 72-86, April.
    3. Venkatesh Bala & Sanjeev Goyal, 1998. "Learning from Neighbours," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 595-621.
    4. Timothy G. Conley & Christopher R. Udry, 2010. "Learning about a New Technology: Pineapple in Ghana," American Economic Review, American Economic Association, vol. 100(1), pages 35-69, March.
    5. 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.
    6. Daron Acemoglu & Munther A. Dahleh & Ilan Lobel & Asuman Ozdaglar, 2011. "Bayesian Learning in Social Networks," Review of Economic Studies, Oxford University Press, vol. 78(4), pages 1201-1236.
    7. 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.
    8. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    9. Antonio Guarino & Antonella Ianni, 2010. "Bayesian Social Learning with Local Interactions," Games, MDPI, Open Access Journal, vol. 1(4), pages 1-21, October.
    10. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, Oxford University Press, vol. 107(3), pages 797-817.
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    Cited by:

    1. Mueller-Frank, Manuel & Arieliy, Itai, 2015. "Social Learning and the Vanishing Value of Private Information," IESE Research Papers D/1119, IESE Business School.

    More about this item

    Keywords

    Social Networks; Learning; Homophily;

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