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Multi-Dimensional Social Learning

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This paper provides a model of social learning where the order in which actions are taken is determined by an m-dimensional integer lattice rather than along a line as in the sequential social learning model. The observation structure is determined by a random network. Every agent links to each of his preceding lattice neighbors independently with probability p, and observes the actions of all agents that are reachable via a directed path in the realized social network. We establish a strong discontinuity of learning with respect to the linkage probability. If p is close to but di¤erent from one an arbitrary high proportion of agents select the optimal action in the limit, for any informative signal structure. For bounded signals and a linkage probability equal to one, however, there exists a positive probability that all agents select the suboptimal action. We also show that for every p

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  • Mueller-Frank, Manuel & Arieliy, Itai, 2015. "Multi-Dimensional Social Learning," IESE Research Papers D/1117, IESE Business School.
    • Handle: RePEc:ebg:iesewp:d-1117
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    1. Celen, Bogachan & Kariv, Shachar, 2004. "Observational learning under imperfect information," Games and Economic Behavior, Elsevier, vol. 47(1), pages 72-86, April.
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    3. 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.
    4. Jacob K. Goeree & Thomas R. Palfrey & Brian W. Rogers & Richard D. McKelvey, 2007. "Self-Correcting Information Cascades," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 74(3), pages 733-762.
    5. Arieli, Itai & Mueller-Frank, Manuel, 2017. "Inferring beliefs from actions," Games and Economic Behavior, Elsevier, vol. 102(C), pages 455-461.
    6. Anderson, Lisa R & Holt, Charles A, 1997. "Information Cascades in the Laboratory," American Economic Review, American Economic Association, vol. 87(5), pages 847-862, December.
    7. Andrews, Donald W.K., 1988. "Laws of Large Numbers for Dependent Non-Identically Distributed Random Variables," Econometric Theory, Cambridge University Press, vol. 4(3), pages 458-467, December.
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    Cited by:

    1. Itai Arieli & Fedor Sandomirskiy & Rann Smorodinsky, 2020. "On social networks that support learning," Papers 2011.05255, arXiv.org.
    2. Amir Ban & Moran Koren, 2020. "A Practical Approach to Social Learning," Papers 2002.11017, arXiv.org.
    3. Srinivas Arigapudi & Omer Edhan & Yuval Heller & Ziv Hellman, 2022. "Mentors and Recombinators: Multi-Dimensional Social Learning," Papers 2205.00278, arXiv.org, revised Nov 2023.
    4. Alex Centeno, 2022. "A Structural Model for Detecting Communities in Networks," Papers 2209.08380, arXiv.org, revised Oct 2022.
    5. Mueller-Frank, Manuel & Arieliy, Itai, 2015. "Social Learning and the Vanishing Value of Private Information," IESE Research Papers D/1119, IESE Business School.
    6. Arieli, Itai & Koren, Moran & Smorodinsky, Rann, 2022. "The implications of pricing on social learning," Theoretical Economics, Econometric Society, vol. 17(4), November.

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    More about this item

    Keywords

    Social Learning; Lattice; informational cascades;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games

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