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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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    File URL: http://www.iese.edu/research/pdfs/WP-1117-E.pdf
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    1. 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.
    2. 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.
    3. 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.
    4. Celen, Bogachan & Kariv, Shachar, 2004. "Observational learning under imperfect information," Games and Economic Behavior, Elsevier, vol. 47(1), pages 72-86, April.
    5. In Ho Lee & Akos Valentinyi, 2000. "Noisy Contagion Without Mutation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 67(1), pages 47-56.
    6. Arieli, Itai & Mueller-Frank, Manuel, 2017. "Inferring beliefs from actions," Games and Economic Behavior, Elsevier, vol. 102(C), pages 455-461.
    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. Srinivas Arigapudi & Omer Edhan & Yuval Heller & Ziv Hellman, 2022. "Mentors and Recombinators: Multi-Dimensional Social Learning," Papers 2205.00278, arXiv.org, revised Nov 2023.
    2. Mueller-Frank, Manuel & Arieliy, Itai, 2015. "Social Learning and the Vanishing Value of Private Information," IESE Research Papers D/1119, IESE Business School.
    3. Aleksei Smirnov & Egor Starkov, 2024. "Designing Social Learning," Papers 2405.05744, arXiv.org, revised May 2024.
    4. Itai Arieli & Fedor Sandomirskiy & Rann Smorodinsky, 2020. "On social networks that support learning," Papers 2011.05255, arXiv.org.
    5. Amir Ban & Moran Koren, 2020. "A Practical Approach to Social Learning," Papers 2002.11017, arXiv.org.
    6. Alex Centeno, 2022. "A Structural Model for Detecting Communities in Networks," Papers 2209.08380, arXiv.org, revised Oct 2022.
    7. 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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