 Multidimensional Social LearningItai Arieli and Manuel Mueller-Frank The Review of Economic Studies, 2019, vol. 86, issue 3, 913-940 Abstract: This article 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 herding model. The observation structure is determined by a random network. Every agent links to each of his preceding lattice neighbours independently with probability $p$, and observes the actions of all agents that are reachable via a directed path in the realized social network. For $m\geq 2$, we show that as $p<1$ goes to one, (1) so does the asymptotic proportion of agents who take the optimal action, (2) this holds for any informative signal distribution, and (3) bounded signal distributions might achieve higher expected welfare than unbounded signal distributions. In contrast, if signals are bounded and $p=1$, all agents select the suboptimal action with positive probability. Keywords: Social learning; Unbounded signals; Asymptotic learning; Random lattice; Percolation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:oup:restud:v:86:y:2019:i:3:p:913-940. Access Statistics for this article The Review of Economic Studies is currently edited by Thomas Chaney, Xavier d’Haultfoeuille, Andrea Galeotti, Bård Harstad, Nir Jaimovich, Katrine Loken, Elias Papaioannou, Vincent Sterk and Noam Yuchtman More articles in The Review of Economic Studies from Review of Economic Studies Ltd |
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