The speed of sequential asymptotic learning

Wade Hann-Caruthers, Vadim V. Martynov and Omer Tamuz

Journal of Economic Theory, 2018, vol. 173, issue C, 383-409

Abstract: In the classical herding literature, agents receive a private signal regarding a binary state of nature, and sequentially choose an action, after observing the actions of their predecessors. When the informativeness of private signals is unbounded, it is known that agents converge to the correct action and correct belief. We study how quickly convergence occurs, and show that it happens more slowly than it does when agents observe signals. However, we also show that the speed of learning from actions can be arbitrarily close to the speed of learning from signals. In particular, the expected time until the agents stop taking the wrong action can be either finite or infinite, depending on the private signal distribution. In the canonical case of Gaussian private signals we calculate the speed of convergence precisely, and show explicitly that, in this case, learning from actions is significantly slower than learning from signals.

Keywords: Social learning; Herd behavior (search for similar items in EconPapers)
JEL-codes: D83 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Working Paper: The speed of sequential asymptotic learning (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jetheo:v:173:y:2018:i:c:p:383-409

DOI: 10.1016/j.jet.2017.11.009

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