Are imperfect reviews helpful in social learning?

Tho Ngoc Le, Vijay G. Subramanian, Randall A. Berry

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Social learning encompasses situations in which agents attempt to learn from observing the actions of other agents. It is well known that in some cases this can lead to information cascades in which agents blindly follow the actions of others, even though this may not be optimal. Having agents provide reviews in addition to their actions provides one possible way to avoid 'bad cascades.' In this paper, we study one such model where agents sequentially decide whether or not to purchase a product, whose true value is either good or bad. If they purchase the item, agents also leave a review, which may be imperfect. Conditioning on the underlying state of the item, we study the impact of such reviews on the asymptotic properties of cascades. For a good underlying state, using Markov analysis we show that depending on the review quality, reviews may in fact increase the probability of a wrong cascade. On the other hand, for a bad underlying state, we use martingale analysis to bound the tail-probability of the time until a correct cascade happens.

Original languageEnglish (US)
Title of host publicationProceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781509018062
StatePublished - Aug 10 2016
Event2016 IEEE International Symposium on Information Theory, ISIT 2016 - Barcelona, Spain
Duration: Jul 10 2016Jul 15 2016

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095


Other2016 IEEE International Symposium on Information Theory, ISIT 2016

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics


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