Quantifying the utility of imperfect reviews in stopping information cascades

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

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

2 Scopus citations

Abstract

In models of social learning where rational agents observe other agents' actions, information cascades are said to occur when agents ignore their private information and blindly follow the actions of other. It is well known that in some cases, incorrect cascades happen with positive probability leading to a loss in social welfare. Having agents provide reviews in addition to their actions provides one possible way to avoid such 'bad cascades.' In this paper, we study one such model where agents sequentially decide whether or not to purchase a good, whose true value is either 'good' or 'bad.' If they purchase, agents also leave a review, which is imperfect. We study the impact of such reviews on the asymptotic properties of cascades. For a good underlying state, we propose an algorithm that utilizes number theory principles and Markov chain analysis to solve for the probability of a wrong cascade. We discover that the probability of a wrong cascade is a non-monotonic function of the review strength. On the other hand, for a bad underlying state, the agents always eventually reach a correct cascade; we use a martingale analysis to bound the time until this happens.

Original languageEnglish (US)
Title of host publication2016 IEEE 55th Conference on Decision and Control, CDC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6990-6995
Number of pages6
ISBN (Electronic)9781509018376
DOIs
StatePublished - Dec 27 2016
Event55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States
Duration: Dec 12 2016Dec 14 2016

Publication series

Name2016 IEEE 55th Conference on Decision and Control, CDC 2016

Other

Other55th IEEE Conference on Decision and Control, CDC 2016
CountryUnited States
CityLas Vegas
Period12/12/1612/14/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Decision Sciences (miscellaneous)
  • Control and Optimization

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