The impact of observation and action errors on informational cascades

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

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

13 Scopus citations


In models of observational learning among Bayesian agents, informational cascades can result, in which agents ignore their private information and blindly follow the actions of other agents. This paper considers the impacts of two types of errors in such models: action errors, where agents occasionally choose sub-optimal actions and observation errors, where agents observe the action of another agent incorrectly. We investigate and compare the impact of these two types of errors on the agents' welfare and the probability of incorrect cascade. Using a Markov chain model, we derive the net payoff of each agent as a function of his private signal quality and the error rates. A main result of this analysis is that in certain cases, increasing the observation error rate can lead to higher welfare for all but a finite number of agents.

Original languageEnglish (US)
Title of host publication53rd IEEE Conference on Decision and Control,CDC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781479977468
StatePublished - 2014
Event2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States
Duration: Dec 15 2014Dec 17 2014

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Other2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014
Country/TerritoryUnited States
CityLos Angeles

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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