Bayesian Learning with Random Arrivals

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

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

4 Scopus citations


We add to a line of work considering the impact of observation imperfections in models of Bayesian observational learning. In particular, we study a discrete-time model in which in each time-slot, an agent may randomly arrive. Agents who arrive have the opportunity to buy a given item. If an agent chooses to buy, this action is recorded for subsequent agents. However, the decisions of agents that choose not to buy are not recorded. Hence, if no one buys in a given slot, agents are unaware if this was due to no agent arriving or an agent choosing not to buy. We study the impact of this uncertainty on the emergence of information cascades. Using a Markov chain based analysis, we show that the probability of incorrect cascades and the expected time until a cascade happens are not monotonic in the arrival probability of a user. We find that adding a small uncertainty in the arrival information from the perfect information setting will make a buy cascade happen with higher probability than a not-buy cascade. However, if the agents' private signals are weak, then a not-buy cascade is more likely to occur for most arrival rates, resulting in wrong cascades dominating when the item is good and vice-versa when the item is bad.

Original languageEnglish (US)
Title of host publication2018 IEEE International Symposium on Information Theory, ISIT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)9781538647806
StatePublished - Aug 15 2018
Event2018 IEEE International Symposium on Information Theory, ISIT 2018 - Vail, United States
Duration: Jun 17 2018Jun 22 2018

Publication series

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


Other2018 IEEE International Symposium on Information Theory, ISIT 2018
Country/TerritoryUnited States

ASJC Scopus subject areas

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


Dive into the research topics of 'Bayesian Learning with Random Arrivals'. Together they form a unique fingerprint.

Cite this