Learning from randomly arriving agents

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

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

Abstract

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 who do not choose 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 incorrect cascades may occur and that the probability of such cascades is not monotonic in the arrival probability of a user. Moreover, if the agents' private signals are weak, wrong cascades are more likely to happen than correct cascades.

Original languageEnglish (US)
Title of host publication55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages196-197
Number of pages2
ISBN (Electronic)9781538632666
DOIs
StatePublished - Jan 17 2018
Event55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017 - Monticello, United States
Duration: Oct 3 2017Oct 6 2017

Publication series

Name55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017
Volume2018-January

Other

Other55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017
CountryUnited States
CityMonticello
Period10/3/1710/6/17

Fingerprint

Cascade
Choose
Learning
Discrete-time Model
Imperfections
Monotonic
Markov processes
Markov chain
Likely
Uncertainty
Defects
Line
Model

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Energy Engineering and Power Technology
  • Control and Optimization

Cite this

Le, T. N., Subramanian, V. G., & Berry, R. A. (2018). Learning from randomly arriving agents. In 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017 (pp. 196-197). (55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ALLERTON.2017.8262737
Le, Tho Ngoc ; Subramanian, Vijay G. ; Berry, Randall A. / Learning from randomly arriving agents. 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 196-197 (55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017).
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title = "Learning from randomly arriving agents",
abstract = "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 who do not choose 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 incorrect cascades may occur and that the probability of such cascades is not monotonic in the arrival probability of a user. Moreover, if the agents' private signals are weak, wrong cascades are more likely to happen than correct cascades.",
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Le, TN, Subramanian, VG & Berry, RA 2018, Learning from randomly arriving agents. in 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017. 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 196-197, 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017, Monticello, United States, 10/3/17. https://doi.org/10.1109/ALLERTON.2017.8262737

Learning from randomly arriving agents. / Le, Tho Ngoc; Subramanian, Vijay G.; Berry, Randall A.

55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 196-197 (55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017; Vol. 2018-January).

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

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Le TN, Subramanian VG, Berry RA. Learning from randomly arriving agents. In 55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 196-197. (55th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2017). https://doi.org/10.1109/ALLERTON.2017.8262737