The impact of observation and action errors on informational cascades

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

doi:10.1109/CDC.2014.7039678

The impact of observation and action errors on informational cascades

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

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Scopus citations

Abstract

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 language	English (US)
Title of host publication	53rd IEEE Conference on Decision and Control,CDC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1917-1922
Number of pages	6
Edition	February
ISBN (Electronic)	9781479977468
DOIs	https://doi.org/10.1109/CDC.2014.7039678
State	Published - 2014
Event	2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States Duration: Dec 15 2014 → Dec 17 2014

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
Number	February
Volume	2015-February
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Other

Other	2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014
Country/Territory	United States
City	Los Angeles
Period	12/15/14 → 12/17/14

ASJC Scopus subject areas

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

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10.1109/CDC.2014.7039678

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Le, T. N., Subramanian, V. G., & Berry, R. A. (2014). The impact of observation and action errors on informational cascades. In 53rd IEEE Conference on Decision and Control,CDC 2014 (February ed., pp. 1917-1922). [7039678] (Proceedings of the IEEE Conference on Decision and Control; Vol. 2015-February, No. February). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2014.7039678

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title = "The impact of observation and action errors on informational cascades",

abstract = "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.",

author = "Le, {Tho Ngoc} and Subramanian, {Vijay G.} and Berry, {Randall A.}",

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language = "English (US)",

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Le, TN, Subramanian, VG & Berry, RA 2014, The impact of observation and action errors on informational cascades. in 53rd IEEE Conference on Decision and Control,CDC 2014. February edn, 7039678, Proceedings of the IEEE Conference on Decision and Control, no. February, vol. 2015-February, Institute of Electrical and Electronics Engineers Inc., pp. 1917-1922, 2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014, Los Angeles, United States, 12/15/14. https://doi.org/10.1109/CDC.2014.7039678

The impact of observation and action errors on informational cascades. / Le, Tho Ngoc; Subramanian, Vijay G.; Berry, Randall A.
53rd IEEE Conference on Decision and Control,CDC 2014. February. ed. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1917-1922 7039678 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2015-February, No. February).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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.

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The impact of observation and action errors on informational cascades

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