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: Contribution to journal › Conference article › peer-review

10 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)
Article number	7039678
Pages (from-to)	1917-1922
Number of pages	6
Journal	Proceedings of the IEEE Conference on Decision and Control
Volume	2015-February
Issue number	February
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

ASJC Scopus subject areas

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

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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.}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE. Copyright: Copyright 2016 Elsevier B.V., All rights reserved.; 2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 ; Conference date: 15-12-2014 Through 17-12-2014",

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doi = "10.1109/CDC.2014.7039678",

language = "English (US)",

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AU - Subramanian, Vijay G.

AU - Berry, Randall A.

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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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