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 | |
| State | Published - Jan 1 2014 |
| Event | 2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States Duration: Dec 15 2014 → Dec 17 2014 |
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ASJC Scopus subject areas
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization
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The impact of observation and action errors on informational cascades. / Le, Tho Ngoc; Subramanian, Vijay G.; Berry, Randall A.
In: Proceedings of the IEEE Conference on Decision and Control, Vol. 2015-February, No. February, 7039678, 01.01.2014, p. 1917-1922.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - The impact of observation and action errors on informational cascades
AU - Le, Tho Ngoc
AU - Subramanian, Vijay G.
AU - Berry, Randall A
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=84988214420&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84988214420&partnerID=8YFLogxK
U2 - 10.1109/CDC.2014.7039678
DO - 10.1109/CDC.2014.7039678
M3 - Conference article
AN - SCOPUS:84988214420
VL - 2015-February
SP - 1917
EP - 1922
JO - Proceedings of the IEEE Conference on Decision and Control
JF - Proceedings of the IEEE Conference on Decision and Control
SN - 0191-2216
IS - February
M1 - 7039678
ER -