TY - GEN
T1 - Social Learning with Beliefs in a Parallel Network
AU - Seo, Daewon
AU - Raman, Ravi Kiran
AU - Varshney, Lav R.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Consider a social learning problem in a parallel network, where N distributed agents make independent selfish binary decisions, and a central agent aggregates them together with a private signal to make a final decision. In particular, all agents have private beliefs for the true prior, based on which they perform binary hypothesis testing. We focus on the Bayes risk of the central agent, and counterintuitively find that a collection of agents with incorrect beliefs could outperform a set of agents with correct beliefs. We also consider many-agent asymptotics (i.e., N is large) when distributed agents all have identical beliefs, for which it is found that the central agent's decision is polarized and beliefs determine the limit value of the central agent's risk. Moreover, it is surprising that when all agents believe a certain prior-agnostic constant belief, it achieves globally optimal risk as N→∞.
AB - Consider a social learning problem in a parallel network, where N distributed agents make independent selfish binary decisions, and a central agent aggregates them together with a private signal to make a final decision. In particular, all agents have private beliefs for the true prior, based on which they perform binary hypothesis testing. We focus on the Bayes risk of the central agent, and counterintuitively find that a collection of agents with incorrect beliefs could outperform a set of agents with correct beliefs. We also consider many-agent asymptotics (i.e., N is large) when distributed agents all have identical beliefs, for which it is found that the central agent's decision is polarized and beliefs determine the limit value of the central agent's risk. Moreover, it is surprising that when all agents believe a certain prior-agnostic constant belief, it achieves globally optimal risk as N→∞.
UR - http://www.scopus.com/inward/record.url?scp=85090406026&partnerID=8YFLogxK
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U2 - 10.1109/ISIT44484.2020.9174359
DO - 10.1109/ISIT44484.2020.9174359
M3 - Conference contribution
AN - SCOPUS:85090406026
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 1265
EP - 1270
BT - 2020 IEEE International Symposium on Information Theory, ISIT 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Symposium on Information Theory, ISIT 2020
Y2 - 21 July 2020 through 26 July 2020
ER -