TY - GEN
T1 - The Hazards and Benefits of Condescension in Social Learning
AU - Arieli, Itai
AU - Babichenkoyako, Yakov
AU - Müller, Stephan
AU - Pourbabaee, Farzad
AU - Tamuz, Omer
N1 - Publisher Copyright: © 2023 Owner/Author(s).
PY - 2023/7/9
Y1 - 2023/7/9
N2 - In a misspecified social learning setting, agents are condescending if they perceive their peers as having private information that is of lower quality than it is in reality. Applying this to a standard sequential model, we show that outcomes improve when agents are mildly condescending. In contrast, too much condescension leads to worse outcomes, as does anti-condescension.
AB - In a misspecified social learning setting, agents are condescending if they perceive their peers as having private information that is of lower quality than it is in reality. Applying this to a standard sequential model, we show that outcomes improve when agents are mildly condescending. In contrast, too much condescension leads to worse outcomes, as does anti-condescension.
KW - misspecified learning
KW - social learning
KW - speed of learning
UR - http://www.scopus.com/inward/record.url?scp=85168161025&partnerID=8YFLogxK
U2 - 10.1145/3580507.3597752
DO - 10.1145/3580507.3597752
M3 - منشور من مؤتمر
T3 - EC 2023 - Proceedings of the 24th ACM Conference on Economics and Computation
SP - 119
BT - EC 2023 - Proceedings of the 24th ACM Conference on Economics and Computation
T2 - 24th ACM Conference on Economics and Computation, EC 2023
Y2 - 9 July 2023 through 12 July 2023
ER -