The Hazards and Benefits of Condescension in Social Learning

Itai Arieli, Yakov Babichenkoyako, Stephan Müller, Farzad Pourbabaee, Omer Tamuz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationEC 2023 - Proceedings of the 24th ACM Conference on Economics and Computation
Pages119
Number of pages1
ISBN (Electronic)9798400701047
DOIs
StatePublished - 9 Jul 2023
Event24th ACM Conference on Economics and Computation, EC 2023 - London, United Kingdom
Duration: 9 Jul 202312 Jul 2023

Publication series

NameEC 2023 - Proceedings of the 24th ACM Conference on Economics and Computation

Conference

Conference24th ACM Conference on Economics and Computation, EC 2023
Country/TerritoryUnited Kingdom
CityLondon
Period9/07/2312/07/23

Keywords

  • misspecified learning
  • social learning
  • speed of learning

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Economics and Econometrics
  • Computational Mathematics
  • Statistics and Probability

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