The hazards and benefits of condescension in social learning

Itai Arieli (), Yakov Babichenko (), Stephan Müller (), Farzad Pourbabaee () and Omer Tamuz ()
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Itai Arieli: Data and Decision Sciences, Technion–Israel Institute of Technology
Yakov Babichenko: Data and Decision Sciences, Technion–Israel Institute of Technology
Stephan Müller: Department of Economics, University of Goettingen
Farzad Pourbabaee: Department of Economics, Caltech
Omer Tamuz: Department of Economics, Caltech

Theoretical Economics, 2025, vol. 20, issue 1

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.

Keywords: Social; learning (search for similar items in EconPapers)
JEL-codes: D83 (search for similar items in EconPapers)
Date: 2025-01-28
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