Cognitively-Constrained Learning from Neighbors

45 Pages Posted: 4 Dec 2019

See all articles by Wei Li

Wei Li

Vancouver School of Economics, University of British Columbia

Xu Tan

University of Washington - Economics

Date Written: July 18, 2019

Abstract

Agents in a network want to learn the state of the world from their own signals and their neighbors’ reports. But they are cognitively-constrained: they have finite and heterogeneous cognitive abilities. We model cognitive ability as a measure of their sophistication when they reason on behalf of a chain of neighbors. Using a tractable learning rule, agents identify old information and extract new signals from their neighbors to the best of their cognitive abilities. We characterize a cutoff level of cognitive ability for each agent, which depends only on the network structure. Using this property, we show that agents need very moderate levels of cognitive abilities to learn correctly in some environments. But an agent mislearns if her cognitive ability falls short of her cutoff.

Keywords: cognitively-constrained learning rule, mislearning in networks, depth of reasoning

JEL Classification: D03, D83, D85

Suggested Citation

Li, Wei and Tan, Xu, Cognitively-Constrained Learning from Neighbors (July 18, 2019). Available at SSRN: https://ssrn.com/abstract=3489431 or http://dx.doi.org/10.2139/ssrn.3489431

Wei Li (Contact Author)

Vancouver School of Economics, University of British Columbia ( email )

6000 Iona Drive
Vancouver, BC V6T 1L4
Canada
604-822-2839 (Phone)

Xu Tan

University of Washington - Economics ( email )

Seattle, WA
United States

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