Review of Economic Design

, Volume 16, Issue 2, pp 215–250

A cognitive hierarchy model of learning in networks

Original Paper

DOI: 10.1007/s10058-012-0126-6

Cite this article as:
Choi, S. Rev Econ Design (2012) 16: 215. doi:10.1007/s10058-012-0126-6


This paper proposes a method for estimating a hierarchical model of bounded rationality in games of learning in networks. A cognitive hierarchy comprises a set of cognitive types whose behavior ranges from random to substantively rational. Specifically, each cognitive type in the model corresponds to the number of periods in which economic agents process new information. Using experimental data, we estimate type distributions in a variety of task environments and show how estimated distributions depend on the structural properties of the environments. The estimation results identify significant levels of behavioral heterogeneity in the experimental data and overall confirm comparative static conjectures on type distributions across task environments. Surprisingly, the model replicates the aggregate patterns of the behavior in the data quite well. Finally, we found that the dominant type in the data is closely related to Bayes-rational behavior.


Cognitive hierarchy Bounded rationality Social learning Social networks 

JEL Classification

C51 C92 D82 D83 

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Department of EconomicsUniversity College LondonLondonUK

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