, Volume 16, Issue 2-3, pp 215-250

A cognitive hierarchy model of learning in networks

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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.

I thank Colin Camerer, Andrew Caplin, Xiaohong Chen, Guillaume Fréchette, Shachar Kariv, Donghoon Lee, and Andrew Schotter for comments and suggestions. I am especially grateful to Douglas Gale for his continued and invaluable discussion and advice. This paper has also benefitted from suggestions by the participants of seminars at National University of Singapore, New York University, University College London, University Western Ontario, Tinbergen Institute at Amsterdam, International ESA 2005 in Montreal, and World Congress Econometric Society 2005 in London.