Information Heterogeneity and the Speed of Learning in Social Networks
Coauthor(s): Ali Jadbabaie, Pooya Molavi.
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This paper examines how the structure of a social network and the quality of information
available to different agents determine the speed of social learning. To this end, we study a variant
of the seminal model of DeGroot (1974), according to which agents linearly combine their
personal experiences with the views of their neighbors. We show that the rate of learning has
a simple analytical characterization in terms of the relative entropy of agents' signal structures
and their eigenvector centralities. Our characterization establishes that the way information is
dispersed throughout the social network has non-trivial implications for the rate of learning. In
particular, we show that when the informativeness of different agents' signal structures are comparable
in the sense of Blackwell (1953), then a positive assortative matching of signal qualities
and eigenvector centralities maximizes the rate of learning. On the other hand, if information
structures are such that each individual possesses some information crucial for learning, then
the rate of learning is higher when agents with the best signals are located at the periphery of
the network. Finally, we show that the extent of asymmetry in the structure of the social network
plays a key role in the long-run dynamics of the beliefs.
Jadbabaie, Ali, Pooya Molavi, and Alireza Tahbaz-Salehi. "Information Heterogeneity and the Speed of Learning in Social Networks." Columbia University, May 2013.