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Author Chandrasekhar, Arun G.,
Title Testing models of social learning on networks : evidence from a lab experiment in the field / Arun G. Chandrasekhar, Horacio Larreguy, Juan Pablo Xandri.
Imprint Cambridge, Mass. : National Bureau of Economic Research, 2015.

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LOCATION CALL # STATUS MESSAGE
 JOHN CARROLL ELECTRONIC RESOURCE    ONLINE  
View online
Author Chandrasekhar, Arun G.,
Series NBER working paper series ; no. 21468
Working paper series (National Bureau of Economic Research) ; no. 21468.
Subject DeGroot, Morris H., 1931-1989.
Bayesian statistical decision theory -- Econometric models.
Social learning -- India -- Econometric models.
Social networks -- India -- Econometric models.
Alt Name Larreguy Arbesu, Horacio Alejandro,
Xandri, Juan Pablo,
National Bureau of Economic Research,
Description 1 online resource (58 pages) : illustrations.
Note Description based on online resource; title from http://www.nber.org/papers/21468 viewed September 4, 2015.
"August 2015"
Bibliography Note Includes bibliographical references (pages 31-33).
Summary Agents often use noisy signals from their neighbors to update their beliefs about a state of the world. The effectiveness of social learning relies on the details of how agents aggregate information from others. There are two prominent models of information aggregation in networks: (1) Bayesian learning, where agents use Bayes' rule to assess the state of the world and (2) DeGroot learning, where agents instead consider a weighted average of their neighbors' previous period opinions or actions. Agents who engage in DeGroot learning often double-count information and may not converge in the long run. We conduct a lab experiment in the field with 665 subjects across 19 villages in Karnataka, India, designed to structurally test which model best describes social learning. Seven subjects were placed into a network with common knowledge of the network structure. Subjects attempted to learn the underlying (binary) state of the world, having received independent identically distributed signals in the first period. Thereafter, in each period, subjects made guesses about the state of the world, and these guesses were transmitted to their neighbors at the beginning of the following round. We structurally estimate a model of Bayesian learning, relaxing common knowledge of Bayesian rationality by allowing agents to have incomplete information as to whether others are Bayesian or DeGroot. Our estimates show that, despite the flexibility in modeling learning in these networks, agents are robustly best described by DeGroot-learning models wherein they take a simple majority of previous guesses in their neighborhood.
OCLC # 919939740


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