Testing Models of Social Learning on Networks: Evidence From Two Experiments

Arun G. Chandrasekhar; Horacio Larreguy; Juan Pablo Xandri

doi:10.3982/ECTA14407

Testing Models of Social Learning on Networks: Evidence From Two Experiments

Arun G. Chandrasekhar, Horacio Larreguy, Juan Pablo Xandri

Research output: Contribution to journal › Article › peer-review

40 Scopus citations

Abstract

We theoretically and empirically study an incomplete information model of social learning. Agents initially guess the binary state of the world after observing a private signal. In subsequent rounds, agents observe their network neighbors' previous guesses before guessing again. Agents are drawn from a mixture of learning types—Bayesian, who face incomplete information about others' types, and DeGroot, who average their neighbors' previous period guesses and follow the majority. We study (1) learning features of both types of agents in our incomplete information model; (2) what network structures lead to failures of asymptotic learning; (3) whether realistic networks exhibit such structures. We conducted lab experiments with 665 subjects in Indian villages and 350 students from ITAM in Mexico. We perform a reduced-form analysis and then structurally estimate the mixing parameter, finding the share of Bayesian agents to be 10% and 50% in the Indian-villager and Mexican-student samples, respectively.

Original language	English
Pages (from-to)	1-32
Number of pages	32
Journal	Econometrica
Volume	88
Issue number	1
DOIs	https://doi.org/10.3982/ECTA14407
State	Published - 1 Jan 2020
Externally published	Yes

Bibliographical note

Funding Information:
We are grateful to Daron Acemoglu, Abhijit Banerjee, Esther Duflo, Ben Golub, Matthew O. Jackson, Markus Mobius, Omer Tamuz, Adam Szeidl, and Chris Udry for extremely helpful discussions. Essential feedback was provided by Kalyan Chatterjee, Juan Dubra, Rema Hanna, Ben Olken, Evan Sadler, Rob Townsend, Xiao Yu Wang, Luis Zerme?o, and participants at numerous seminars and conferences. We also thank Devika Lakhote, Gowri Nagaraj, Adriana Paz, Mounu Prem, Alejandra Rogel, Diego Dominguez, Jos? Ram?n Enr?quez, Piotr Evdokimov, Andrei Gomberg, and Juan Pablo Micozzi for their assistance. We thank the Russell Sage Behavioral Economics Grant, the NSF GRFP (Chandrasekhar), and the Bank of Spain and Caja Madrid (Larreguy) for financial support.

Publisher Copyright:
© 2020 The Econometric Society

Keywords

Bayesian learning
DeGroot learning
Networks
social learning

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Cite this

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title = "Testing Models of Social Learning on Networks: Evidence From Two Experiments",

abstract = "We theoretically and empirically study an incomplete information model of social learning. Agents initially guess the binary state of the world after observing a private signal. In subsequent rounds, agents observe their network neighbors' previous guesses before guessing again. Agents are drawn from a mixture of learning types—Bayesian, who face incomplete information about others' types, and DeGroot, who average their neighbors' previous period guesses and follow the majority. We study (1) learning features of both types of agents in our incomplete information model; (2) what network structures lead to failures of asymptotic learning; (3) whether realistic networks exhibit such structures. We conducted lab experiments with 665 subjects in Indian villages and 350 students from ITAM in Mexico. We perform a reduced-form analysis and then structurally estimate the mixing parameter, finding the share of Bayesian agents to be 10% and 50% in the Indian-villager and Mexican-student samples, respectively.",

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author = "Chandrasekhar, {Arun G.} and Horacio Larreguy and Xandri, {Juan Pablo}",

note = "Funding Information: We are grateful to Daron Acemoglu, Abhijit Banerjee, Esther Duflo, Ben Golub, Matthew O. Jackson, Markus Mobius, Omer Tamuz, Adam Szeidl, and Chris Udry for extremely helpful discussions. Essential feedback was provided by Kalyan Chatterjee, Juan Dubra, Rema Hanna, Ben Olken, Evan Sadler, Rob Townsend, Xiao Yu Wang, Luis Zerme?o, and participants at numerous seminars and conferences. We also thank Devika Lakhote, Gowri Nagaraj, Adriana Paz, Mounu Prem, Alejandra Rogel, Diego Dominguez, Jos? Ram?n Enr?quez, Piotr Evdokimov, Andrei Gomberg, and Juan Pablo Micozzi for their assistance. We thank the Russell Sage Behavioral Economics Grant, the NSF GRFP (Chandrasekhar), and the Bank of Spain and Caja Madrid (Larreguy) for financial support. Publisher Copyright: {\textcopyright} 2020 The Econometric Society",

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N2 - We theoretically and empirically study an incomplete information model of social learning. Agents initially guess the binary state of the world after observing a private signal. In subsequent rounds, agents observe their network neighbors' previous guesses before guessing again. Agents are drawn from a mixture of learning types—Bayesian, who face incomplete information about others' types, and DeGroot, who average their neighbors' previous period guesses and follow the majority. We study (1) learning features of both types of agents in our incomplete information model; (2) what network structures lead to failures of asymptotic learning; (3) whether realistic networks exhibit such structures. We conducted lab experiments with 665 subjects in Indian villages and 350 students from ITAM in Mexico. We perform a reduced-form analysis and then structurally estimate the mixing parameter, finding the share of Bayesian agents to be 10% and 50% in the Indian-villager and Mexican-student samples, respectively.

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Testing Models of Social Learning on Networks: Evidence From Two Experiments

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Keywords

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