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

Arun G. Chandrasekhar, Horacio Larreguy, Juan Pablo Xandri

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


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 languageEnglish
Pages (from-to)1-32
Number of pages32
Issue number1
StatePublished - 1 Jan 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020 The Econometric Society


  • Bayesian learning
  • DeGroot learning
  • Networks
  • social learning


Dive into the research topics of 'Testing Models of Social Learning on Networks: Evidence From Two Experiments'. Together they form a unique fingerprint.

Cite this