INSTITUTO MILENIO IMPERFECCIONES DE MERCADO Y POLÍTICA PÚBLICAS

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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 reducedform

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.

 

Arun G. Chandrasekhar, Horacio Larreguy and Juan Pablo Xandri

Lugar:

Sala de Consejo, Beauchef 851, piso 4 - Departamento de Ingeniería Industrial, U. de Chile.

Expositor:

Juan Pablo Xandri

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