Testing models of social learning on networks: evidence from two experiments
16 October, 2019 13:00 hrs
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
Location:
Sala de Consejo, Beauchef 851, piso 4 - Departamento de Ingeniería Industrial, U. de Chile.
Speaker:
Juan Pablo Xandri
MIPP Chile 2024