Testing Models of Social Learning on Networks: Evidence from a Lab Experiment in the Field

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. Types are drawn from a mixture of learning models—Bayesian, where agents face incomplete information about others’ types, and DeGroot, where agents follow the majority of their neighbors’ previous period guesses. We study (1) learning features of both types 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 conduct a reduced form analysis and then structurally estimate the mixing parameter, finding the share of Bayesian agents to be 10% and 50% in the village and student samples, respectively.