When payoffs from different actions are unknown, agents use their own past experience as well as the experience of their neighbors to guide their current decision making. This paper develops a general framework to study the relationship between the structure of information flows and the process of social learning. We show that in a connected society, local learning ensures that all agents obtain the same utility, in the long run. We develop conditions under which this utility is the maximal attainable, i.e. optimal actions are adopted. This analysis identifies a structural property of information structures -- local independence -- which greatly facilitates social learning. Our analysis also suggests that there exists a negative relationship between the degree of social integration and the likelihood of diversity. Simulations of the model generate spatial and temporal patterns of adoption that are consistent with empirical work.

Additional Metadata
Keywords Royal Family, conformism, connected societies, diffusion, diversity, locally independent agents, social integration
JEL Search; Learning; Information and Knowledge (jel D83), Information and Product Quality; Standardization and Compatibility (jel L15), Technological Change; Research and Development (R&D): General (jel O30), R&D; Agricultural Technology; Agricultural Extension Services (jel Q16), General Regional Economics: General (jel R10)
Persistent URL hdl.handle.net/1765/1362
Series Econometric Institute Research Papers
Citation
Bala, V, & Goyal, S. (1995). Learning from Neighbors (No. EI 9549-/A). Econometric Institute Research Papers. Retrieved from http://hdl.handle.net/1765/1362