Collective Search in Networks

Abstract

I study social learning in networks with information acquisition and choice. Rational agents act in sequence, observe the choices of their connections, and acquire information via sequential search. Complete learning occurs if search costs are not bounded away from zero, the network is sufficiently connected, and information paths are identifiable. If search costs are bounded away from zero, even a weaker notion of long-run learning fails, except in special networks. When agents observe random numbers of immediate predecessors, the rate of convergence, the probability of wrong herds, and long-run efficiency properties are the same as in the complete network. Network transparency has short-run implications for welfare and efficiency and the density of indirect connections affects convergence rates. Simply letting agents observe the shares of earlier choices reduces inefficiency and welfare losses.

Keywords: Social Networks; Rational Learning; Improvement and Large-Sample Principles; Speed of Learning; Search; Bandit Problems; Information Acquisition and Choice

JEL Classification: C72; D62; D81; D83; D85