Sequential Collective Search in Networks
63 Pages Posted: 19 Jun 2018 Last revised: 16 Dec 2018
Date Written: December 15, 2018
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. I characterize equilibria of the model by linking agents' search policies to the probability that they select the best action. If search costs are small enough, an improvement principle holds. This allows me to show that asymptotic learning obtains in sufficiently connected networks in which information paths are identifiable. When search costs are bounded away from zero, even a weaker notion of long-run learning fails, except in particular networks. Networks in which agents observe random numbers of immediate predecessors share many properties with the complete network, including the rate of convergence and the probability of wrong herds. Transparency of past histories has short-run implications for welfare and efficiency. Simply letting agents observe the shares of earlier choices reduces inefficiency and welfare losses.
Keywords: Social Networks; Rational Learning; Herding; Search; Bandit Problems; Sequential Decisions; Information Acquisition and Choice; Improvement and Large-Sample Principles.
JEL Classification: C72; D62; D81; D83; D85
Suggested Citation: Suggested Citation
