Collective Search in Networks

51 Pages Posted: 19 Jun 2018 Last revised: 31 Aug 2020

See all articles by Niccolò Lomys

Niccolò Lomys

University of Toulouse 1 - Toulouse School of Economics (TSE)

Date Written: August 30, 2020

Abstract

I study social learning in networks with information acquisition and choice. Bayesian 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 and the network is sufficiently connected and has identifiable information paths. If search costs are bounded away from zero, complete learning is possible in many stochastic networks, including almost-complete networks, but even a weaker notion of long-run learning fails in many other 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. The density of indirect connections affects convergence rates. Network transparency has short-run implications for welfare and efficiency. Simply letting agents observe the shares of earlier choices reduces inefficiency and welfare losses.

Keywords: Networks; Bayesian Learning; Improvement and Large-Sample Principles; Speed and Efficiency of Social Learning; Search; Bandit Problems; Information Choice.

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

Suggested Citation

Lomys, Niccolò, Collective Search in Networks (August 30, 2020). Available at SSRN: https://ssrn.com/abstract=3197244 or http://dx.doi.org/10.2139/ssrn.3197244

Niccolò Lomys (Contact Author)

University of Toulouse 1 - Toulouse School of Economics (TSE) ( email )

Place Anatole-France
Toulouse Cedex, F-31042
France

HOME PAGE: http://niccololomys.com/

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
72
Abstract Views
617
rank
397,538
PlumX Metrics