Collective Search in Networks

43 Pages Posted: 19 Jun 2018 Last revised: 10 Oct 2019

See all articles by Niccolò Lomys

Niccolò Lomys

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

Date Written: October 10, 2019


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

Suggested Citation

Lomys, Niccolò, Collective Search in Networks (October 10, 2019). Available at SSRN: or

Niccolò Lomys (Contact Author)

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

Place Anatole-France
Toulouse Cedex, F-31042


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