Collective Search in Networks

Niccolò Lomys

CSEF Working Papers from Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy

Abstract: I study the dynamics of collective search in networks. Bayesian agents act in sequence, observe the choices of their connections, and privately acquire information about the qualities of different actions via sequential search. If search costs are not bounded away from zero, maximal learning occurs in sufficiently connected networks where individual neighborhood realizations weakly distort agents’ beliefs about the realized network. If search costs are bounded away from zero, maximal learning is possible in several stochastic networks, including almost-complete networks, but generally fails otherwise. When agents observe random numbers of immediate predecessors, the learning rate, 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.

Keywords: Networks; Bayesian Learning; Search; Speed and Efficiency of Social Learning. (search for similar items in EconPapers)
JEL-codes: C7 D6 D8 (search for similar items in EconPapers)
Date: 2023-10-18
New Economics Papers: this item is included in nep-gth, nep-mic, nep-net and nep-ure
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.csef.it/WP/wp688.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sef:csefwp:688

Access Statistics for this paper

More papers in CSEF Working Papers from Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy Contact information at EDIRC.
Bibliographic data for series maintained by Dr. Maria Carannante (info@csef.it).