Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published March 14, 2006 | Published
Report Open

Diffusion on Social Networks

Abstract

We analyze a model of diffusion on social networks. Agents are connected according to an undirected graph (the network) and choose one of two actions (e.g., either to adopt a new behavior or technology or not to adopt it). The return to each of the actions depends on how many neighbors an agent has, which actions the agent's neighbors choose, and some agent-specific cost and benefit parameters. At the outset, a small portion of the population is randomly selected to adopt the behavior. We analyze whether the behavior spreads to a larger portion of the population. We show that there is a threshold where "tipping" occurs: if a large enough initial group is selected then the behavior grows and spreads to a significant portion of the population, while otherwise the behavior collapses so that no one in the population chooses to adopt the behavior. We characterize the tipping threshold and the eventual portion that adopts if the threshold is surpassed. We also show how the threshold and adoption rate depend on the network structure. Applications of the techniques introduced in this paper include marketing, epidemiology, technological transfers, and information transmission, among others.

Additional Information

An earlier version of this work was presented in a lecture at the Public Economic Theory Meetings 2005 in Marseille. We thank the organizers: Nicolas Gravel, Alain Trannoy, and Myrna Wooders, for the invitation to present this work there, and Laurent Methevet for his helpful comments. We are also grateful for financial support from the Center for Advanced Studies in the Behavioral Sciences, the Lee Center for Advanced Networking, and the Guggenheim Foundation.

Attached Files

Published - sswp1251.pdf

Files

sswp1251.pdf
Files (564.8 kB)
Name Size Download all
md5:4c91a3d22639909a7957073fc2d5dea8
564.8 kB Preview Download

Additional details

Created:
August 19, 2023
Modified:
January 13, 2024