Rapid innovation diffusion in social networks

Social and technological innovations often spread through social networks as people respond to what their neighbors are doing. Previous research has identified specific network structures, such as local clustering, that promote rapid diffusion. Here we derive bounds that are independent of network s...

Full description

Bibliographic Details
Main Authors: Kreindler, Gabriel Emanuel (Contributor), Young, H. Peyton (Author)
Other Authors: Massachusetts Institute of Technology. Department of Economics (Contributor)
Format: Article
Language:English
Published: National Academy of Sciences (U.S.), 2015-02-05T17:12:19Z.
Subjects:
Online Access:Get fulltext
LEADER 01899 am a22002053u 4500
001 93787
042 |a dc 
100 1 0 |a Kreindler, Gabriel Emanuel  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Economics  |e contributor 
100 1 0 |a Kreindler, Gabriel Emanuel  |e contributor 
700 1 0 |a Young, H. Peyton  |e author 
245 0 0 |a Rapid innovation diffusion in social networks 
260 |b National Academy of Sciences (U.S.),   |c 2015-02-05T17:12:19Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/93787 
520 |a Social and technological innovations often spread through social networks as people respond to what their neighbors are doing. Previous research has identified specific network structures, such as local clustering, that promote rapid diffusion. Here we derive bounds that are independent of network structure and size, such that diffusion is fast whenever the payoff gain from the innovation is sufficiently high and the agents' responses are sufficiently noisy. We also provide a simple method for computing an upper bound on the expected time it takes for the innovation to become established in any finite network. For example, if agents choose log-linear responses to what their neighbors are doing, it takes on average less than 80 revision periods for the innovation to diffuse widely in any network, provided that the error rate is at least 5% and the payoff gain (relative to the status quo) is at least 150%. Qualitatively similar results hold for other smoothed best-response functions and populations that experience heterogeneous payoff shocks. 
520 |a United States. Office of Naval Research (Grant N00014-09-1-0751) 
520 |a United States. Air Force Office of Scientific Research (Grant FA9550-09-1-0538) 
546 |a en_US 
655 7 |a Article 
773 |t Proceedings of the National Academy of Sciences of the United States of America