Literature DB >> 25024191

Rapid innovation diffusion in social networks.

Gabriel E Kreindler1, H Peyton Young2.   

Abstract

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.

Entities:  

Keywords:  convergence time; coordination game; local interaction model; logit; noisy best response

Mesh:

Year:  2014        PMID: 25024191      PMCID: PMC4113916          DOI: 10.1073/pnas.1400842111

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  5 in total

1.  Emergence of scaling in random networks

Authors: 
Journal:  Science       Date:  1999-10-15       Impact factor: 47.728

2.  The web of human sexual contacts.

Authors:  F Liljeros; C R Edling; L A Amaral; H E Stanley; Y Aberg
Journal:  Nature       Date:  2001-06-21       Impact factor: 49.962

3.  The dynamics of social innovation.

Authors:  H Peyton Young
Journal:  Proc Natl Acad Sci U S A       Date:  2011-12-22       Impact factor: 11.205

4.  The spread of innovations in social networks.

Authors:  Andrea Montanari; Amin Saberi
Journal:  Proc Natl Acad Sci U S A       Date:  2010-11-12       Impact factor: 11.205

5.  A simple model of global cascades on random networks.

Authors:  Duncan J Watts
Journal:  Proc Natl Acad Sci U S A       Date:  2002-04-30       Impact factor: 11.205

  5 in total
  9 in total

1.  The spontaneous emergence of conventions: an experimental study of cultural evolution.

Authors:  Damon Centola; Andrea Baronchelli
Journal:  Proc Natl Acad Sci U S A       Date:  2015-02-02       Impact factor: 11.205

2.  Networks of conforming or nonconforming individuals tend to reach satisfactory decisions.

Authors:  Pouria Ramazi; James Riehl; Ming Cao
Journal:  Proc Natl Acad Sci U S A       Date:  2016-10-31       Impact factor: 11.205

3.  In the light of evolution VIII: Darwinian thinking in the social sciences. Introduction.

Authors:  Brian Skyrms; John C Avise; Francisco J Ayala
Journal:  Proc Natl Acad Sci U S A       Date:  2014-07-22       Impact factor: 11.205

4.  Data-Driven Diffusion Of Innovations: Successes And Challenges In 3 Large-Scale Innovative Delivery Models.

Authors:  David A Dorr; Deborah J Cohen; Julia Adler-Milstein
Journal:  Health Aff (Millwood)       Date:  2018-02       Impact factor: 6.301

5.  The spread of technological innovations: effects of psychology, culture and policy interventions.

Authors:  Denis Tverskoi; Sudarsanam Babu; Sergey Gavrilets
Journal:  R Soc Open Sci       Date:  2022-06-22       Impact factor: 3.653

6.  Emergence of Shared Intentionality Is Coupled to the Advance of Cumulative Culture.

Authors:  Simon D Angus; Jonathan Newton
Journal:  PLoS Comput Biol       Date:  2015-10-30       Impact factor: 4.475

7.  Climatic shocks associate with innovation in science and technology.

Authors:  Carsten K W De Dreu; Mathijs A van Dijk
Journal:  PLoS One       Date:  2018-01-24       Impact factor: 3.240

8.  Influence of trust in the spreading of information.

Authors:  Hongrun Wu; Alex Arenas; Sergio Gómez
Journal:  Phys Rev E       Date:  2017-01-03       Impact factor: 2.529

9.  The scale-invariant, temporal profile of neuronal avalanches in relation to cortical γ-oscillations.

Authors:  Stephanie R Miller; Shan Yu; Dietmar Plenz
Journal:  Sci Rep       Date:  2019-11-11       Impact factor: 4.379

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.