Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 107, Issue 47, Pages 20196-20201Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1004098107
Keywords
Markov chain; convergence times; game dynamics
Categories
Funding
- National Science Foundation [CCF-0743978, DMS-0806211, CCF-0915145]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [0915145] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [743978] Funding Source: National Science Foundation
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Which network structures favor the rapid spread of new ideas, behaviors, or technologies? This question has been studied extensively using epidemic models. Here we consider a complementary point of view and consider scenarios where the individuals' behavior is the result of a strategic choice among competing alternatives. In particular, we study models that are based on the dynamics of coordination games. Classical results in game theory studying this model provide a simple condition for a new action or innovation to become widespread in the network. The present paper characterizes the rate of convergence as a function of the structure of the interaction network. The resulting predictions differ strongly from the ones provided by epidemic models. In particular, it appears that innovation spreads much more slowly on well-connected network structures dominated by long-range links than in low-dimensional ones dominated, for example, by geographic proximity.
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