title: Conformity biased transmission in social networks.
abstract: In this paper we explore how the structure of a population can differentially influence the spread of novel behaviors, depending on the learning strategy of each individual. We use a series of simulations to analyze how frequency dependent learning rules might affect how easily novel behaviors can spread through a population on four artificial social networks, and three real social networks. We measured the likelihood that a novel behavior could spread through the population, and the likelihood that there were multiple behavioral variants in the population, a measure of cultural diversity. Surprisingly, we find few differences between networks on either measure. However, we do find that where a behavior originated on a network can have a substantial impact on the likelihood that it spreads, and that this location effect depends on the learning strategy of an individual. These results suggest that for first-order analysis of how behaviors spread through a population, social network structure can be ignored, but that the social network structure may be useful for more fine-tuned analyses and predictions.
authors.affiliation: School of Biology, University of St Andrews, Harold Mitchel Building, St Andrews, Fife, KY16 9TH, UK. Electronic address: aczw@st-andrews.ac.uk.
authors.forename: Andrew
authors.initials: A
authors.lastname: Whalen
authors.affiliation: School of Biology, University of St Andrews, Harold Mitchel Building, St Andrews, Fife, KY16 9TH, UK.
authors.forename: Kevin
authors.initials: K
authors.lastname: Laland
mesh_terms: D005075:Biological Evolution; D006801:Humans; D012919:Social Behavior; D012944:Social Support
keywords: Conformity; Cultural transmission; Social learning; Social networks