Network-based Diffusion Analysis: A New Method for Detecting Social Learning

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Network-based Diffusion Analysis: A New Method for Detecting Social Learning

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Title: Network-based Diffusion Analysis: A New Method for Detecting Social Learning
Author: Mathias, Franz; Nunn, Charles

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Citation: Franz, Mathias and Charles L. Nunn. 2009. Network-based diffusion analysis:  A new method for detecting social learning. Proceedings of the Royal Society B 276(1663): 1829-1836.
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Abstract: Social learning has been documented in a wide diversity of animals. In free-living
animals, however, it has been difficult to discern whether animals learn socially by
observing other group members or asocially by acquiring a new behaviour independently. We addressed this challenge by developing network-based diffusion analysis (NBDA), which analyzes the spread of traits through animal groups and takes into account that social network structure directs social learning opportunities. NBDA fits agent-based models of social and asocial learning to the observed data using
maximum-likelihood estimation. The underlying learning mechanism can then be
identified using model selection based on the Akaike information criterion. We tested
our method with artificially created learning data that are based on a real-world co-feeding network of macaques. NBDA is better able to discriminate between social and asocial learning in comparison to diffusion curve analysis, the main method that was previously applied in this context. NBDA thus offers a new, more reliable statistical test of learning mechanisms. In addition, it can be used to address a wide range of questions related to social learning, such as identifying behavioural strategies used by animals when deciding whom to copy.
Published Version: http://dx.doi.org/10.1098/rspb.2008.1824
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:2787016
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