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Harcourt, J. L., Ang, T. Z., Sweetman, G., Johnstone, R. A., & Manica, A. (2009). Social feedback and the emergence of leaders and followers. Curr Biol, 19(3), 248–252.
Abstract: In many animal groups, certain individuals consistently appear at the forefront of coordinated movements [1-4]. How such leaders emerge is poorly understood [5, 6]. Here, we show that in pairs of sticklebacks, Gasterosteus aculeatus, leadership arises from individual differences in the way that fish respond to their partner's movements. Having first established that individuals differed in their propensity to leave cover in order to look for food, we randomly paired fish of varying boldness, and we used a Markov Chain model to infer the individual rules underlying their joint behavior. Both fish in a pair responded to each other's movements-each was more likely to leave cover if the other was already out and to return if the other had already returned. However, we found that bolder individuals displayed greater initiative and were less responsive to their partners, whereas shyer individuals displayed less initiative but followed their partners more faithfully; they also, as followers, elicited greater leadership tendencies in their bold partners. We conclude that leadership in this case is reinforced by positive social feedback.
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Pattison, P., & Wasserman, S. (1999). Logit models and logistic regressions for social networks: II. Multivariate relations. Br J Math Stat Psychol, 52 ( Pt 2), 169–193.
Abstract: The research described here builds on our previous work by generalizing the univariate models described there to models for multivariate relations. This family, labelled p*, generalizes the Markov random graphs of Frank and Strauss, which were further developed by them and others, building on Besag's ideas on estimation. These models were first used to model random variables embedded in lattices by Ising, and have been quite common in the study of spatial data. Here, they are applied to the statistical analysis of multigraphs, in general, and the analysis of multivariate social networks, in particular. In this paper, we show how to formulate models for multivariate social networks by considering a range of theoretical claims about social structure. We illustrate the models by developing structural models for several multivariate networks.
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