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Fast and Accurate Decisions as a Result of Scale-Free Network Properties in Two Primate Species

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Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

The influence of particular individuals on others opinions and behaviours has long been studied by social and political scientists, and it is often suggested that certain individuals can act as leaders because they are socially connected, and have more ‘influence’ over others. However, this idea is difficult to test in a real-world (human or non-human) setting. Here, we present a study that describes the collective movements of two primate species: Macaca tonkeana and Macaca mulatta faced with the decision of when to stop resting and start foraging. We show that individuals that are central to the group’s social network elicit stronger follower behaviour and are crucial to the achievement of consensus decisions. This ‘embedded’ leader-follower dynamic improves the efficiency of the decision-making process, enabling faster decision times. Our data additionally suggest that a behavioural rule-of-thumb ‘follow my close affiliate’ can result in the most central individual leading decisions by virtue of the scale-free properties of the network. This may allow groups to utilise the knowledge of elder, dominant, or natal individuals (who are often central in social networks) whilst simultaneously maintaining bonds with highly social individuals which may bring indirect fitness benefits itself.

Keywords

  • Speed-accuracy trade-off
  • Macaca
  • Eigenvector
  • Centrality
  • Optimality
  • Decision-making

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Fig. 71.1
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Acknowledgements

We are grateful to A. Jacobs for his help on statistics. A.J. King was supported by a NERC Fellowship.

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Correspondence to Cédric Sueur .

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Sueur, C., King, A.J., Pelé, M., Petit, O. (2013). Fast and Accurate Decisions as a Result of Scale-Free Network Properties in Two Primate Species. In: Gilbert, T., Kirkilionis, M., Nicolis, G. (eds) Proceedings of the European Conference on Complex Systems 2012. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-00395-5_71

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