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Flocking Behaviour: Agent-Based Simulation and Hierarchical Leadership

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Abstract

We have studied how leaders emerge in a group as a consequence of interactions among its members. We propose that leaders can emerge as a consequence of a self-organized process based on local rules of dyadic interactions among individuals. Flocks are an example of self-organized behaviour in a group and properties similar to those observed in flocks might also explain some of the dynamics and organization of human groups. We developed an agent-based model that generated flocks in a virtual world and implemented it in a multi-agent simulation computer program that computed indices at each time step of the simulation to quantify the degree to which a group moved in a coordinated way (index of flocking behaviour) and the degree to which specific individuals led the group (index of hierarchical leadership). We ran several series of simulations in order to test our model and determine how these indices behaved under specific agent and world conditions. We identified the agent, world property, and model parameters that made stable, compact flocks emerge, and explored possible environmental properties that predicted the probability of becoming a leader.

Suggested Citation

  • Vicenç Quera & Francesc S. Beltran & Ruth Dolado, 2010. "Flocking Behaviour: Agent-Based Simulation and Hierarchical Leadership," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(2), pages 1-8.
  • Handle: RePEc:jas:jasssj:2009-71-3
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    References listed on IDEAS

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    1. Kurt C. Foster & Stephen Q. Muth & John J. Potterat & Richard B. Rothenberg, 2001. "A Faster Katz Status Score Algorithm," Computational and Mathematical Organization Theory, Springer, vol. 7(4), pages 275-285, December.
    2. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
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    1. Davide Secchi & Raffaello Seri, 2017. "Controlling for false negatives in agent-based models: a review of power analysis in organizational research," Computational and Mathematical Organization Theory, Springer, vol. 23(1), pages 94-121, March.

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