Analysis of Emergent Symmetry Breaking in Collective Decision Making

Heiko Hamann, Thomas Schmickl, Heinz Wörn, Karl Crailsheim
Neural Computing & Applications 21(2) (2012), 207-218


  We investigate a simulated multi-agent system (MAS) that
  collectively decides to aggregate at an area of high utility. The
  agents' control algorithm is based on random agent-agent encounters
  and is inspired by the aggregation behavior of honeybees. In this
  article, we define symmetry breaking, several symmetry breaking
  measures, and report the phenomenon of emergent symmetry breaking
  within our observed system. The ability of the MAS to successfully
  break the symmetry depends significantly on a local-neighborhood
  based threshold of the agents' control algorithm that determines at
  which number of neighbors the agents stop. This dependency is
  analyzed and two macroscopic features are determined that
  significantly influence the symmetry breaking behavior. In addition,
  we investigate the connection between the ability of the MAS to
  break symmetries and the ability to stay flexible in a dynamic

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