Neural Computing and Applications

, Volume 21, Issue 2, pp 207–218 | Cite as

Analysis of emergent symmetry breaking in collective decision making

  • Heiko Hamann
  • Thomas Schmickl
  • Heinz Wörn
  • Karl Crailsheim
Swam Intelligence


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 environment.


Symmetry breaking Collective decision making Swarm intelligence Multi-agent system 



The authors thank the anonymous reviewers for their helpful comments as well as Sibylle Hahshold, Martina Szopek, Gerald Radspieler and Ronald Thenius for providing us with data of honeybee experiments and for building the honeybee temperature arena. This work is supported by: EU-IST FET project I-SWARM, no. 507006; EU-IST-FET project ‘SYMBRION’, no. 216342; EU-ICT project ‘REPLICATOR’, no. 216240. Austrian Science Fund (FWF) research grants: P15961-B06 and P19478-B16. German Research Foundation (DFG) within the Research Training Group GRK 1194 Self-organizing Sensor-Actuator Networks.


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Copyright information

© Springer-Verlag London Limited 2010

Authors and Affiliations

  • Heiko Hamann
    • 1
  • Thomas Schmickl
    • 1
  • Heinz Wörn
    • 2
  • Karl Crailsheim
    • 1
  1. 1.Artificial Life Lab of the Department of ZoologyKarl-Franzens University GrazGrazAustria
  2. 2.Universität Karslruhe (TH), Institute for Process Control and RoboticsKarlsruheGermany

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