Analysis of emergent symmetry breaking in collective decision making
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 agentagent 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 multi-agent system swarm intelligence