Self-Organized Criticality of Belief Propagation in Large Heterogeneous Teams

  • Robin Glinton
  • Praveen Paruchuri
  • Paul Scerri
  • Katia Sycara
Part of the Springer Optimization and Its Applications book series (SOIA, volume 40)


Large, heterogeneous teams will often be faced with situations where there is a large volume of incoming, conflicting data about some important fact. Not every team member will have access to the same data and team members will be influenced most by the teammates with whom they communicate directly. In this paper, we use an abstract model to investigate the dynamics and emergent behaviors of a large team trying to decide whether some fact is true. Simulation results show that the belief dynamics of a large team have the properties of a Self-Organizing Critical system. A key property of such systems is that they regularly enter critical states, where one additional input can cause dramatic, system wide changes. In the belief sharing case, this criticality corresponds to a situation where one additional sensor input causes many agents to change their beliefs. This can include the entire team coming to a “wrong” conclusion despite the majority of the evidence suggesting the right conclusion. Self-organizing criticality is not dependent on carefully tuned parameters, hence the observed phenomena are likely to occur in the real world.


Belief Propagation Scale Free Network Sensor Reading Network Type Belief Change 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Robin Glinton
    • 1
  • Praveen Paruchuri
    • 1
  • Paul Scerri
    • 1
  • Katia Sycara
    • 1
  1. 1.Robotics InstituteCarnegie Mellon UniversityPittsburghUSA

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