Analysis of Swarm Behaviors Based on an Inversion of the Fluctuation Theorem

Heiko Hamann, Thomas Schmickl, Karl Crailsheim
Artificial Life (2013)


  A grand challenge in the field of artificial life is to find a
  general theory of emergent self-organizing systems. In swarm systems
  most of the observed complexity is based on motion of simple
  entities. Similarly statistical mechanics focuses on collective
  properties induced by motion of many interacting particles. In this
  paper we apply methods from statistical mechanics to swarm
  systems. We try to explain the emergent behavior of a simulated
  swarm by applying methods based on the fluctuation
  theorem. Empirical results indicate that swarms are able to produce
  negative entropy within an isolated sub-system due to `frozen
  accidents'. Individuals of a swarm are able to locally detect
  fluctuations of the global entropy measure and store them, if they
  are negative entropy productions. By accumulating these stored
  fluctuations over time the swarm as a whole is producing negative
  entropy and the system ends up in an ordered state. We claim that
  this indicates the existence of an inverted fluctuation theorem for
  emergent self-organizing dissipative systems. This approach bears
  the potential of general applicability.