GLOBAL EMERGENT BEHAVIORS IN CLOUDS OF AGENTS

Bio-inspired Information Processing and Networks

Presented by: José M.F. Moura, Author(s): Soummya Kar, Princeton University, United States; José M.F. Moura, Carnegie Mellon University, United States

Networks of biological agents (for example, ants, bees, fish, birds) and complex man-made cyberphysical infrastructures (for example, the power grid, transportation networks) exhibit one thing in common -- the emergence of collective global phenomena from apparently random local interactions. This paper proposes a distributed graphical model of interacting agents (a stochastic network type model) and studies its appropriate asymptotics. We show that metastability may occur -- i.e., under certain conditions, the agents act in synchrony and may exhibit collectively possibly different stable equilibria -- these are the global emergent behaviors of the cloud of interacting agents. We characterize these global behaviors as emph{synchronous} fixed points determined from ordinary differential equations that arise as mean field limits of the adopted stochastic model.

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